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Radar Retrievals (pyart.retrieve)

Radar retrievals.

Radar retrievals

kdp_maesaka(radar[, gatefilter, method, …])	Compute the specific differential phase (KDP) from corrected (e.g., unfolded) total differential phase data based on the variational method outlined in Maesaka et al.
kdp_schneebeli(radar[, gatefilter, …])	Estimates Kdp with the Kalman filter method by Schneebeli and al.
kdp_vulpiani(radar[, gatefilter, …])	Estimates Kdp with the Vulpiani method for a 2D array of psidp measurements with the first dimension being the distance from radar and the second dimension being the angles (azimuths for PPI, elev for RHI).The input psidp is assumed to be pre-filtered (for ex.
kdp_leastsquare_single_window(radar[, …])	Compute the specific differential phase (KDP) from differential phase data using a piecewise least square method.
kdp_leastsquare_double_window(radar[, …])	Compute the specific differential phase (KDP) from differential phase data using a piecewise least square method.
calculate_snr_from_reflectivity(radar[, …])	Calculate the signal to noise ratio, in dB, from the reflectivity field.
calculate_velocity_texture(radar[, …])	Derive the texture of the velocity field.
compute_ccor(radar[, filt_field, …])	Computes the clutter correction ratio (CCOR), i.e.
compute_snr(radar[, refl_field, …])	Computes SNR from a reflectivity field and the noise in dBZ.
compute_l(radar[, rhohv_field, l_field])	Computes Rhohv in logarithmic scale according to L=-log10(1-RhoHV).
compute_cdr(radar[, rhohv_field, zdr_field, …])	Computes the Circular Depolarization Ratio.
compute_noisedBZ(nrays, noisedBZ_val, …[, …])	Computes noise in dBZ from reference noise value.
compute_radial_noise_hs(radar[, ind_rmin, …])	Computes radial noise in dBm from signal power using the algorithm from Hildebrand and Sekhon 1974
compute_radial_noise_ivic(radar[, …])	Computes radial noise in dBm from signal power using the algorithm described in Ivic et al.
compute_signal_power(radar[, lmf, attg, …])	Computes received signal power OUTSIDE THE RADOME in dBm from a reflectivity field.
compute_rcs(radar[, kw2, pulse_width, …])	Computes the radar cross-section (assuming a point target) from radar reflectivity.
compute_rcs_from_pr(radar[, lmf, attg, …])	Computes the radar cross-section (assuming a point target) from radar reflectivity by first computing the received power and then the RCS from it.
compute_vol_refl(radar[, kw, freq, …])	Computes the volumetric reflectivity from the effective reflectivity factor
compute_bird_density(radar[, sigma_bird, …])	Computes the bird density from the volumetric reflectivity
fetch_radar_time_profile(sonde_dset, radar)	Extract the correct profile from a interpolated sonde.
map_profile_to_gates(profile, heights, radar)	Given a profile of a variable map it to the gates of radar assuming 4/3Re.
steiner_conv_strat(grid[, dx, dy, intense, …])	Partition reflectivity into convective-stratiform using the Steiner et al.
hydroclass_semisupervised(radar[, …])	Classifies precipitation echoes following the approach by Besic et al (2016).
get_freq_band(freq)	Returns the frequency band name (S, C, X, …).
texture_of_complex_phase(radar[, …])	Calculate the texture of the differential phase field.
grid_displacement_pc(grid1, grid2, field, level)	Calculate the grid displacement using phase correlation.
grid_shift(grid, advection[, trim_edges, …])	Shift a grid by a certain number of pixels.
est_rain_rate_zpoly(radar[, refl_field, …])	Estimates rainfall rate from reflectivity using a polynomial Z-R relation developed at McGill University.
est_rain_rate_z(radar[, alpha, beta, …])	Estimates rainfall rate from reflectivity using a power law.
est_rain_rate_kdp(radar[, alpha, beta, …])	Estimates rainfall rate from kdp using alpha power law.
est_rain_rate_a(radar[, alpha, beta, …])	Estimates rainfall rate from specific attenuation using alpha power law.
est_rain_rate_zkdp(radar[, alphaz, betaz, …])	Estimates rainfall rate from a blending of power law r-kdp and r-z relations.
est_rain_rate_za(radar[, alphaz, betaz, …])	Estimates rainfall rate from a blending of power law r-alpha and r-z relations.
est_rain_rate_hydro(radar[, alphazr, …])	Estimates rainfall rate using different relations between R and the polarimetric variables depending on the hydrometeor type.
est_wind_vel(radar[, vert_proj, vel_field, …])	Estimates wind velocity.
est_vertical_windshear(radar[, az_tol, …])	Estimates wind shear.
atmospheric_gas_att(freq, elev, rng)	Computes the one-way atmospheric gas attenuation [dB] according to the empirical formula in Doviak and Zrnic (1993) pp 44.
get_coeff_attg(freq)	get the 1-way gas attenuation for a particular frequency
est_wind_profile(radar[, npoints_min, …])	Estimates the vertical wind profile using VAD techniques
detect_ml(radar[, gatefilter, fill_value, …])	Detects the melting layer (ML) using the reflectivity and copolar correlation coefficient.
melting_layer_giangrande(radar[, nVol, …])	Detects the melting layer following the approach by Giangrande et al (2008)
