Dataset processing (pyrad.proc)¶
Initiate the dataset processing.
Auxiliary functions¶
get_process_func(dataset_type, dsname) |
Maps the dataset type into its processing function and data set format associated. |
process_raw(procstatus, dscfg[, radar_list]) |
Dummy function that returns the initial input data set |
process_save_radar(procstatus, dscfg[, …]) |
Dummy function that allows to save the entire radar object |
process_fixed_rng(procstatus, dscfg[, …]) |
Obtains radar data at a fixed range |
process_fixed_rng_span(procstatus, dscfg[, …]) |
For each azimuth-elevation gets the data within a fixed range span and computes a user-defined statistic: mean, min, max, mode, median |
process_roi(procstatus, dscfg[, radar_list]) |
Obtains the radar data at a region of interest defined by a TRT file or by the user. |
process_azimuthal_average(procstatus, dscfg) |
Averages radar data in azimuth obtaining and RHI as a result |
process_radar_resampling(procstatus, dscfg) |
Resamples the radar data to mimic another radar with different geometry and antenna pattern |
Gridded data functions¶
process_raw_grid(procstatus, dscfg[, radar_list]) |
Dummy function that returns the initial input data set |
process_grid(procstatus, dscfg[, radar_list]) |
Puts the radar data in a regular grid |
process_grid_point(procstatus, dscfg[, …]) |
Obtains the grid data at a point location. |
process_grid_time_stats(procstatus, dscfg[, …]) |
computes the temporal statistics of a field |
process_grid_time_stats2(procstatus, dscfg) |
computes temporal statistics of a field |
process_grid_texture(procstatus, dscfg[, …]) |
Computes the 2D texture of a gridded field |
process_grid_fields_diff(procstatus, dscfg) |
Computes grid field differences |
process_grid_mask(procstatus, dscfg[, …]) |
Mask data. |
process_normalize_luminosity(procstatus, dscfg) |
Normalize the data by the sinus of the sun elevation. |
process_pixel_filter(procstatus, dscfg[, …]) |
Masks all pixels that are not of the class specified in keyword pixel_type |
Spectral data functions¶
process_raw_spectra(procstatus, dscfg[, …]) |
Dummy function that returns the initial input data set |
process_spectra_point(procstatus, dscfg[, …]) |
Obtains the spectra or IQ data at a point location. |
process_filter_0Doppler(procstatus, dscfg[, …]) |
Function to filter the 0-Doppler line bin and neighbours of the Doppler spectra |
process_filter_spectra_noise(procstatus, dscfg) |
Filter the noise of the Doppler spectra by clipping any data below the noise level plus a margin |
process_filter_srhohv(procstatus, dscfg[, …]) |
Filter Doppler spectra as a function of spectral RhoHV |
process_spectra_ang_avg(procstatus, dscfg[, …]) |
Function to average the spectra over the rays. |
process_spectral_power(procstatus, dscfg[, …]) |
Computes the spectral power |
process_spectral_noise(procstatus, dscfg[, …]) |
Computes the spectral noise |
process_spectral_phase(procstatus, dscfg[, …]) |
Computes the spectral phase |
process_spectral_reflectivity(procstatus, dscfg) |
Computes spectral reflectivity |
process_spectral_differential_reflectivity(…) |
Computes spectral differential reflectivity |
process_spectral_differential_phase(…[, …]) |
Computes the spectral differential phase |
process_spectral_rhohv(procstatus, dscfg[, …]) |
Computes the spectral RhoHV |
process_pol_variables(procstatus, dscfg[, …]) |
Computes the polarimetric variables from the complex spectra |
process_noise_power(procstatus, dscfg[, …]) |
Computes the noise power from the spectra |
process_reflectivity(procstatus, dscfg[, …]) |
Computes reflectivity from the spectral reflectivity |
process_differential_reflectivity(…[, …]) |
Computes differential reflectivity from the horizontal and vertical spectral reflectivity |
process_differential_phase(procstatus, dscfg) |
Computes the differential phase from the spectral differential phase and the spectral reflectivity |
process_rhohv(procstatus, dscfg[, radar_list]) |
Computes RhoHV from the complex spectras |
process_Doppler_velocity(procstatus, dscfg) |
Compute the Doppler velocity from the spectral reflectivity |
process_Doppler_width(procstatus, dscfg[, …]) |
Compute the Doppler spectrum width from the spectral reflectivity |
process_ifft(procstatus, dscfg[, radar_list]) |
Compute the Doppler spectrum width from the spectral reflectivity |
IQ data functions¶
process_raw_iq(procstatus, dscfg[, radar_list]) |
Dummy function that returns the initial input data set |
process_pol_variables_iq(procstatus, dscfg) |
Computes the polarimetric variables from the IQ data |
process_reflectivity_iq(procstatus, dscfg[, …]) |
Computes reflectivity from the IQ data |
process_st1_iq(procstatus, dscfg[, radar_list]) |
Computes the statistical test one lag fluctuation from the horizontal or vertical IQ data |
process_st2_iq(procstatus, dscfg[, radar_list]) |
Computes the statistical test two lag fluctuation from the horizontal or vertical IQ data |
process_wbn_iq(procstatus, dscfg[, radar_list]) |
Computes the wide band noise from the horizontal or vertical IQ data |
process_differential_reflectivity_iq(…[, …]) |
Computes differential reflectivity from the horizontal and vertical IQ data |
process_mean_phase_iq(procstatus, dscfg[, …]) |
Computes the mean phase from the horizontal or vertical IQ data |
process_differential_phase_iq(procstatus, dscfg) |
Computes the differential phase from the horizontal and vertical IQ data |
process_rhohv_iq(procstatus, dscfg[, radar_list]) |
Computes RhoHV from the horizontal and vertical IQ data |
process_Doppler_velocity_iq(procstatus, dscfg) |
Compute the Doppler velocity from the spectral reflectivity |
process_Doppler_width_iq(procstatus, dscfg) |
Compute the Doppler spectrum width from the spectral reflectivity |
process_fft(procstatus, dscfg[, radar_list]) |
Compute the Doppler spectra form the IQ data with a Fourier transform |
Echo classification and filtering¶
process_echo_id(procstatus, dscfg[, radar_list]) |
identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Precipitation |
process_birds_id(procstatus, dscfg[, radar_list]) |
identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Birds |
process_clt_to_echo_id(procstatus, dscfg[, …]) |
Converts clutter exit code from rad4alp into pyrad echo ID |
process_echo_filter(procstatus, dscfg[, …]) |
Masks all echo types that are not of the class specified in keyword echo_type |
process_cdf(procstatus, dscfg[, radar_list]) |
Collects the fields necessary to compute the Cumulative Distribution Function |
process_filter_snr(procstatus, dscfg[, …]) |
filters out low SNR echoes |
process_filter_visibility(procstatus, dscfg) |
filters out rays gates with low visibility and corrects the reflectivity |
process_outlier_filter(procstatus, dscfg[, …]) |
filters out gates which are outliers respect to the surrounding |
process_hydroclass(procstatus, dscfg[, …]) |
Classifies precipitation echoes |
process_melting_layer(procstatus, dscfg[, …]) |
Detects the melting layer |
process_filter_vel_diff(procstatus, dscfg[, …]) |
filters out range gates that could not be used for Doppler velocity estimation |
process_zdr_column(procstatus, dscfg[, …]) |
Detects ZDR columns |
Phase processing and attenuation correction¶
process_correct_phidp0(procstatus, dscfg[, …]) |
corrects phidp of the system phase |
process_smooth_phidp_single_window(…[, …]) |
corrects phidp of the system phase and smoothes it using one window |
process_smooth_phidp_double_window(…[, …]) |
corrects phidp of the system phase and smoothes it using one window |
process_kdp_leastsquare_single_window(…[, …]) |
Computes specific differential phase using a piecewise least square method |
process_kdp_leastsquare_double_window(…[, …]) |
Computes specific differential phase using a piecewise least square method |
process_phidp_kdp_Vulpiani(procstatus, dscfg) |
Computes specific differential phase and differential phase using the method developed by Vulpiani et al. |
process_phidp_kdp_Kalman(procstatus, dscfg) |
Computes specific differential phase and differential phase using the Kalman filter as proposed by Schneebeli et al. |
process_phidp_kdp_Maesaka(procstatus, dscfg) |
Estimates PhiDP and KDP using the method by Maesaka. |
process_phidp_kdp_lp(procstatus, dscfg[, …]) |
Estimates PhiDP and KDP using a linear programming algorithm. |
process_attenuation(procstatus, dscfg[, …]) |
Computes specific attenuation and specific differential attenuation using the Z-Phi method and corrects reflectivity and differential reflectivity |
Monitoring, calibration and noise correction¶
process_correct_bias(procstatus, dscfg[, …]) |
Corrects a bias on the data |
process_correct_noise_rhohv(procstatus, dscfg) |
identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Precipitation |
process_rhohv_rain(procstatus, dscfg[, …]) |
Keeps only suitable data to evaluate the 80 percentile of RhoHV in rain |
process_zdr_precip(procstatus, dscfg[, …]) |
Keeps only suitable data to evaluate the differential reflectivity in moderate rain or precipitation (for vertical scans) |
process_zdr_snow(procstatus, dscfg[, radar_list]) |
Keeps only suitable data to evaluate the differential reflectivity in snow |
process_estimate_phidp0(procstatus, dscfg[, …]) |
estimates the system differential phase offset at each ray |
process_sun_hits(procstatus, dscfg[, radar_list]) |
monitoring of the radar using sun hits |
process_selfconsistency_kdp_phidp(…[, …]) |
Computes specific differential phase and differential phase in rain using the selfconsistency between Zdr, Zh and KDP |
process_selfconsistency_bias(procstatus, dscfg) |
Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley |
process_selfconsistency_bias2(procstatus, dscfg) |
Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley |
process_time_avg_std(procstatus, dscfg[, …]) |
computes the average and standard deviation of data. |
process_occurrence(procstatus, dscfg[, …]) |
computes the frequency of occurrence of data. |
process_occurrence_period(procstatus, dscfg) |
computes the frequency of occurrence over a long period of time by adding together shorter periods |
process_monitoring(procstatus, dscfg[, …]) |
computes monitoring statistics |
process_gc_monitoring(procstatus, dscfg[, …]) |
computes ground clutter monitoring statistics |
process_time_avg(procstatus, dscfg[, radar_list]) |
computes the temporal mean of a field |
process_weighted_time_avg(procstatus, dscfg) |
computes the temporal mean of a field weighted by the reflectivity |
process_time_avg_flag(procstatus, dscfg[, …]) |
computes a flag field describing the conditions of the data used while averaging |
process_time_stats(procstatus, dscfg[, …]) |
computes the temporal statistics of a field |
process_time_stats2(procstatus, dscfg[, …]) |
computes the temporal mean of a field |
process_colocated_gates(procstatus, dscfg[, …]) |
Find colocated gates within two radars |
process_intercomp(procstatus, dscfg[, …]) |
intercomparison between two radars |
process_intercomp_time_avg(procstatus, dscfg) |
intercomparison between the average reflectivity of two radars |
process_fields_diff(procstatus, dscfg[, …]) |
Computes the field difference between RADAR001 and radar002, i.e. |
process_intercomp_fields(procstatus, dscfg) |
intercomparison between two radars |
Retrievals¶
process_ccor(procstatus, dscfg[, radar_list]) |
Computes the Clutter Correction Ratio, i.e. |
process_signal_power(procstatus, dscfg[, …]) |
Computes the signal power in dBm |
process_rcs(procstatus, dscfg[, radar_list]) |
Computes the radar cross-section (assuming a point target) from radar reflectivity. |
process_rcs_pr(procstatus, dscfg[, radar_list]) |
Computes the radar cross-section (assuming a point target) from radar reflectivity by first computing the received power and then the RCS from it. |
process_radial_noise_hs(procstatus, dscfg[, …]) |
Computes the radial noise from the signal power using the Hildebrand and Sekhon 1974 method |
process_radial_noise_ivic(procstatus, dscfg) |
Computes the radial noise from the signal power using the Ivic 2013 method |
process_snr(procstatus, dscfg[, radar_list]) |
Computes SNR |
process_l(procstatus, dscfg[, radar_list]) |
Computes L parameter |
process_cdr(procstatus, dscfg[, radar_list]) |
Computes Circular Depolarization Ratio |
process_rainrate(procstatus, dscfg[, radar_list]) |
Estimates rainfall rate from polarimetric moments |
process_rainfall_accumulation(procstatus, dscfg) |
Computes rainfall accumulation fields |
process_vol_refl(procstatus, dscfg[, radar_list]) |
Computes the volumetric reflectivity in 10log10(cm^2 km^-3) |
process_bird_density(procstatus, dscfg[, …]) |
Computes the bird density from the volumetric reflectivity |
Doppler processing¶
process_turbulence(procstatus, dscfg[, …]) |
Computes turbulence from the Doppler spectrum width and reflectivity using the PyTDA package |
process_dealias_fourdd(procstatus, dscfg[, …]) |
Dealiases the Doppler velocity field using the 4DD technique from Curtis and Houze, 2001 |
process_dealias_region_based(procstatus, dscfg) |
Dealiases the Doppler velocity field using a region based algorithm |
process_dealias_unwrap_phase(procstatus, dscfg) |
Dealiases the Doppler velocity field using multi-dimensional phase unwrapping |
process_radial_velocity(procstatus, dscfg[, …]) |
Estimates the radial velocity respect to the radar from the wind velocity |
process_wind_vel(procstatus, dscfg[, radar_list]) |
Estimates the horizontal or vertical component of the wind from the radial velocity |
process_windshear(procstatus, dscfg[, …]) |
Estimates the wind shear from the wind velocity |
process_vad(procstatus, dscfg[, radar_list]) |
Estimates vertical wind profile using the VAD (velocity Azimuth Display) technique |
Time series functions¶
process_point_measurement(procstatus, dscfg) |
Obtains the radar data at a point location. |
process_qvp(procstatus, dscfg[, radar_list]) |
Computes quasi vertical profiles, by averaging over height levels PPI data. |
process_rqvp(procstatus, dscfg[, radar_list]) |
Computes range defined quasi vertical profiles, by averaging over height levels PPI data. |
process_svp(procstatus, dscfg[, radar_list]) |
Computes slanted vertical profiles, by averaging over height levels PPI data. |
process_evp(procstatus, dscfg[, radar_list]) |
Computes enhanced vertical profiles, by averaging over height levels PPI data. |
process_time_height(procstatus, dscfg[, …]) |
Produces time height radar objects at a point of interest defined by latitude and longitude. |
process_ts_along_coord(procstatus, dscfg[, …]) |
Produces time series along a particular antenna coordinate |
Trajectory functions¶
process_trajectory(procstatus, dscfg[, …]) |
Return trajectory |
process_traj_atplane(procstatus, dscfg[, …]) |
Return time series according to trajectory |
process_traj_antenna_pattern(procstatus, dscfg) |
Process a new array of data volumes considering a plane trajectory. |
process_traj_lightning(procstatus, dscfg[, …]) |
Return time series according to lightning trajectory |
process_traj_trt(procstatus, dscfg[, …]) |
Processes data according to TRT trajectory |
process_traj_trt_contour(procstatus, dscfg) |
Gets the TRT cell contour corresponding to each radar volume |
COSMO data¶
process_cosmo(procstatus, dscfg[, radar_list]) |
Gets COSMO data and put it in radar coordinates |
process_cosmo_lookup_table(procstatus, dscfg) |
Gets COSMO data and put it in radar coordinates using look up tables computed or loaded when initializing |
process_cosmo_coord(procstatus, dscfg[, …]) |
Gets the COSMO indices corresponding to each cosmo coordinates |
process_hzt(procstatus, dscfg[, radar_list]) |
Gets iso0 degree data in HZT format and put it in radar coordinates |
process_hzt_lookup_table(procstatus, dscfg) |
Gets HZT data and put it in radar coordinates using look up tables computed or loaded when initializing |
process_hzt_coord(procstatus, dscfg[, …]) |
Gets the HZT indices corresponding to each HZT coordinates |
process_cosmo_to_radar(procstatus, dscfg[, …]) |
Gets COSMO data and put it in radar coordinates using look up tables |
DEM data¶
process_dem(procstatus, dscfg[, radar_list]) |
Gets DEM data and put it in radar coordinates |
process_visibility(procstatus, dscfg[, …]) |
Gets the visibility in percentage from the minimum visible elevation. |
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pyrad.proc.get_process_func(dataset_type, dsname)[source]¶ Maps the dataset type into its processing function and data set format associated.
Parameters: - dataset_type : str
The following is a list of data set types ordered by type of output dataset with the function they call. For details of what they do check the function documentation:
- ‘VOL’ format output:
‘ATTENUATION’: process_attenuation ‘AZI_AVG’: process_azimuthal_average ‘BIAS_CORRECTION’: process_correct_bias ‘BIRDS_ID’: process_birds_id ‘BIRD_DENSITY’: process_bird_density ‘CCOR’: process_ccor ‘CDF’: process_cdf ‘CDR’: process_cdr ‘CLT_TO_SAN’: process_clt_to_echo_id ‘COSMO’: process_cosmo ‘COSMO_LOOKUP’: process_cosmo_lookup_table ‘DEM’: process_dem ‘DEALIAS_FOURDD’: process_dealias_fourdd ‘DEALIAS_REGION’: process_dealias_region_based ‘DEALIAS_UNWRAP’: process_dealias_unwrap_phase ‘DOPPLER_VELOCITY’: process_Doppler_velocity ‘DOPPLER_VELOCITY_IQ’: process_Doppler_velocity_iq ‘DOPPLER_WIDTH’: process_Doppler_width ‘DOPPLER_WIDTH_IQ’: process_Doppler_width_iq ‘ECHO_FILTER’: process_echo_filter ‘FIELDS_DIFF’: process_fields_diff ‘FIXED_RNG’: process_fixed_rng ‘FIXED_RNG_SPAN’: process_fixed_rng_span ‘HYDROCLASS’: process_hydroclass ‘HZT’: process_hzt ‘HZT_LOOKUP’: process_hzt_lookup_table ‘KDP_LEASTSQUARE_1W’: process_kdp_leastsquare_single_window ‘KDP_LEASTSQUARE_2W’: process_kdp_leastsquare_double_window ‘L’: process_l ‘MEAN_PHASE_IQ’: process_mean_phase_iq ‘NCVOL’: process_save_radar ‘NOISE_POWER’: process_noise_power ‘OUTLIER_FILTER’: process_outlier_filter ‘PhiDP’: process_differential_phase ‘PHIDP0_CORRECTION’: process_correct_phidp0 ‘PHIDP0_ESTIMATE’: process_estimate_phidp0 ‘PhiDP_IQ’: process_differential_phase_iq ‘PHIDP_KDP_KALMAN’: process_phidp_kdp_Kalman ‘PHIDP_KDP_LP’: process_phidp_kdp_lp ‘PHIDP_KDP_VULPIANI’: process_phidp_kdp_Vulpiani ‘PHIDP_SMOOTH_1W’: process_smooth_phidp_single_window ‘PHIDP_SMOOTH_2W’: process_smooth_phidp_double_window ‘POL_VARIABLES’: process_pol_variables ‘POL_VARIABLES_IQ’: process_pol_variables_iq ‘PWR’: process_signal_power ‘RADAR_RESAMPLING’: process_radar_resampling ‘RADIAL_NOISE_HS’: process_radial_noise_hs ‘RADIAL_NOISE_IVIC’: process_radial_noise_ivic ‘RADIAL_VELOCITY’: process_radial_velocity ‘RAINRATE’: process_rainrate ‘RAW’: process_raw ‘REFLECTIVITY’: process_reflectivity ‘REFLECTIVITY_IQ’: process_reflectivity_iq ‘RCS’: process_rcs ‘RCS_PR’: process_rcs_pr ‘RhoHV’: process_rhohv ‘RhoHV_IQ’: process_rhohv_iq ‘RHOHV_CORRECTION’: process_correct_noise_rhohv ‘RHOHV_RAIN’: process_rhohv_rain ‘ROI’: process_roi ‘SAN’: process_echo_id ‘SELFCONSISTENCY_BIAS’: process_selfconsistency_bias ‘SELFCONSISTENCY_BIAS2’: process_selfconsistency_bias2 ‘SELFCONSISTENCY_KDP_PHIDP’: process_selfconsistency_kdp_phidp ‘SNR’: process_snr ‘SNR_FILTER’: process_filter_snr ‘ST1_IQ’: process_st1_iq ‘ST2_IQ’: process_st2_iq ‘TRAJ_TRT’ : process_traj_trt ‘TRAJ_TRT_CONTOUR’ : process_traj_trt_contour ‘TURBULENCE’: process_turbulence ‘VAD’: process_vad ‘VEL_FILTER’: process_filter_vel_diff ‘VIS’: process_visibility ‘VIS_FILTER’: process_filter_visibility ‘VOL_REFL’: process_vol_refl ‘WBN’: process_wbn_iq ‘WIND_VEL’: process_wind_vel ‘WINDSHEAR’: process_windshear ‘ZDR’: process_differential_reflectivity ‘ZDR_IQ’: process_differential_reflectivity_iq ‘ZDR_PREC’: process_zdr_precip ‘ZDR_SNOW’: process_zdr_snow
- ‘SPECTRA’ format output:
‘FFT’: process_fft ‘FILTER_0DOPPLER’: process_filter_0Doppler ‘FILTER_SPECTRA_NOISE’: process_filter_spectra_noise ‘IFFT’: process_ifft ‘RAW_IQ’: process_raw_iq ‘RAW_SPECTRA’: process_raw_spectra ‘SPECTRA_ANGULAR_AVERAGE’: process_spectra_ang_avg ‘SPECTRA_POINT’: process_spectra_point ‘SPECTRAL_NOISE’: process_spectral_noise ‘SPECTRAL_PHASE’: process_spectral_phase ‘SPECTRAL_POWER’: process_spectral_power ‘SPECTRAL_REFLECTIVITY’: process_spectral_reflectivity ‘sPhiDP’: process_spectral_differential_phase ‘sRhoHV’: process_spectral_RhoHV ‘SRHOHV_FILTER’: process_filter_srhohv ‘sZDR’: process_spectral_differential_reflectivity
- ‘COLOCATED_GATES’ format output:
‘COLOCATED_GATES’: process_colocated_gates
- ‘COSMO_COORD’ format output:
‘COSMO_COORD’: process_cosmo_coord ‘HZT_COORD’: process_hzt_coord
- ‘COSMO2RADAR’ format output:
‘COSMO2RADAR’: process_cosmo_to_radar
- ‘GRID’ format output:
‘RAW_GRID’: process_raw_grid ‘GRID’: process_grid ‘GRID_FIELDS_DIFF’: process_grid_fields_diff ‘GRID_MASK’: process_grid_mask ‘GRID_TEXTURE’: process_grid_texture ‘NORMALIZE_LUMINOSITY’: process_normalize_luminosity ‘PIXEL_FILTER’: process_pixel_filter
- ‘GRID_TIMEAVG’ format output:
‘GRID_TIME_STATS’: process_grid_time_stats ‘GRID_TIME_STATS2’: process_grid_time_stats2
- ‘INTERCOMP’ format output:
‘INTERCOMP’: process_intercomp ‘INTERCOMP_FIELDS’: process_intercomp_fields ‘INTERCOMP_TIME_AVG’: process_intercomp_time_avg
- ‘ML’ format output:
‘ML_DETECTION’: process_melting_layer
- ‘MONITORING’ format output:
‘GC_MONITORING’: process_gc_monitoring ‘MONITORING’: process_monitoring
- ‘OCCURRENCE’ format output:
‘OCCURRENCE’: process_occurrence ‘OCCURRENCE_PERIOD’: process_occurrence_period ‘TIMEAVG_STD’: process_time_avg_std
- ‘QVP’ format output:
‘EVP’: process_evp ‘QVP’: process_qvp ‘rQVP’: process_rqvp ‘SVP’: process_svp ‘TIME_HEIGHT’: process_time_height ‘TIME_ALONG_COORD’: process_ts_along_coord
- ‘SPARSE_GRID’ format output:
‘ZDR_COLUMN’: process_zdr_column
- ‘SUN_HITS’ format output:
‘SUN_HITS’: process_sun_hits
- ‘TIMEAVG’ format output:
‘FLAG_TIME_AVG’: process_time_avg_flag ‘TIME_AVG’: process_time_avg ‘WEIGHTED_TIME_AVG’: process_weighted_time_avg ‘TIME_STATS’: process_time_stats ‘TIME_STATS2’: process_time_stats2 ‘RAIN_ACCU’: process_rainfall_accumulation
- ‘TIMESERIES’ format output:
‘GRID_POINT_MEASUREMENT’: process_grid_point ‘POINT_MEASUREMENT’: ‘process_point_measurement’ ‘TRAJ_ANTENNA_PATTERN’: process_traj_antenna_pattern ‘TRAJ_ATPLANE’: process_traj_atplane ‘TRAJ_LIGHTNING’: process_traj_lightning
- ‘TRAJ_ONLY’ format output:
‘TRAJ’: process_trajectory
- dsname : str
Name of dataset
Returns: - func_name : str or processing function
pyrad function used to process the data set type
- dsformat : str
data set format, i.e.: ‘VOL’, etc.
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pyrad.proc.process_Doppler_velocity(procstatus, dscfg, radar_list=None)[source]¶ Compute the Doppler velocity from the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
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pyrad.proc.process_Doppler_velocity_iq(procstatus, dscfg, radar_list=None)[source]¶ Compute the Doppler velocity from the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- direction : str
The convention used in the Doppler mean field. Can be negative_away or negative_towards
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
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pyrad.proc.process_Doppler_width(procstatus, dscfg, radar_list=None)[source]¶ Compute the Doppler spectrum width from the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
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pyrad.proc.process_Doppler_width_iq(procstatus, dscfg, radar_list=None)[source]¶ Compute the Doppler spectrum width from the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signals
- lag : int
Time lag used in the denominator of the computation
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_attenuation(procstatus, dscfg, radar_list=None)[source]¶ Computes specific attenuation and specific differential attenuation using the Z-Phi method and corrects reflectivity and differential reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- ATT_METHOD : float. Dataset keyword
The attenuation estimation method used. One of the following: ZPhi, Philin
- fzl : float. Dataset keyword
The default freezing level height. It will be used if no temperature field name is specified or the temperature field is not in the radar object. Default 2000.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_azimuthal_average(procstatus, dscfg, radar_list=None)[source]¶ Averages radar data in azimuth obtaining and RHI as a result
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- angle : float or None. Dataset keyword
The center angle to average. If not set or set to -1 all available azimuth angles will be used
- delta_azi : float. Dataset keyword
The angle span to average. If not set or set to -1 all the available azimuth angles will be used
- avg_type : str. Dataset keyword
Average type. Can be mean or median
- nvalid_min : int. Dataset keyword
the (minimum) radius of the region of interest in m. Default half the largest resolution
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the gridded data
- ind_rad : int
radar index
-
pyrad.proc.process_bird_density(procstatus, dscfg, radar_list=None)[source]¶ Computes the bird density from the volumetric reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- sigma_bird : float. Dataset keyword
The bird radar cross section
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_birds_id(procstatus, dscfg, radar_list=None)[source]¶ identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Birds
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_ccor(procstatus, dscfg, radar_list=None)[source]¶ Computes the Clutter Correction Ratio, i.e. the ratio between the signal without Doppler filtering and the signal with Doppler filtering
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_cdf(procstatus, dscfg, radar_list=None)[source]¶ Collects the fields necessary to compute the Cumulative Distribution Function
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_cdr(procstatus, dscfg, radar_list=None)[source]¶ Computes Circular Depolarization Ratio
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_clt_to_echo_id(procstatus, dscfg, radar_list=None)[source]¶ Converts clutter exit code from rad4alp into pyrad echo ID
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_colocated_gates(procstatus, dscfg, radar_list=None)[source]¶ Find colocated gates within two radars
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- h_tol : float. Dataset keyword
Tolerance in altitude difference between radar gates [m]. Default 100.
- latlon_tol : float. Dataset keyword
Tolerance in latitude and longitude position between radar gates [deg]. Default 0.0005
- vol_d_tol : float. Dataset keyword
Tolerance in pulse volume diameter [m]. Default 100.
- vismin : float. Dataset keyword
Minimum visibility [percent]. Default None.
- hmin : float. Dataset keyword
Minimum altitude [m MSL]. Default None.
- hmax : float. Dataset keyword
Maximum altitude [m MSL]. Default None.
- rmin : float. Dataset keyword
Minimum range [m]. Default None.
- rmax : float. Dataset keyword
Maximum range [m]. Default None.
- elmin : float. Dataset keyword
Minimum elevation angle [deg]. Default None.
- elmax : float. Dataset keyword
Maximum elevation angle [deg]. Default None.
- azrad1min : float. Dataset keyword
Minimum azimuth angle [deg] for radar 1. Default None.
- azrad1max : float. Dataset keyword
Maximum azimuth angle [deg] for radar 1. Default None.
- azrad2min : float. Dataset keyword
Minimum azimuth angle [deg] for radar 2. Default None.
- azrad2max : float. Dataset keyword
Maximum azimuth angle [deg] for radar 2. Default None.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : radar object
radar object containing the flag field
- ind_rad : int
radar index
-
pyrad.proc.process_correct_bias(procstatus, dscfg, radar_list=None)[source]¶ Corrects a bias on the data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type to correct for bias
- bias : float. Dataset keyword
The bias to be corrected [dB]. Default 0
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_correct_noise_rhohv(procstatus, dscfg, radar_list=None)[source]¶ identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Precipitation
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The data types used in the correction
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_correct_phidp0(procstatus, dscfg, radar_list=None)[source]¶ corrects phidp of the system phase
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
The minimum range where to look for valid data [m]
- rmax : float. Dataset keyword
The maximum range where to look for valid data [m]
- rcell : float. Dataset keyword
The length of a continuous cell to consider it valid precip [m]
- Zmin : float. Dataset keyword
The minimum reflectivity [dBZ]
- Zmax : float. Dataset keyword
The maximum reflectivity [dBZ]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_cosmo(procstatus, dscfg, radar_list=None)[source]¶ Gets COSMO data and put it in radar coordinates
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
arbitrary data type
- keep_in_memory : int. Dataset keyword
if set keeps the COSMO data dict, the COSMO coordinates dict and the COSMO field in radar coordinates in memory
- regular_grid : int. Dataset keyword
if set it is assume that the radar has a grid constant in time and there is no need to compute a new COSMO field if the COSMO data has not changed
- cosmo_type : str. Dataset keyword
name of the COSMO field to process. Default TEMP
- cosmo_variables : list of strings. Dataset keyword
Py-art name of the COSMO fields. Default temperature
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_cosmo_coord(procstatus, dscfg, radar_list=None)[source]¶ Gets the COSMO indices corresponding to each cosmo coordinates
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
arbitrary data type
- cosmopath : string. General keyword
path where to store the look up table
- model : string. Dataset keyword
The COSMO model to use. Can be cosmo-1, cosmo-1e, cosmo-2, cosmo-7
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_cosmo_lookup_table(procstatus, dscfg, radar_list=None)[source]¶ Gets COSMO data and put it in radar coordinates using look up tables computed or loaded when initializing
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
arbitrary data type
- lookup_table : int. Dataset keyword
if set a pre-computed look up table for the COSMO coordinates is loaded. Otherwise the look up table is computed taking the first radar object as reference
- regular_grid : int. Dataset keyword
if set it is assume that the radar has a grid constant in time and therefore there is no need to interpolate the COSMO field in memory to the current radar grid
- cosmo_type : str. Dataset keyword
name of the COSMO field to process. Default TEMP
- cosmo_variables : list of strings. Dataset keyword
Py-art name of the COSMO fields. Default temperature
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_cosmo_to_radar(procstatus, dscfg, radar_list=None)[source]¶ Gets COSMO data and put it in radar coordinates using look up tables
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
arbitrary data type
- cosmo_type : str. Dataset keyword
name of the COSMO field to process. Default TEMP
- cosmo_variables : list of strings. Dataset keyword
Py-art name of the COSMO fields. Default temperature
- cosmo_time_index_min, cosmo_time_index_max : int
minimum and maximum indices of the COSMO data to retrieve. If a value is provided only data corresponding to the time indices within the interval will be used. If None all data will be used. Default None
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_dealias_fourdd(procstatus, dscfg, radar_list=None)[source]¶ Dealiases the Doppler velocity field using the 4DD technique from Curtis and Houze, 2001
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- filt : int. Dataset keyword
Flag controlling Bergen and Albers filter, 1 = yes, 0 = no.
- sign : int. Dataset keyword
Sign convention which the radial velocities in the volume created from the sounding data will will. This should match the convention used in the radar data. A value of 1 represents when positive values velocities are towards the radar, -1 represents when negative velocities are towards the radar.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_dealias_region_based(procstatus, dscfg, radar_list=None)[source]¶ Dealiases the Doppler velocity field using a region based algorithm
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- interval_splits : int, optional
Number of segments to split the nyquist interval into when finding regions of similar velocity. More splits creates a larger number of initial regions which takes longer to process but may result in better dealiasing. The default value of 3 seems to be a good compromise between performance and artifact free dealiasing. This value is not used if the interval_limits parameter is not None.
- skip_between_rays, skip_along_ray : int, optional
Maximum number of filtered gates to skip over when joining regions, gaps between region larger than this will not be connected. Parameters specify the maximum number of filtered gates between and along a ray. Set these parameters to 0 to disable unfolding across filtered gates.
- centered : bool, optional
True to apply centering to each sweep after the dealiasing algorithm so that the average number of unfolding is near 0. False does not apply centering which may results in individual sweeps under or over folded by the nyquist interval.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_dealias_unwrap_phase(procstatus, dscfg, radar_list=None)[source]¶ Dealiases the Doppler velocity field using multi-dimensional phase unwrapping
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- unwrap_unit : {‘ray’, ‘sweep’, ‘volume’}, optional
Unit to unwrap independently. ‘ray’ will unwrap each ray individually, ‘sweep’ each sweep, and ‘volume’ will unwrap the entire volume in a single pass. ‘sweep’, the default, often gives superior results when the lower sweeps of the radar volume are contaminated by clutter. ‘ray’ does not use the gatefilter parameter and rays where gates ared masked will result in poor dealiasing for that ray.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_dem(procstatus, dscfg, radar_list=None)[source]¶ Gets DEM data and put it in radar coordinates
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
arbitrary data type
- keep_in_memory : int. Dataset keyword
if set keeps the COSMO data dict, the COSMO coordinates dict and the COSMO field in radar coordinates in memory. Default False
- regular_grid : int. Dataset keyword
if set it is assume that the radar has a grid constant in time and there is no need to compute a new COSMO field if the COSMO data has not changed. Default False
- dem_field : str. Dataset keyword
name of the DEM field to process
- demfile : str. Dataset keyword
Name of the file containing the DEM data
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_differential_phase(procstatus, dscfg, radar_list=None)[source]¶ Computes the differential phase from the spectral differential phase and the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_differential_phase_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes the differential phase from the horizontal and vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- phase_offset : float. Dataset keyword
The system differential phase offset to remove
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_differential_reflectivity(procstatus, dscfg, radar_list=None)[source]¶ Computes differential reflectivity from the horizontal and vertical spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_differential_reflectivity_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes differential reflectivity from the horizontal and vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- lag : int
The time lag to use in the estimators
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_echo_filter(procstatus, dscfg, radar_list=None)[source]¶ Masks all echo types that are not of the class specified in keyword echo_type
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- echo_type : int or list of ints
The type of echoes to keep: 1 noise, 2 clutter, 3 precipitation. Default 3
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_echo_id(procstatus, dscfg, radar_list=None)[source]¶ identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Precipitation
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_estimate_phidp0(procstatus, dscfg, radar_list=None)[source]¶ estimates the system differential phase offset at each ray
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
The minimum range where to look for valid data [m]
- rmax : float. Dataset keyword
The maximum range where to look for valid data [m]
- rcell : float. Dataset keyword
The length of a continuous cell to consider it valid precip [m]
- Zmin : float. Dataset keyword
The minimum reflectivity [dBZ]
- Zmax : float. Dataset keyword
The maximum reflectivity [dBZ]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_evp(procstatus, dscfg, radar_list=None)[source]¶ Computes enhanced vertical profiles, by averaging over height levels PPI data.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- lat, lon : float
latitude and longitude of the point of interest [deg]
- latlon_tol : float
tolerance in latitude and longitude in deg. Default 0.0005
- delta_rng, delta_azi : float
maximum range distance [m] and azimuth distance [degree] from the central point of the evp containing data to average. Default 5000. and 10.
- hmax : float
The maximum height to plot [m]. Default 10000.
- hres : float
The height resolution [m]. Default 250.
- avg_type : str
The type of averaging to perform. Can be either “mean” or “median” Default “mean”
- nvalid_min : int
Minimum number of valid points to consider the data valid when performing the averaging. Default 1
- interp_kind : str
type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. Default ‘none’. ‘none’ will select from all data points within the regular grid height bin the closest to the center of the bin. ‘nearest’ will select the closest data point to the center of the height bin regardless if it is within the height bin or not. Data points can be masked values If another type of interpolation is selected masked values will be eliminated from the data points before the interpolation
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the EVP and a keyboard stating whether the processing has finished or not.
- ind_rad : int
radar index
-
pyrad.proc.process_fft(procstatus, dscfg, radar_list=None)[source]¶ Compute the Doppler spectra form the IQ data with a Fourier transform
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- window : list of str
Parameters of the window used to obtain the spectra. The parameters are the ones corresponding to function scipy.signal.windows.get_window. It can also be [‘None’].
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_fields_diff(procstatus, dscfg, radar_list=None)[source]¶ Computes the field difference between RADAR001 and radar002, i.e. RADAR001-RADAR002. Assumes both radars have the same geometry
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing a radar object containing the field differences
- ind_rad : int
radar index
-
pyrad.proc.process_filter_0Doppler(procstatus, dscfg, radar_list=None)[source]¶ Function to filter the 0-Doppler line bin and neighbours of the Doppler spectra
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- filter_width : float
The Doppler filter width. Default 0.
- filter_units : str
Can be ‘m/s’ or ‘Hz’. Default ‘m/s’
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_filter_snr(procstatus, dscfg, radar_list=None)[source]¶ filters out low SNR echoes
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- SNRmin : float. Dataset keyword
The minimum SNR to keep the data.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_filter_spectra_noise(procstatus, dscfg, radar_list=None)[source]¶ Filter the noise of the Doppler spectra by clipping any data below the noise level plus a margin
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- clipping_level : float
The clipping level [dB above noise level]. Default 10.
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_filter_srhohv(procstatus, dscfg, radar_list=None)[source]¶ Filter Doppler spectra as a function of spectral RhoHV
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- sRhoHV_threshold : float
Data with sRhoHV module above this threshold will be filtered. Default 1.
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_filter_vel_diff(procstatus, dscfg, radar_list=None)[source]¶ filters out range gates that could not be used for Doppler velocity estimation
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_filter_visibility(procstatus, dscfg, radar_list=None)[source]¶ filters out rays gates with low visibility and corrects the reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- VISmin : float. Dataset keyword
The minimum visibility to keep the data.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_fixed_rng(procstatus, dscfg, radar_list=None)[source]¶ Obtains radar data at a fixed range
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of strings. Dataset keyword
The fields we want to extract
- rng : float. Dataset keyword
The fixed range [m]
- RngTol : float. Dataset keyword
The tolerance between the nominal range and the radar range
- ele_min, ele_max, azi_min, azi_max : floats. Dataset keyword
The azimuth and elevation limits of the data [deg]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and metadata at the point of interest
- ind_rad : int
radar index
-
pyrad.proc.process_fixed_rng_span(procstatus, dscfg, radar_list=None)[source]¶ For each azimuth-elevation gets the data within a fixed range span and computes a user-defined statistic: mean, min, max, mode, median
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of strings. Dataset keyword
The fields we want to extract
- rmin, rmax : float. Dataset keyword
The range limits [m]
- ele_min, ele_max, azi_min, azi_max : floats. Dataset keyword
The azimuth and elevation limits of the data [deg]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and metadata at the point of interest
- ind_rad : int
radar index
-
pyrad.proc.process_gc_monitoring(procstatus, dscfg, radar_list=None)[source]¶ computes ground clutter monitoring statistics
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- excessgatespath : str. Config keyword
The path to the gates in excess of quantile location
- excessgates_fname : str. Dataset keyword
The name of the gates in excess of quantile file
- datatype : list of string. Dataset keyword
The input data types
- step : float. Dataset keyword
The width of the histogram bin. Default is None. In that case the default step in function get_histogram_bins is used
- regular_grid : Boolean. Dataset keyword
Whether the radar has a Boolean grid or not. Default False
- val_min : Float. Dataset keyword
Minimum value to consider that the gate has signal. Default None
- filter_prec : str. Dataset keyword
Give which type of volume should be filtered. None, no filtering; keep_wet, keep wet volumes; keep_dry, keep dry volumes.
- rmax_prec : float. Dataset keyword
Maximum range to consider when looking for wet gates [m]
- percent_prec_max : float. Dataset keyword
Maxim percentage of wet gates to consider the volume dry
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : Radar
radar object containing histogram data
- ind_rad : int
radar index
-
pyrad.proc.process_grid(procstatus, dscfg, radar_list=None)[source]¶ Puts the radar data in a regular grid
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- gridconfig : dictionary. Dataset keyword
Dictionary containing some or all of this keywords: xmin, xmax, ymin, ymax, zmin, zmax : floats
minimum and maximum horizontal distance from grid origin [km] and minimum and maximum vertical distance from grid origin [m] Defaults -40, 40, -40, 40, 0., 10000.
- hres, vres : floats
horizontal and vertical grid resolution [m] Defaults 1000., 500.
- latorig, lonorig, altorig : floats
latitude and longitude of grid origin [deg] and altitude of grid origin [m MSL] Defaults the latitude, longitude and altitude of the radar
- wfunc : str. Dataset keyword
the weighting function used to combine the radar gates close to a grid point. Possible values BARNES, BARNES2, CRESSMAN, NEAREST Default NEAREST
- roif_func : str. Dataset keyword
the function used to compute the region of interest. Possible values: dist_beam, constant
- roi : float. Dataset keyword
the (minimum) radius of the region of interest in m. Default half the largest resolution
- beamwidth : float. Dataset keyword
the radar antenna beamwidth [deg]. If None that of the key radar_beam_width_h in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present a default 1 deg value will be used
- beam_spacing : float. Dataset keyword
the beam spacing, i.e. the ray angle resolution [deg]. If None, that of the attribute ray_angle_res of the radar object will be used. If the attribute is None a default 1 deg value will be used
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the gridded data
- ind_rad : int
radar index
-
pyrad.proc.process_grid_fields_diff(procstatus, dscfg, radar_list=None)[source]¶ Computes grid field differences
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing a radar object containing the field differences
- ind_rad : int
radar index
-
pyrad.proc.process_grid_mask(procstatus, dscfg, radar_list=None)[source]¶ Mask data. Puts True if data is above a certain threshold and false otherwise.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of Radar objects
Optional. list of radar objects
- threshold : float
Threshold used for the mask. Values below threshold are set to False. Above threshold are set to True. Default 0.
- x_dir_ext, y_dir_ext : int
Number of pixels by which to extend the mask on each side of the west-east direction and south-north direction
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_grid_point(procstatus, dscfg, radar_list=None)[source]¶ Obtains the grid data at a point location.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- latlon : boolean. Dataset keyword
if True position is obtained from latitude, longitude information, otherwise position is obtained from grid index (iz, iy, ix).
- lon : float. Dataset keyword
the longitude [deg]. Use when latlon is True.
- lat : float. Dataset keyword
the latitude [deg]. Use when latlon is True.
- alt : float. Dataset keyword
altitude [m MSL]. Use when latlon is True.
- iz, iy, ix : int. Dataset keyword
The grid indices. Use when latlon is False
- latlonTol : float. Dataset keyword
latitude-longitude tolerance to determine which grid point to use [deg]
- altTol : float. Dataset keyword
Altitude tolerance to determine which grid point to use [deg]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and metadata at the point of interest
- ind_rad : int
radar index
-
pyrad.proc.process_grid_texture(procstatus, dscfg, radar_list=None)[source]¶ Computes the 2D texture of a gridded field
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- xwind, ywind : int
The size of the local window in the x and y axis. Default 7
- fill_value : float
The value with which to fill masked data. Default np.NaN
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing a radar object containing the field differences
- ind_rad : int
radar index
-
pyrad.proc.process_grid_time_stats(procstatus, dscfg, radar_list=None)[source]¶ computes the temporal statistics of a field
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be performed over the entire data. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- lin_trans: int. Dataset keyword
If 1 apply linear transformation before averaging
- use_nan : bool. Dataset keyword
If true non valid data will be used
- nan_value : float. Dataset keyword
The value of the non valid data. Default 0
- stat: string. Dataset keyword
Statistic to compute: Can be mean, std, cov, min, max. Default mean
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_grid_time_stats2(procstatus, dscfg, radar_list=None)[source]¶ computes temporal statistics of a field
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be performed over the entire data. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- stat: string. Dataset keyword
Statistic to compute: Can be median, mode, percentileXX
- use_nan : bool. Dataset keyword
If true non valid data will be used
- nan_value : float. Dataset keyword
The value of the non valid data. Default 0
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_hydroclass(procstatus, dscfg, radar_list=None)[source]¶ Classifies precipitation echoes
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- HYDRO_METHOD : string. Dataset keyword
The hydrometeor classification method. One of the following: SEMISUPERVISED
- RADARCENTROIDS : string. Dataset keyword
Used with HYDRO_METHOD SEMISUPERVISED. The name of the radar of which the derived centroids will be used. One of the following: A Albis, L Lema, P Plaine Morte, DX50
- compute_entropy : bool. Dataset keyword
If true the entropy is computed and the field hydroclass_entropy is output
- output_distances : bool. Dataset keyword
If true the de-mixing algorithm based on the distances to the centroids is computed and the field proportions of each hydrometeor in the radar range gate is output
- vectorize : bool. Dataset keyword
If true a vectorized version of the algorithm is used
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_hzt(procstatus, dscfg, radar_list=None)[source]¶ Gets iso0 degree data in HZT format and put it in radar coordinates
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- metranet_read_lib : str. Global keyword
Type of METRANET reader library used to read the data. Can be ‘C’ or ‘python’
- datatype : string. Dataset keyword
arbitrary data type
- keep_in_memory : int. Dataset keyword
if set keeps the COSMO data dict, the COSMO coordinates dict and the COSMO field in radar coordinates in memory
- regular_grid : int. Dataset keyword
if set it is assume that the radar has a grid constant in time and there is no need to compute a new COSMO field if the COSMO data has not changed
- cosmo_type : str. Dataset keyword
name of the COSMO field to process. Default TEMP
- cosmo_variables : list of strings. Dataset keyword
Py-art name of the COSMO fields. Default temperature
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_hzt_coord(procstatus, dscfg, radar_list=None)[source]¶ Gets the HZT indices corresponding to each HZT coordinates
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- metranet_read_lib : str. Global keyword
Type of METRANET reader library used to read the data. Can be ‘C’ or ‘python’
- datatype : string. Dataset keyword
arbitrary data type
- cosmopath : string. General keyword
path where to store the look up table
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_hzt_lookup_table(procstatus, dscfg, radar_list=None)[source]¶ Gets HZT data and put it in radar coordinates using look up tables computed or loaded when initializing
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- metranet_read_lib : str. Global keyword
Type of METRANET reader library used to read the data. Can be ‘C’ or ‘python’
- datatype : string. Dataset keyword
arbitrary data type
- lookup_table : int. Dataset keyword
if set a pre-computed look up table for the COSMO coordinates is loaded. Otherwise the look up table is computed taking the first radar object as reference
- regular_grid : int. Dataset keyword
if set it is assume that the radar has a grid constant in time and therefore there is no need to interpolate the COSMO field in memory to the current radar grid
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_ifft(procstatus, dscfg, radar_list=None)[source]¶ Compute the Doppler spectrum width from the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_intercomp(procstatus, dscfg, radar_list=None)[source]¶ intercomparison between two radars
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- coloc_data_dir : string. Dataset keyword
name of the directory containing the csv file with colocated data
- coloc_radars_name : string. Dataset keyword
string identifying the radar names
- azi_tol : float. Dataset keyword
azimuth tolerance between the two radars. Default 0.5 deg
- ele_tol : float. Dataset keyword
elevation tolerance between the two radars. Default 0.5 deg
- rng_tol : float. Dataset keyword
range tolerance between the two radars. Default 50 m
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing a dictionary with intercomparison data and the key “final” which contains a boolean that is true when all volumes have been processed
- ind_rad : int
radar index
-
pyrad.proc.process_intercomp_fields(procstatus, dscfg, radar_list=None)[source]¶ intercomparison between two radars
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing a dictionary with intercomparison data
- ind_rad : int
radar index
-
pyrad.proc.process_intercomp_time_avg(procstatus, dscfg, radar_list=None)[source]¶ intercomparison between the average reflectivity of two radars
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- coloc_data_dir : string. Dataset keyword
name of the directory containing the csv file with colocated data
- coloc_radars_name : string. Dataset keyword
string identifying the radar names
- azi_tol : float. Dataset keyword
azimuth tolerance between the two radars. Default 0.5 deg
- ele_tol : float. Dataset keyword
elevation tolerance between the two radars. Default 0.5 deg
- rng_tol : float. Dataset keyword
range tolerance between the two radars. Default 50 m
- clt_max : int. Dataset keyword
maximum number of samples that can be clutter contaminated. Default 100 i.e. all
- phi_excess_max : int. Dataset keyword
maximum number of samples that can have excess instantaneous PhiDP. Default 100 i.e. all
- non_rain_max : int. Dataset keyword
maximum number of samples that can be no rain. Default 100 i.e. all
- phi_avg_max : float. Dataset keyword
maximum average PhiDP allowed. Default 600 deg i.e. any
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing a dictionary with intercomparison data and the key “final” which contains a boolean that is true when all volumes have been processed
- ind_rad : int
radar index
-
pyrad.proc.process_kdp_leastsquare_double_window(procstatus, dscfg, radar_list=None)[source]¶ Computes specific differential phase using a piecewise least square method
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rwinds : float. Dataset keyword
The length of the short segment for the least square method [m]
- rwindl : float. Dataset keyword
The length of the long segment for the least square method [m]
- Zthr : float. Dataset keyword
The threshold defining which estimated data to use [dBZ]
- vectorize : Bool. Dataset keyword
Whether to vectorize the KDP processing. Default false
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_kdp_leastsquare_single_window(procstatus, dscfg, radar_list=None)[source]¶ Computes specific differential phase using a piecewise least square method
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rwind : float. Dataset keyword
The length of the segment for the least square method [m]
- vectorize : bool. Dataset keyword
Whether to vectorize the KDP processing. Default false
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_l(procstatus, dscfg, radar_list=None)[source]¶ Computes L parameter
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_mean_phase_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes the mean phase from the horizontal or vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_melting_layer(procstatus, dscfg, radar_list=None)[source]¶ Detects the melting layer
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_monitoring(procstatus, dscfg, radar_list=None)[source]¶ computes monitoring statistics
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- step : float. Dataset keyword
The width of the histogram bin. Default is None. In that case the default step in function get_histogram_bins is used
- max_rays : int. Dataset keyword
The maximum number of rays per sweep used when computing the histogram. If set above 0 the number of rays per sweep will be checked and if above max_rays the last rays of the sweep will be removed
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : Radar
radar object containing histogram data
- ind_rad : int
radar index
-
pyrad.proc.process_noise_power(procstatus, dscfg, radar_list=None)[source]¶ Computes the noise power from the spectra
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- units : str
The units of the returned signal. Can be ‘ADU’, ‘dBADU’ or ‘dBm’
- navg : int
Number of spectra averaged
- rmin : int
Range from which the data is used to estimate the noise
- nnoise_min : int
Minimum number of samples to consider the estimated noise power valid
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_normalize_luminosity(procstatus, dscfg, radar_list=None)[source]¶ Normalize the data by the sinus of the sun elevation. The sun elevation is computed at the central pixel.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_occurrence(procstatus, dscfg, radar_list=None)[source]¶ computes the frequency of occurrence of data. It looks only for gates where data is present.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- regular_grid : Boolean. Dataset keyword
Whether the radar has a Boolean grid or not. Default False
- rmin, rmax : float. Dataset keyword
minimum and maximum ranges where the computation takes place. If -1 the whole range is considered. Default is -1
- val_min : Float. Dataset keyword
Minimum value to consider that the gate has signal. Default None
- filter_prec : str. Dataset keyword
Give which type of volume should be filtered. None, no filtering; keep_wet, keep wet volumes; keep_dry, keep dry volumes.
- rmax_prec : float. Dataset keyword
Maximum range to consider when looking for wet gates [m]
- percent_prec_max : float. Dataset keyword
Maxim percentage of wet gates to consider the volume dry
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_occurrence_period(procstatus, dscfg, radar_list=None)[source]¶ computes the frequency of occurrence over a long period of time by adding together shorter periods
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- regular_grid : Boolean. Dataset keyword
Whether the radar has a Boolean grid or not. Default False
- rmin, rmax : float. Dataset keyword
minimum and maximum ranges where the computation takes place. If -1 the whole range is considered. Default is -1
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_outlier_filter(procstatus, dscfg, radar_list=None)[source]¶ filters out gates which are outliers respect to the surrounding
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- threshold : float. Dataset keyword
The distance between the value of the examined range gate and the median of the surrounding gates to consider the gate an outlier
- nb : int. Dataset keyword
The number of neighbours (to one side) to analyse. i.e. 2 would correspond to 24 gates
- nb_min : int. Dataset keyword
Minimum number of neighbouring gates to consider the examined gate valid
- percentile_min, percentile_max : float. Dataset keyword
gates below (above) these percentiles (computed over the sweep) are considered potential outliers and further examined
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_phidp_kdp_Kalman(procstatus, dscfg, radar_list=None)[source]¶ Computes specific differential phase and differential phase using the Kalman filter as proposed by Schneebeli et al. The data is assumed to be clutter free and continous
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- parallel : boolean. Dataset keyword
if set use parallel computing
- get_phidp : boolean. Datset keyword
if set the PhiDP computed by integrating the resultant KDP is added to the radar field
- frequency : float. Dataset keyword
the radar frequency [Hz]. If None that of the key frequency in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present it will be assumed that the radar is C band
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_phidp_kdp_Maesaka(procstatus, dscfg, radar_list=None)[source]¶ Estimates PhiDP and KDP using the method by Maesaka. This method only retrieves data in rain (i.e. below the melting layer)
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
The minimum range where to look for valid data [m]
- rmax : float. Dataset keyword
The maximum range where to look for valid data [m]
- rcell : float. Dataset keyword
The length of a continuous cell to consider it valid precip [m]
- Zmin : float. Dataset keyword
The minimum reflectivity [dBZ]
- Zmax : float. Dataset keyword
The maximum reflectivity [dBZ]
- fzl : float. Dataset keyword
The freezing level height [m]. Default 2000.
- ml_thickness : float. Dataset keyword
The melting layer thickness in meters. Default 700.
- beamwidth : float. Dataset keyword
the antenna beamwidth [deg]. If None that of the keys radar_beam_width_h or radar_beam_width_v in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the beamwidth will be set to None
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_phidp_kdp_Vulpiani(procstatus, dscfg, radar_list=None)[source]¶ Computes specific differential phase and differential phase using the method developed by Vulpiani et al. The data is assumed to be clutter free and monotonous
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rwind : float. Dataset keyword
The length of the segment [m]
- n_iter : int. Dataset keyword
number of iterations
- interp : boolean. Dataset keyword
if set non valid values are interpolated using neighbouring valid values
- parallel : boolean. Dataset keyword
if set use parallel computing
- get_phidp : boolean. Datset keyword
if set the PhiDP computed by integrating the resultant KDP is added to the radar field
- frequency : float. Dataset keyword
the radar frequency [Hz]. If None that of the key frequency in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present it will be assumed that the radar is C band
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_phidp_kdp_lp(procstatus, dscfg, radar_list=None)[source]¶ Estimates PhiDP and KDP using a linear programming algorithm. This method only retrieves data in rain (i.e. below the melting layer)
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- fzl : float. Dataset keyword
The freezing level height [m]. Default 2000.
- ml_thickness : float. Dataset keyword
The melting layer thickness in meters. Default 700.
- beamwidth : float. Dataset keyword
the antenna beamwidth [deg]. If None that of the keys radar_beam_width_h or radar_beam_width_v in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the beamwidth will be set to None
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_pixel_filter(procstatus, dscfg, radar_list=None)[source]¶ Masks all pixels that are not of the class specified in keyword pixel_type
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- pixel_type : int or list of ints
The type of pixels to keep: 0 No data, 1 Below threshold, 2 Above threshold. Default 2
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_point_measurement(procstatus, dscfg, radar_list=None)[source]¶ Obtains the radar data at a point location.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- single_point : boolean. Dataset keyword
if True only one gate per radar volume is going to be kept. Otherwise all gates within the azimuth and elevation tolerance are going to be kept. This is useful to extract all data from fixed pointing scans. Default True
- latlon : boolean. Dataset keyword
if True position is obtained from latitude, longitude information, otherwise position is obtained from antenna coordinates (range, azimuth, elevation).
- truealt : boolean. Dataset keyword
if True the user input altitude is used to determine the point of interest. if False use the altitude at a given radar elevation ele over the point of interest.
- lon : float. Dataset keyword
the longitude [deg]. Use when latlon is True.
- lat : float. Dataset keyword
the latitude [deg]. Use when latlon is True.
- alt : float. Dataset keyword
altitude [m MSL]. Use when latlon is True.
- ele : float. Dataset keyword
radar elevation [deg]. Use when latlon is False or when latlon is True and truealt is False
- azi : float. Dataset keyword
radar azimuth [deg]. Use when latlon is False
- rng : float. Dataset keyword
range from radar [m]. Use when latlon is False
- AziTol : float. Dataset keyword
azimuthal tolerance to determine which radar azimuth to use [deg]
- EleTol : float. Dataset keyword
elevation tolerance to determine which radar elevation to use [deg]
- RngTol : float. Dataset keyword
range tolerance to determine which radar bin to use [m]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and metadata at the point of interest
- ind_rad : int
radar index
-
pyrad.proc.process_pol_variables(procstatus, dscfg, radar_list=None)[source]¶ Computes the polarimetric variables from the complex spectra
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal. Default False
- smooth_window : int or None
Size of the moving Gaussian smoothing window. If none no smoothing will be applied. Default None
- variables : list of str
list of variables to compute. Default dBZ
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_pol_variables_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes the polarimetric variables from the IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- lag : int
The time lag to use in the estimators
- direction : str
The convention used in the Doppler mean field. Can be negative_away or negative_towards
- variables : list of str
list of variables to compute. Default dBZ
- phase_offset : float. Dataset keyword
The system differential phase offset to remove
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_qvp(procstatus, dscfg, radar_list=None)[source]¶ Computes quasi vertical profiles, by averaging over height levels PPI data.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- angle : int or float
If the radar object contains a PPI volume, the sweep number to use, if it contains an RHI volume the elevation angle. Default 0.
- ang_tol : float
If the radar object contains an RHI volume, the tolerance in the elevation angle for the conversion into PPI
- hmax : float
The maximum height to plot [m]. Default 10000.
- hres : float
The height resolution [m]. Default 50
- avg_type : str
The type of averaging to perform. Can be either “mean” or “median” Default “mean”
- nvalid_min : int
Minimum number of valid points to accept average. Default 30.
- interp_kind : str
type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. Default ‘none’ ‘none’ will select from all data points within the regular grid height bin the closest to the center of the bin. ‘nearest’ will select the closest data point to the center of the height bin regardless if it is within the height bin or not. Data points can be masked values If another type of interpolation is selected masked values will be eliminated from the data points before the interpolation
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the QVP and a keyboard stating whether the processing has finished or not.
- ind_rad : int
radar index
-
pyrad.proc.process_radar_resampling(procstatus, dscfg, radar_list=None)[source]¶ Resamples the radar data to mimic another radar with different geometry and antenna pattern
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
- datatype : list of string. Dataset keyword
The input data types
- antennaType : str. Dataset keyword
Type of antenna of the radar we want to get the view from. Can be AZIMUTH, ELEVATION, LOWBEAM, HIGHBEAM
- par_azimuth_antenna : dict. Global keyword
Dictionary containing the parameters of the PAR azimuth antenna, i.e. name of the file with the antenna elevation pattern and fixed antenna angle
- par_elevation_antenna : dict. Global keyword
Dictionary containing the parameters of the PAR elevation antenna, i.e. name of the file with the antenna azimuth pattern and fixed antenna angle
- asr_lowbeam_antenna : dict. Global keyword
Dictionary containing the parameters of the ASR low beam antenna, i.e. name of the file with the antenna elevation pattern and fixed antenna angle
- asr_highbeam_antenna : dict. Global keyword
Dictionary containing the parameters of the ASR high beam antenna, i.e. name of the file with the antenna elevation pattern and fixed antenna angle
- target_radar_pos : dict. Global keyword
Dictionary containing the latitude, longitude and altitude of the radar we want to get the view from. If not specifying it will assume the radar is collocated
- change_antenna_pattern : Bool. Dataset keyword
If true the target radar has a different antenna pattern than the observations radar. Default True
- rhi_resolution : Bool. Dataset keyword
Resolution of the synthetic RHI used to compute the data as viewed from the synthetic radar [deg]. Default 0.5
- max_altitude : float. Dataset keyword
Max altitude of the data to use when computing the view from the synthetic radar [m MSL]. Default 12000.
- latlon_tol : float. Dataset keyword
The tolerance in latitude and longitude to determine which synthetic radar gates are co-located with real radar gates [deg]. Default 0.04
- alt_tol : float. Dataset keyword
The tolerance in altitude to determine which synthetic radar gates are co-located with real radar gates [m]. Default 1000.
- distance_upper_bound : float. Dataset keyword
The maximum distance where to look for a neighbour when determining which synthetic radar gates are co-located with real radar gates [m]. Default 1000.
- use_cKDTree : Bool. Dataset keyword
Which function to use to find co-located real radar gates with the synthetic radar. If True a function using cKDTree from scipy.spatial is used. This function uses parameter distance_upper_bound. If False a native implementation is used that takes as parameters latlon_tol and alt_tol. Default True.
- pattern_thres : float. Dataset keyword
The minimum of the sum of the weights given to each value in order to consider the weighted quantile valid. It is related to the number of valid data points
- data_is_log : dict. Dataset keyword
Dictionary specifying for each field if it is in log (True) or linear units (False). Default False
- use_nans : dict. Dataset keyword
Dictionary specifying whether the nans have to be used in the computation of the statistics for each field. Default False
- nan_value : dict. Dataset keyword
Dictionary with the value to use to substitute the NaN values when computing the statistics of each field. Default 0
- moving_angle_min, moving_angle_max: float. Dataset keyword
The minimum and maximum azimuth angle (deg) of the target radar. Default 0, 360.
- ray_res: float
Ray resolution (deg). Default 1 deg.
- rng_min, rng_max:
The minimum and maximum range of the target radar (m). Default 0, 100000
- rng_res : float
The target radar range resolution (m). Default 100.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the new radar
- ind_rad : int
radar index
-
pyrad.proc.process_radial_noise_hs(procstatus, dscfg, radar_list=None)[source]¶ Computes the radial noise from the signal power using the Hildebrand and Sekhon 1974 method
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- rmin : float. Dataset keyword
The minimum range from which to start the computation
- nbins_min : int. Dataset keyword
The minimum number of noisy gates to consider the estimation valid
- max_std_pwr : float. Dataset keyword
The maximum standard deviation of the noise power to consider the estimation valid
- get_noise_pos : bool. Dataset keyword
If True a field flagging the position of the noisy gets will be returned
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_radial_noise_ivic(procstatus, dscfg, radar_list=None)[source]¶ Computes the radial noise from the signal power using the Ivic 2013 method
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- npulses_ray : int
Default number of pulses used in the computation of the ray. If the number of pulses is not in radar.instrument_parameters this will be used instead. Default 30
- ngates_min: int
minimum number of gates with noise to consider the retrieval valid. Default 800
- iterations: int
number of iterations in step 7. Default 10.
- get_noise_pos : bool
If true an additional field with gates containing noise according to the algorithm is produced
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_radial_velocity(procstatus, dscfg, radar_list=None)[source]¶ Estimates the radial velocity respect to the radar from the wind velocity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- latitude, longitude : float
arbitrary coordinates [deg] from where to compute the radial velocity. If any of them is None it will be the radar position
- altitude : float
arbitrary altitude [m MSL] from where to compute the radial velocity. If None it will be the radar altitude
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rainfall_accumulation(procstatus, dscfg, radar_list=None)[source]¶ Computes rainfall accumulation fields
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be performed over the entire data. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- use_nan : bool. Dataset keyword
If true non valid data will be used
- nan_value : float. Dataset keyword
The value of the non valid data. Default 0
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rainrate(procstatus, dscfg, radar_list=None)[source]¶ Estimates rainfall rate from polarimetric moments
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- RR_METHOD : string. Dataset keyword
The rainfall rate estimation method. One of the following: Z, ZPoly, KDP, A, ZKDP, ZA, hydro
- alpha, beta : float
factor and exponent of the R-Var power law R = alpha*Var^Beta. Default value depending on RR_METHOD. Z (0.0376, 0.6112), KDP (None, None), A (None, None)
- alphaz, betaz : float
factor and exponent of the R-Z power law R = alpha*Z^Beta. Default value (0.0376, 0.6112)
- alphazr, betazr : float
factor and exponent of the R-Z power law R = alpha*Z^Beta applied to rain in method hydro. Default value (0.0376, 0.6112)
- alphazs, betazs : float
factor and exponent of the R-Z power law R = alpha*Z^Beta applied to solid precipitation in method hydro. Default value (0.1, 0.5)
- alphakdp, betakdp : float
factor and exponent of the R-KDP power law R = alpha*KDP^Beta. Default value (None, None)
- alphaa, betaa : float
factor and exponent of the R-Ah power law R = alpha*Ah^Beta. Default value (None, None)
- thresh : float
In hybrid methods, Rainfall rate threshold at which the retrieval method used changes [mm/h]. Default value depending on RR_METHOD. ZKDP 10, ZA 10, hydro 10
- mp_factor : float
Factor by which the Z-R relation is multiplied in the melting layer in method hydro. Default 0.6
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_raw(procstatus, dscfg, radar_list=None)[source]¶ Dummy function that returns the initial input data set
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_raw_grid(procstatus, dscfg, radar_list=None)[source]¶ Dummy function that returns the initial input data set
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_raw_iq(procstatus, dscfg, radar_list=None)[source]¶ Dummy function that returns the initial input data set
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_raw_spectra(procstatus, dscfg, radar_list=None)[source]¶ Dummy function that returns the initial input data set
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rcs(procstatus, dscfg, radar_list=None)[source]¶ Computes the radar cross-section (assuming a point target) from radar reflectivity.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- kw2 : float. Dataset keyowrd
The water constant
- pulse_width : float. Dataset keyowrd
The pulse width [s]
- beamwidthv : float. Global keyword
The vertical polarization antenna beamwidth [deg]. Used if input is vertical reflectivity
- beamwidthh : float. Global keyword
The horizontal polarization antenna beamwidth [deg]. Used if input is horizontal reflectivity
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rcs_pr(procstatus, dscfg, radar_list=None)[source]¶ Computes the radar cross-section (assuming a point target) from radar reflectivity by first computing the received power and then the RCS from it.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- AntennaGainH, AntennaGainV : float. Dataset keyword
The horizontal (vertical) polarization antenna gain [dB]. If None it will be obtained from the attribute instrument_parameters of the radar object
- txpwrh, txpwrv : float. Dataset keyword
The transmitted power of the horizontal (vertical) channel [dBm]. If None it will be obtained from the attribute radar_calibration of the radar object
- mflossh, mflossv : float. Dataset keyword
The matching filter losses of the horizontal (vertical) channel [dB]. If None it will be obtained from the attribute radar_calibration of the radar object. Defaults to 0
- radconsth, radconstv : float. Dataset keyword
The horizontal (vertical) channel radar constant. If None it will be obtained from the attribute radar_calibration of the radar object
- lrxh, lrxv : float. Global keyword
The horizontal (vertical) receiver losses from the antenna feed to the reference point. [dB] positive value. Default 0
- ltxh, ltxv : float. Global keyword
The horizontal (vertical) transmitter losses from the output of the high power amplifier to the antenna feed. [dB] positive value. Default 0
- lradomeh, lradomev : float. Global keyword
The 1-way dry radome horizontal (vertical) channel losses. [dB] positive value. Default 0.
- attg : float. Dataset keyword
The gas attenuation [dB/km]. If none it will be obtained from the attribute radar_calibration of the radar object or assigned according to the radar frequency. Defaults to 0.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_reflectivity(procstatus, dscfg, radar_list=None)[source]¶ Computes reflectivity from the spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_reflectivity_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes reflectivity from the IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rhohv(procstatus, dscfg, radar_list=None)[source]¶ Computes RhoHV from the complex spectras
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rhohv_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes RhoHV from the horizontal and vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- lag : int
Time lag used in the computation
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_rhohv_rain(procstatus, dscfg, radar_list=None)[source]¶ Keeps only suitable data to evaluate the 80 percentile of RhoHV in rain
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
minimum range where to look for rain [m]. Default 1000.
- rmax : float. Dataset keyword
maximum range where to look for rain [m]. Default 50000.
- Zmin : float. Dataset keyword
minimum reflectivity to consider the bin as precipitation [dBZ]. Default 20.
- Zmax : float. Dataset keyword
maximum reflectivity to consider the bin as precipitation [dBZ] Default 40.
- ml_thickness : float. Dataset keyword
assumed thickness of the melting layer. Default 700.
- fzl : float. Dataset keyword
The default freezing level height. It will be used if no temperature field name is specified or the temperature field is not in the radar object. Default 2000.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_roi(procstatus, dscfg, radar_list=None)[source]¶ Obtains the radar data at a region of interest defined by a TRT file or by the user.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- trtfile : str. Dataset keyword
TRT file from which to extract the region of interest
- lon_roi, lat_roi : float array. Dataset keyword
latitude and longitude positions defining a region of interest
- alt_min, alt_max : float. Dataset keyword
Minimum and maximum altitude of the region of interest. Can be None
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and metadata at the point of interest
- ind_rad : int
radar index
-
pyrad.proc.process_rqvp(procstatus, dscfg, radar_list=None)[source]¶ Computes range defined quasi vertical profiles, by averaging over height levels PPI data.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- hmax : float
The maximum height to plot [m]. Default 10000.
- hres : float
The height resolution [m]. Default 2.
- avg_type : str
The type of averaging to perform. Can be either “mean” or “median” Default “mean”
- nvalid_min : int
Minimum number of valid points to accept average. Default 30.
- interp_kind : str
type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. Default ‘nearest’ ‘none’ will select from all data points within the regular grid height bin the closest to the center of the bin. ‘nearest’ will select the closest data point to the center of the height bin regardless if it is within the height bin or not. Data points can be masked values If another type of interpolation is selected masked values will be eliminated from the data points before the interpolation
- rmax : float
ground range up to which the data is intended for use [m]. Default 50000.
- weight_power : float
Power p of the weighting function 1/abs(grng-(rmax-1))**p given to the data outside the desired range. -1 will set the weight to 0. Default 2.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the QVP and a keyboard stating whether the processing has finished or not.
- ind_rad : int
radar index
-
pyrad.proc.process_save_radar(procstatus, dscfg, radar_list=None)[source]¶ Dummy function that allows to save the entire radar object
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_selfconsistency_bias(procstatus, dscfg, radar_list=None)[source]¶ Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- parametrization : str
The type of parametrization for the self-consistency curves. Can be ‘None’, ‘Gourley’, ‘Wolfensberger’, ‘Louf’, ‘Gorgucci’ or ‘Vaccarono’ ‘None’ will use tables from config files. Default ‘None’.
- fzl : float. Dataset keyword
Default freezing level height. Default 2000.
- rsmooth : float. Dataset keyword
length of the smoothing window [m]. Default 2000.
- min_rhohv : float. Dataset keyword
minimum valid RhoHV. Default 0.92
- filter_rain : Bool. Dataset keyword
If True the hydrometeor classification is used to filter out gates that are not rain. Default True
- max_phidp : float. Dataset keyword
maximum valid PhiDP [deg]. Default 20.
- ml_thickness : float. Dataset keyword
Melting layer thickness [m]. Default 700.
- rcell : float. Dataset keyword
length of continuous precipitation to consider the precipitation cell a valid phidp segment [m]. Default 15000.
- dphidp_min : float. Dataset keyword
minimum phase shift [deg]. Default 2.
- dphidp_max : float. Dataset keyword
maximum phase shift [deg]. Default 16.
- frequency : float. Dataset keyword
the radar frequency [Hz]. If None that of the key frequency in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the selfconsistency will not be computed
- check_wet_radome : Bool. Dataset keyword
if True the average reflectivity of the closest gates to the radar is going to be check to find out whether there is rain over the radome. If there is rain no bias will be computed. Default True.
- wet_radome_refl : Float. Dataset keyword
Average reflectivity [dBZ] of the gates close to the radar to consider the radome as wet. Default 25.
- wet_radome_rng_min, wet_radome_rng_max : Float. Dataset keyword
Min and max range [m] of the disk around the radar used to compute the average reflectivity to determine whether the radome is wet. Default 2000 and 4000.
- wet_radome_ngates_min : int
Minimum number of valid gates to consider that the radome is wet. Default 180
- valid_gates_only : Bool
If True the reflectivity bias obtained for each valid ray is going to be assigned only to gates of the segment used. That will give more weight to longer segments when computing the total bias. Default False
- keep_points : Bool
If True the ZDR, ZH and KDP of the gates used in the self- consistency algorithm are going to be stored for further analysis. Default False
- rkdp : float
The length of the window used to compute KDP with the single window least square method [m]. Default 6000.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_selfconsistency_bias2(procstatus, dscfg, radar_list=None)[source]¶ Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- parametrization : str
The type of parametrization for the self-consistency curves. Can be ‘None’, ‘Gourley’, ‘Wolfensberger’, ‘Louf’, ‘Gorgucci’ or ‘Vaccarono’ ‘None’ will use tables from config files. Default ‘None’.
- fzl : float. Dataset keyword
Default freezing level height. Default 2000.
- rsmooth : float. Dataset keyword
length of the smoothing window [m]. Default 2000.
- min_rhohv : float. Dataset keyword
minimum valid RhoHV. Default 0.92
- filter_rain : Bool. Dataset keyword
If True the hydrometeor classification is used to filter out gates that are not rain. Default True
- max_phidp : float. Dataset keyword
maximum valid PhiDP [deg]. Default 20.
- ml_thickness : float. Dataset keyword
Melting layer thickness [m]. Default 700.
- rcell : float. Dataset keyword
length of continuous precipitation to consider the precipitation cell a valid phidp segment [m]. Default 15000.
- frequency : float. Dataset keyword
the radar frequency [Hz]. If None that of the key frequency in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the selfconsistency will not be computed
- check_wet_radome : Bool. Dataset keyword
if True the average reflectivity of the closest gates to the radar is going to be check to find out whether there is rain over the radome. If there is rain no bias will be computed. Default True.
- wet_radome_refl : Float. Dataset keyword
Average reflectivity [dBZ] of the gates close to the radar to consider the radome as wet. Default 25.
- wet_radome_rng_min, wet_radome_rng_max : Float. Dataset keyword
Min and max range [m] of the disk around the radar used to compute the average reflectivity to determine whether the radome is wet. Default 2000 and 4000.
- wet_radome_ngates_min : int
Minimum number of valid gates to consider that the radome is wet. Default 180
- keep_points : Bool
If True the ZDR, ZH and KDP of the gates used in the self- consistency algorithm are going to be stored for further analysis. Default False
- bias_per_gate : Bool
If True the bias per gate will be computed
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_selfconsistency_kdp_phidp(procstatus, dscfg, radar_list=None)[source]¶ Computes specific differential phase and differential phase in rain using the selfconsistency between Zdr, Zh and KDP
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of strings. Dataset keyword
The input data types
- parametrization : str
The type of parametrization for the self-consistency curves. Can be ‘None’, ‘Gourley’, ‘Wolfensberger’, ‘Louf’, ‘Gorgucci’ or ‘Vaccarono’ ‘None’ will use tables from config files. Default ‘None’.
- rsmooth : float. Dataset keyword
length of the smoothing window [m]. Default 2000.
- min_rhohv : float. Dataset keyword
minimum valid RhoHV. Default 0.92
- filter_rain : Bool. Dataset keyword
If True the hydrometeor classification is used to filter out gates that are not rain. Default True
- max_phidp : float. Dataset keyword
maximum valid PhiDP [deg]. Default 20.
- ml_thickness : float. Dataset keyword
assumed melting layer thickness [m]. Default 700.
- fzl : float. Dataset keyword
The default freezing level height. It will be used if no temperature field name is specified or the temperature field is not in the radar object. Default 2000.
- frequency : float. Dataset keyword
the radar frequency [Hz]. If None that of the key frequency in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the selfconsistency will not be computed
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_signal_power(procstatus, dscfg, radar_list=None)[source]¶ Computes the signal power in dBm
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- mflossh, mflossv : float. Dataset keyword
The matching filter losses of the horizontal (vertical) channel [dB]. If None it will be obtained from the attribute radar_calibration of the radar object. Defaults to 0
- radconsth, radconstv : float. Dataset keyword
The horizontal (vertical) channel radar constant. If None it will be obtained from the attribute radar_calibration of the radar object
- lrxh, lrxv : float. Global keyword
The horizontal (vertical) receiver losses from the antenna feed to the reference point. [dB] positive value. Default 0
- lradomeh, lradomev : float. Global keyword
The 1-way dry radome horizontal (vertical) channel losses. [dB] positive value. Default 0.
- attg : float. Dataset keyword
The gas attenuation [dB/km]. If none it will be obtained from the attribute radar_calibration of the radar object or assigned according to the radar frequency. Defaults to 0.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_smooth_phidp_double_window(procstatus, dscfg, radar_list=None)[source]¶ corrects phidp of the system phase and smoothes it using one window
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
The minimum range where to look for valid data [m]
- rmax : float. Dataset keyword
The maximum range where to look for valid data [m]
- rcell : float. Dataset keyword
The length of a continuous cell to consider it valid precip [m]
- rwinds : float. Dataset keyword
The length of the short smoothing window [m]
- rwindl : float. Dataset keyword
The length of the long smoothing window [m]
- Zmin : float. Dataset keyword
The minimum reflectivity [dBZ]
- Zmax : float. Dataset keyword
The maximum reflectivity [dBZ]
- Zthr : float. Dataset keyword
The threshold defining wich smoothed data to used [dBZ]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_smooth_phidp_single_window(procstatus, dscfg, radar_list=None)[source]¶ corrects phidp of the system phase and smoothes it using one window
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
The minimum range where to look for valid data [m]
- rmax : float. Dataset keyword
The maximum range where to look for valid data [m]
- rcell : float. Dataset keyword
The length of a continuous cell to consider it valid precip [m]
- rwind : float. Dataset keyword
The length of the smoothing window [m]
- Zmin : float. Dataset keyword
The minimum reflectivity [dBZ]
- Zmax : float. Dataset keyword
The maximum reflectivity [dBZ]
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_snr(procstatus, dscfg, radar_list=None)[source]¶ Computes SNR
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- output_type : string. Dataset keyword
The output data type. Either SNRh or SNRv
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectra_ang_avg(procstatus, dscfg, radar_list=None)[source]¶ Function to average the spectra over the rays. This function is intended mainly for vertically pointing scans. The function assumes the volume is composed of a single sweep, it averages over the number of rays specified by the user and produces a single ray output.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- navg : int
Number of spectra to average. If -1 all spectra will be averaged. Default -1.
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectra_point(procstatus, dscfg, radar_list=None)[source]¶ Obtains the spectra or IQ data at a point location.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- single_point : boolean. Dataset keyword
if True only one gate per radar volume is going to be kept. Otherwise all gates within the azimuth and elevation tolerance are going to be kept. This is useful to extract all data from fixed pointing scans. Default True
- latlon : boolean. Dataset keyword
if True position is obtained from latitude, longitude information, otherwise position is obtained from antenna coordinates (range, azimuth, elevation). Default False
- truealt : boolean. Dataset keyword
if True the user input altitude is used to determine the point of interest. if False use the altitude at a given radar elevation ele over the point of interest. Default True
- lon : float. Dataset keyword
the longitude [deg]. Use when latlon is True.
- lat : float. Dataset keyword
the latitude [deg]. Use when latlon is True.
- alt : float. Dataset keyword
altitude [m MSL]. Use when latlon is True. Default 0.
- ele : float. Dataset keyword
radar elevation [deg]. Use when latlon is False or when latlon is True and truealt is False
- azi : float. Dataset keyword
radar azimuth [deg]. Use when latlon is False
- rng : float. Dataset keyword
range from radar [m]. Use when latlon is False
- AziTol : float. Dataset keyword
azimuthal tolerance to determine which radar azimuth to use [deg]. Default 0.5
- EleTol : float. Dataset keyword
elevation tolerance to determine which radar elevation to use [deg]. Default 0.5
- RngTol : float. Dataset keyword
range tolerance to determine which radar bin to use [m]. Default 50.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and metadata at the point of interest
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_differential_phase(procstatus, dscfg, radar_list=None)[source]¶ Computes the spectral differential phase
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_differential_reflectivity(procstatus, dscfg, radar_list=None)[source]¶ Computes spectral differential reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- smooth_window : int or None
Size of the moving Gaussian smoothing window. If none no smoothing will be applied
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_noise(procstatus, dscfg, radar_list=None)[source]¶ Computes the spectral noise
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- units : str
The units of the returned signal. Can be ‘ADU’, ‘dBADU’ or ‘dBm’
- navg : int
Number of spectra averaged
- rmin : int
Range from which the data is used to estimate the noise
- nnoise_min : int
Minimum number of samples to consider the estimated noise power valid
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_phase(procstatus, dscfg, radar_list=None)[source]¶ Computes the spectral phase
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_power(procstatus, dscfg, radar_list=None)[source]¶ Computes the spectral power
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- units : str
The units of the returned signal. Can be ‘ADU’, ‘dBADU’ or ‘dBm’
- subtract_noise : Bool
If True noise will be subtracted from the signal
- smooth_window : int or None
Size of the moving Gaussian smoothing window. If none no smoothing will be applied
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_reflectivity(procstatus, dscfg, radar_list=None)[source]¶ Computes spectral reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- smooth_window : int or None
Size of the moving Gaussian smoothing window. If none no smoothing will be applied
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_spectral_rhohv(procstatus, dscfg, radar_list=None)[source]¶ Computes the spectral RhoHV
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- subtract_noise : Bool
If True noise will be subtracted from the signal
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_st1_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes the statistical test one lag fluctuation from the horizontal or vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_st2_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes the statistical test two lag fluctuation from the horizontal or vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_sun_hits(procstatus, dscfg, radar_list=None)[source]¶ monitoring of the radar using sun hits
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- delev_max : float. Dataset keyword
maximum elevation distance from nominal radar elevation where to look for a sun hit signal [deg]. Default 1.5
- dazim_max : float. Dataset keyword
maximum azimuth distance from nominal radar elevation where to look for a sun hit signal [deg]. Default 1.5
- elmin : float. Dataset keyword
minimum radar elevation where to look for sun hits [deg]. Default 1.
- attg : float. Dataset keyword
gaseous attenuation. Default None
- sun_position : string. Datset keyword
The function to compute the sun position to use. Can be ‘MF’ or ‘pysolar’
- sun_hit_method : str. Dataset keyword
Method used to estimate the power of the sun hit. Can be HS (Hildebrand and Sekhon 1974) or Ivic (Ivic 2013)
- rmin : float. Dataset keyword
minimum range where to look for a sun hit signal [m]. Used in HS method. Default 50000.
- hmin : float. Dataset keyword
minimum altitude where to look for a sun hit signal [m MSL]. Default 10000. The actual range from which a sun hit signal will be search will be the minimum between rmin and the range from which the altitude is higher than hmin. Used in HS method. Default 10000.
- nbins_min : int. Dataset keyword.
minimum number of range bins that have to contain signal to consider the ray a potential sun hit. Default 20 for HS and 8000 for Ivic.
- npulses_ray : int
Default number of pulses used in the computation of the ray. If the number of pulses is not in radar.instrument_parameters this will be used instead. Used in Ivic method. Default 30
- iterations: int
number of iterations in step 7 of Ivic method. Default 10.
- max_std_pwr : float. Dataset keyword
maximum standard deviation of the signal power to consider the data a sun hit [dB]. Default 2. Used in HS method
- max_std_zdr : float. Dataset keyword
maximum standard deviation of the ZDR to consider the data a sun hit [dB]. Default 2.
- az_width_co : float. Dataset keyword
co-polar antenna azimuth width (convoluted with sun width) [deg]. Default None
- el_width_co : float. Dataset keyword
co-polar antenna elevation width (convoluted with sun width) [deg]. Default None
- az_width_cross : float. Dataset keyword
cross-polar antenna azimuth width (convoluted with sun width) [deg]. Default None
- el_width_cross : float. Dataset keyword
cross-polar antenna elevation width (convoluted with sun width) [deg]. Default None
- ndays : int. Dataset keyword
number of days used in sun retrieval. Default 1
- coeff_band : float. Dataset keyword
multiplicate coefficient to transform pulse width into receiver bandwidth
- frequency : float. Dataset keyword
the radar frequency [Hz]. If None that of the key frequency in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present frequency dependent parameters will not be computed
- beamwidth : float. Dataset keyword
the antenna beamwidth [deg]. If None that of the keys radar_beam_width_h or radar_beam_width_v in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the beamwidth dependent parameters will not be computed
- pulse_width : float. Dataset keyword
the pulse width [s]. If None that of the key pulse_width in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the pulse width dependent parameters will not be computed
- ray_angle_res : float. Dataset keyword
the ray angle resolution [deg]. If None that of the key ray_angle_res in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the ray angle resolution parameters will not be computed
- AntennaGainH, AntennaGainV : float. Dataset keyword
the horizontal (vertical) polarization antenna gain [dB]. If None that of the attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the ray angle resolution parameters will not be computed
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - sun_hits_dict : dict
dictionary containing a radar object, a sun_hits dict and a sun_retrieval dictionary
- ind_rad : int
radar index
-
pyrad.proc.process_svp(procstatus, dscfg, radar_list=None)[source]¶ Computes slanted vertical profiles, by averaging over height levels PPI data.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- angle : int or float
If the radar object contains a PPI volume, the sweep number to use, if it contains an RHI volume the elevation angle. Default 0.
- ang_tol : float
If the radar object contains an RHI volume, the tolerance in the elevation angle for the conversion into PPI. Default 1.
- lat, lon : float
latitude and longitude of the point of interest [deg]
- latlon_tol : float
tolerance in latitude and longitude in deg. Default 0.0005
- delta_rng, delta_azi : float
maximum range distance [m] and azimuth distance [degree] from the central point of the svp containing data to average. Default 5000. and 10.
- hmax : float
The maximum height to plot [m]. Default 10000.
- hres : float
The height resolution [m]. Default 250.
- avg_type : str
The type of averaging to perform. Can be either “mean” or “median” Default “mean”
- nvalid_min : int
Minimum number of valid points to consider the data valid when performing the averaging. Default 1
- interp_kind : str
type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. Default ‘none’ ‘none’ will select from all data points within the regular grid height bin the closest to the center of the bin. ‘nearest’ will select the closest data point to the center of the height bin regardless if it is within the height bin or not. Data points can be masked values If another type of interpolation is selected masked values will be eliminated from the data points before the interpolation
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the svp and a keyboard stating whether the processing has finished or not.
- ind_rad : int
radar index
-
pyrad.proc.process_time_avg(procstatus, dscfg, radar_list=None)[source]¶ computes the temporal mean of a field
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- lin_trans: int. Dataset keyword
If 1 apply linear transformation before averaging
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_time_avg_flag(procstatus, dscfg, radar_list=None)[source]¶ computes a flag field describing the conditions of the data used while averaging
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- phidpmax: float. Dataset keyword
maximum PhiDP
- beamwidth : float. Dataset keyword
the antenna beamwidth [deg]. If None that of the keys radar_beam_width_h or radar_beam_width_v in attribute instrument_parameters of the radar object will be used. If the key or the attribute are not present the beamwidth will be set to None
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : Radar
radar object
- ind_rad : int
radar index
-
pyrad.proc.process_time_avg_std(procstatus, dscfg, radar_list=None)[source]¶ computes the average and standard deviation of data. It looks only for gates where data is present.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- regular_grid : Boolean. Dataset keyword
Whether the radar has a Boolean grid or not. Default False
- rmin, rmax : float. Dataset keyword
minimum and maximum ranges where the computation takes place. If -1 the whole range is considered. Default is -1
- val_min : Float. Dataset keyword
Minimum reflectivity value to consider that the gate has signal. Default None
- filter_prec : str. Dataset keyword
Give which type of volume should be filtered. None, no filtering; keep_wet, keep wet volumes; keep_dry, keep dry volumes.
- rmax_prec : float. Dataset keyword
Maximum range to consider when looking for wet gates [m]
- percent_prec_max : float. Dataset keyword
Maxim percentage of wet gates to consider the volume dry
- lin_trans : Boolean. Dataset keyword
If True the data will be transformed into linear units. Default False
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_time_height(procstatus, dscfg, radar_list=None)[source]¶ Produces time height radar objects at a point of interest defined by latitude and longitude. A time-height contains the evolution of the vertical structure of radar measurements above the location of interest.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the point measurement
- lat, lon : float
latitude and longitude of the point of interest [deg]
- latlon_tol : float
tolerance in latitude and longitude in deg. Default 0.0005
- hmax : float
The maximum height to plot [m]. Default 10000.
- hres : float
The height resolution [m]. Default 50
- interp_kind : str
type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. Default ‘none’ ‘none’ will select from all data points within the regular grid height bin the closest to the center of the bin. ‘nearest’ will select the closest data point to the center of the height bin regardless if it is within the height bin or not. Data points can be masked values If another type of interpolation is selected masked values will be eliminated from the data points before the interpolation
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the QVP and a keyboard stating whether the processing has finished or not.
- ind_rad : int
radar index
-
pyrad.proc.process_time_stats(procstatus, dscfg, radar_list=None)[source]¶ computes the temporal statistics of a field
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be performed over the entire data. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- lin_trans: int. Dataset keyword
If 1 apply linear transformation before averaging
- use_nan : bool. Dataset keyword
If true non valid data will be used
- nan_value : float. Dataset keyword
The value of the non valid data. Default 0
- stat: string. Dataset keyword
Statistic to compute: Can be mean, std, cov, min, max. Default mean
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_time_stats2(procstatus, dscfg, radar_list=None)[source]¶ computes the temporal mean of a field
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. If -1 the statistics are going to be performed over the entire data. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- stat: string. Dataset keyword
Statistic to compute: Can be median, mode, percentileXX
- use_nan : bool. Dataset keyword
If true non valid data will be used
- nan_value : float. Dataset keyword
The value of the non valid data. Default 0
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_traj_antenna_pattern(procstatus, dscfg, radar_list=None, trajectory=None)[source]¶ Process a new array of data volumes considering a plane trajectory. As result a timeseries with the values transposed for a given antenna pattern is created. The result is created when the LAST flag is set.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
- datatype : list of string. Dataset keyword
The input data types
- antennaType : str. Dataset keyword
Type of antenna of the radar we want to get the view from. Can be AZIMUTH, ELEVATION, LOWBEAM, HIGHBEAM
- par_azimuth_antenna : dict. Global ekyword
Dictionary containing the parameters of the PAR azimuth antenna, i.e. name of the file with the antenna elevation pattern and fixed antenna angle
- par_elevation_antenna : dict. Global keyword
Dictionary containing the parameters of the PAR elevation antenna, i.e. name of the file with the antenna azimuth pattern and fixed antenna angle
- asr_lowbeam_antenna : dict. Global keyword
Dictionary containing the parameters of the ASR low beam antenna, i.e. name of the file with the antenna elevation pattern and fixed antenna angle
- asr_highbeam_antenna : dict. Global keyword
Dictionary containing the parameters of the ASR high beam antenna, i.e. name of the file with the antenna elevation pattern and fixed antenna angle
- target_radar_pos : dict. Global keyword
Dictionary containing the latitude, longitude and altitude of the radar we want to get the view from. If not specifying it will assume the radar is collocated
- range_all : Bool. Dataset keyword
If the real radar and the synthetic radar are co-located and this parameter is true the statistics are going to be computed using all the data from range 0 to the position of the plane. Default False
- rhi_resolution : Bool. Dataset keyword
Resolution of the synthetic RHI used to compute the data as viewed from the synthetic radar [deg]. Default 0.5
- max_altitude : float. Dataset keyword
Max altitude of the data to use when computing the view from the synthetic radar [m MSL]. Default 12000.
- latlon_tol : float. Dataset keyword
The tolerance in latitude and longitude to determine which synthetic radar gates are co-located with real radar gates [deg]. Default 0.04
- alt_tol : float. Datset keyword
The tolerance in altitude to determine which synthetic radar gates are co-located with real radar gates [m]. Default 1000.
- distance_upper_bound : float. Dataset keyword
The maximum distance where to look for a neighbour when determining which synthetic radar gates are co-located with real radar gates [m]. Default 1000.
- use_cKDTree : Bool. Dataset keyword
Which function to use to find co-located real radar gates with the synthetic radar. If True a function using cKDTree from scipy.spatial is used. This function uses parameter distance_upper_bound. If False a native implementation is used that takes as parameters latlon_tol and alt_tol. Default True.
- pattern_thres : float. Dataset keyword
The minimum of the sum of the weights given to each value in order to consider the weighted quantile valid. It is related to the number of valid data points
- data_is_log : dict. Dataset keyword
Dictionary specifying for each field if it is in log (True) or linear units (False). Default False
- use_nans : dict. Dataset keyword
Dictionary specyfing whether the nans have to be used in the computation of the statistics for each field. Default False
- nan_value : dict. Dataset keyword
Dictionary with the value to use to substitute the NaN values when computing the statistics of each field. Default 0
- radar_list : list of Radar objects
Optional. list of radar objects
- trajectory : Trajectory object
containing trajectory samples
Returns: - trajectory : Trajectory object
Object holding time series
- ind_rad : int
radar index
-
pyrad.proc.process_traj_atplane(procstatus, dscfg, radar_list=None, trajectory=None)[source]¶ Return time series according to trajectory
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- data_is_log : dict. Dataset keyword
Dictionary specifying for each field if it is in log (True) or linear units (False). Default False
- ang_tol : float. Dataset keyword
Factor that multiplies the angle resolution. Used when determining the neighbouring rays. Default 1.2
- radar_list : list of Radar objects
Optional. list of radar objects
- trajectory : Trajectory object
containing trajectory samples
Returns: - trajectory : Trajectory object
Object holding time series
- ind_rad : int
radar index
-
pyrad.proc.process_traj_lightning(procstatus, dscfg, radar_list=None, trajectory=None)[source]¶ Return time series according to lightning trajectory
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- data_is_log : dict. Dataset keyword
Dictionary specifying for each field if it is in log (True) or linear units (False). Default False
- ang_tol : float. Dataset keyword
Factor that multiplies the angle resolution. Used when determining the neighbouring rays. Default 1.2
- radar_list : list of Radar objects
Optional. list of radar objects
- trajectory : Trajectory object
containing trajectory samples
Returns: - trajectory : Trajectory object
Object holding time series
- ind_rad : int
radar index
-
pyrad.proc.process_traj_trt(procstatus, dscfg, radar_list=None, trajectory=None)[source]¶ Processes data according to TRT trajectory
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- time_tol : float. Dataset keyword
tolerance between reference time of the radar volume and that of the TRT cell [s]. Default 100.
- alt_min, alt_max : float. Dataset keyword
Minimum and maximum altitude of the data inside the TRT cell to retrieve [m MSL]. Default None
- cell_center : Bool. Dataset keyword
If True only the range gate closest to the center of the cell is extracted. Default False
- latlon_tol : Float. Dataset keyword
Tolerance in lat/lon when extracting data only from the center of the TRT cell. Default 0.01
- radar_list : list of Radar objects
Optional. list of radar objects
- trajectory : Trajectory object
containing trajectory samples
Returns: - new_dataset : dictionary
Dictionary containing radar_out, a radar object containing only data from inside the TRT cell
- ind_rad : int
radar index
-
pyrad.proc.process_traj_trt_contour(procstatus, dscfg, radar_list=None, trajectory=None)[source]¶ Gets the TRT cell contour corresponding to each radar volume
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- time_tol : float. Dataset keyword
tolerance between reference time of the radar volume and that of the TRT cell [s]. Default 100.
- radar_list : list of Radar objects
Optional. list of radar objects
- trajectory : Trajectory object
containing trajectory samples
Returns: - new_dataset : dict
Dictionary containing radar_out and roi_dict. Radar out is the current radar object. roi_dict contains the positions defining the TRT cell contour
- ind_rad : int
radar index
-
pyrad.proc.process_trajectory(procstatus, dscfg, radar_list=None, trajectory=None)[source]¶ Return trajectory
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
- trajectory : Trajectory object
containing trajectory samples
Returns: - new_dataset : Trajectory object
radar object
- ind_rad : int
None
-
pyrad.proc.process_ts_along_coord(procstatus, dscfg, radar_list=None)[source]¶ Produces time series along a particular antenna coordinate
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The data type where we want to extract the time series
- mode : str
coordinate to extract data along. Can be ALONG_AZI, ALONG_ELE or ALONG_RNG
- fixed_range, fixed_azimuth, fixed_elevation : float
The fixed range [m], azimuth [deg] or elevation [deg] to extract. In each mode two of these parameters have to be defined. If they are not defined they default to 0.
- ang_tol, rng_tol : float
The angle tolerance [deg] and range tolerance [m] around the fixed range or azimuth/elevation
- value_start, value_stop : float
The minimum and maximum value at which the data along a coordinate start and stop
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the data and a keyboard stating whether the processing has finished or not.
- ind_rad : int
radar index
-
pyrad.proc.process_turbulence(procstatus, dscfg, radar_list=None)[source]¶ Computes turbulence from the Doppler spectrum width and reflectivity using the PyTDA package
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- radius : float. Dataset keyword
Search radius for calculating Eddy Dissipation Rate (EDR). Default 2
- split_cut : Bool. Dataset keyword
Set to True for split-cut volumes. Default False
- max_split_cut : Int. Dataset keyword
Total number of tilts that are affected by split cuts. Only relevant if split_cut=True. Default 2
- xran, yran : float array. Dataset keyword
Spatial range in X,Y to consider. Default [-100, 100] for both X and Y
- beamwidth : Float. Dataset keyword
Radar beamwidth. Default None. If None it will be obtained from the radar object metadata. If cannot be obtained defaults to 1 deg.
- compute_gate_pos : Bool. Dataset keyword
If True the gate position is going to be computed in PyTDA. Otherwise the position from the radar object is used. Default False
- verbose : Bool. Dataset keyword
True for verbose output. Default False
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_vad(procstatus, dscfg, radar_list=None)[source]¶ Estimates vertical wind profile using the VAD (velocity Azimuth Display) technique
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_visibility(procstatus, dscfg, radar_list=None)[source]¶ Gets the visibility in percentage from the minimum visible elevation. Anything with elevation lower than the minimum visible elevation plus and offset is set to 0 while above is set to 100.
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
arbitrary data type
- offset : float. Dataset keyword
The offset above the minimum visibility that must be filtered
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_vol_refl(procstatus, dscfg, radar_list=None)[source]¶ Computes the volumetric reflectivity in 10log10(cm^2 km^-3)
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- freq : float. Dataset keyword
The radar frequency
- kw : float. Dataset keyword
The water constant
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_wbn_iq(procstatus, dscfg, radar_list=None)[source]¶ Computes the wide band noise from the horizontal or vertical IQ data
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted configuration keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of spectra objects
Optional. list of spectra objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_weighted_time_avg(procstatus, dscfg, radar_list=None)[source]¶ computes the temporal mean of a field weighted by the reflectivity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- period : float. Dataset keyword
the period to average [s]. Default 3600.
- start_average : float. Dataset keyword
when to start the average [s from midnight UTC]. Default 0.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : Radar
radar object
- ind_rad : int
radar index
-
pyrad.proc.process_wind_vel(procstatus, dscfg, radar_list=None)[source]¶ Estimates the horizontal or vertical component of the wind from the radial velocity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- vert_proj : Boolean
If true the vertical projection is computed. Otherwise the horizontal projection is computed
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_windshear(procstatus, dscfg, radar_list=None)[source]¶ Estimates the wind shear from the wind velocity
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : string. Dataset keyword
The input data type
- az_tol : float
The tolerance in azimuth when looking for gates on top of the gate when computation is performed
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_zdr_column(procstatus, dscfg, radar_list=None)[source]¶ Detects ZDR columns
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_zdr_precip(procstatus, dscfg, radar_list=None)[source]¶ Keeps only suitable data to evaluate the differential reflectivity in moderate rain or precipitation (for vertical scans)
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- ml_filter : boolean. Dataset keyword
indicates if a filter on data in and above the melting layer is applied. Default True.
- rmin : float. Dataset keyword
minimum range where to look for rain [m]. Default 1000.
- rmax : float. Dataset keyword
maximum range where to look for rain [m]. Default 50000.
- Zmin : float. Dataset keyword
minimum reflectivity to consider the bin as precipitation [dBZ]. Default 20.
- Zmax : float. Dataset keyword
maximum reflectivity to consider the bin as precipitation [dBZ] Default 22.
- RhoHVmin : float. Dataset keyword
minimum RhoHV to consider the bin as precipitation Default 0.97
- PhiDPmax : float. Dataset keyword
maximum PhiDP to consider the bin as precipitation [deg] Default 10.
- elmax : float. Dataset keyword
maximum elevation angle where to look for precipitation [deg] Default None.
- ml_thickness : float. Dataset keyword
assumed thickness of the melting layer. Default 700.
- fzl : float. Dataset keyword
The default freezing level height. It will be used if no temperature field name is specified or the temperature field is not in the radar object. Default 2000.
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index
-
pyrad.proc.process_zdr_snow(procstatus, dscfg, radar_list=None)[source]¶ Keeps only suitable data to evaluate the differential reflectivity in snow
Parameters: - procstatus : int
Processing status: 0 initializing, 1 processing volume, 2 post-processing
- dscfg : dictionary of dictionaries
data set configuration. Accepted Configuration Keywords:
- datatype : list of string. Dataset keyword
The input data types
- rmin : float. Dataset keyword
minimum range where to look for rain [m]. Default 1000.
- rmax : float. Dataset keyword
maximum range where to look for rain [m]. Default 50000.
- Zmin : float. Dataset keyword
minimum reflectivity to consider the bin as snow [dBZ]. Default 0.
- Zmax : float. Dataset keyword
maximum reflectivity to consider the bin as snow [dBZ] Default 30.
- SNRmin : float. Dataset keyword
minimum SNR to consider the bin as snow [dB]. Default 10.
- SNRmax : float. Dataset keyword
maximum SNR to consider the bin as snow [dB] Default 50.
- RhoHVmin : float. Dataset keyword
minimum RhoHV to consider the bin as snow Default 0.97
- PhiDPmax : float. Dataset keyword
maximum PhiDP to consider the bin as snow [deg] Default 10.
- elmax : float. Dataset keyword
maximum elevation angle where to look for snow [deg] Default None.
- KDPmax : float. Dataset keyword
maximum KDP to consider the bin as snow [deg] Default None
- TEMPmin : float. Dataset keyword
minimum temperature to consider the bin as snow [deg C]. Default None
- TEMPmax : float. Dataset keyword
maximum temperature to consider the bin as snow [deg C] Default None
- hydroclass : list of ints. Dataset keyword
list of hydrometeor classes to keep for the analysis Default [2] (dry snow)
- radar_list : list of Radar objects
Optional. list of radar objects
Returns: - new_dataset : dict
dictionary containing the output
- ind_rad : int
radar index