melting_layer_hydroclass(radar[, …])	Using the results of the hydrometeor classification by Besic et al.
_get_res_vol_sides(radar)	Computes the height of the lower left and upper right points of the range resolution volume.
velocity_azimuth_display(radar[, vel_field, …])	Velocity azimuth display.
quasi_vertical_profile(radar[, …])	Quasi Vertical Profile.
compute_qvp(radar, field_names[, ref_time, …])	Computes quasi vertical profiles.
compute_rqvp(radar, field_names[, ref_time, …])	Computes range-defined quasi vertical profiles.
compute_evp(radar, field_names, lon, lat[, …])	Computes enhanced vertical profiles.
compute_svp(radar, field_names, lon, lat, angle)	Computes slanted vertical profiles.
compute_vp(radar, field_names, lon, lat[, …])	Computes vertical profiles.
compute_ts_along_coord(radar, field_name[, …])	Computes time series along a particular antenna coordinate, i.e.
compute_iq(spectra, fields_in_list, …[, …])	Computes the IQ data from the spectra through an inverse Fourier transform
compute_spectral_power(spectra[, units, …])	Computes the spectral power from the complex spectra in ADU.
compute_spectral_noise(spectra[, units, …])	Computes the spectral noise power from the complex spectra in ADU.
compute_spectral_phase(spectra[, signal_field])	Computes the spectral phase from the complex spectra in ADU
compute_spectral_reflectivity(spectra[, …])	Computes the spectral reflectivity from the complex spectra in ADU or from the signal power in ADU.
compute_spectral_differential_reflectivity(spectra)	Computes the spectral differential reflectivity from the complex spectras or the power in ADU
compute_spectral_differential_phase(spectra)	Computes the spectral differential reflectivity from the complex spectras in ADU or sRhoHV
compute_spectral_rhohv(spectra[, …])	Computes the spectral RhoHV from the complex spectras in ADU
compute_pol_variables(spectra, fields_list)	Computes the polarimetric variables from the complex spectra in ADU or the spectral powers and spectral RhoHV
compute_noise_power(spectra[, units, navg, …])	Computes the noise power from the complex spectra in ADU.
compute_reflectivity(spectra[, sdBZ_field])	Computes the reflectivity from the spectral reflectivity
compute_differential_reflectivity(spectra[, …])	Computes the differential reflectivity from the horizontal and vertical spectral reflectivity
compute_differential_phase(spectra[, …])	Computes the differential phase from the spectral differential phase and the spectral reflectivity
compute_rhohv(spectra[, use_rhohv, …])	Computes RhoHV from the horizontal and vertical spectral reflectivity or from sRhoHV and the spectral powers
compute_Doppler_velocity(spectra[, sdBZ_field])	Computes the Doppler velocity from the spectral reflectivity
compute_Doppler_width(spectra[, sdBZ_field])	Computes the Doppler width from the spectral reflectivity
compute_reflectivity_iq(radar[, …])	Computes the reflectivity from the IQ signal data
compute_st1_iq(radar[, signal_field])	Computes the statistical test one lag fluctuation from the horizontal or vertical channel IQ data
compute_st2_iq(radar[, signal_field])	Computes the statistical test two lag fluctuation from the horizontal or vertical channel IQ data
compute_wbn_iq(radar[, signal_field])	Computes the wide band noise from the horizontal or vertical channel IQ data
compute_differential_reflectivity_iq(radar)	Computes the differential reflectivity from the horizontal and vertical IQ data
compute_mean_phase_iq(radar[, signal_field])	Computes the differential phase from the horizontal or vertical channel IQ data
compute_differential_phase_iq(radar[, …])	Computes the differential phase from the horizontal and vertical channels IQ data
compute_rhohv_iq(radar[, subtract_noise, …])	Computes RhoHV from the horizontal and vertical channels IQ data
compute_Doppler_velocity_iq(radar[, …])	Computes the Doppler velocity from the IQ data
compute_Doppler_width_iq(radar[, …])	Computes the Doppler width from the IQ data
compute_pol_variables_iq(radar, fields_list)	Computes the polarimetric variables from the IQ signals in ADU
compute_spectra(radar, fields_in_list, …)	Computes the spectra from IQ data through a Fourier transform

pyart.retrieve.atmospheric_gas_att(freq, elev, rng)

Computes the one-way atmospheric gas attenuation [dB] according to the empirical formula in Doviak and Zrnic (1993) pp 44. This formula is valid for elev < 10 deg and rng < 200 km so values above these will be saturated to 10 deg and 200 km respectively

Parameters:

freq : float

radar frequency [Hz]

elev : float or array of floats

elevation angle [deg]

rng : float or array of floats. If array must have the same size as elev

range [km]

Returns:

latm : float or array of floats

1-way gas attenuation [dB]

pyart.retrieve.calculate_snr_from_reflectivity(radar, refl_field=None, snr_field=None, toa=25000.0)

Calculate the signal to noise ratio, in dB, from the reflectivity field.

Parameters:

radar : Radar

Radar object from which to retrieve reflectivity field.

refl_field : str, optional

Name of field in radar which contains the reflectivity. None will use the default field name in the Py-ART configuration file.

snr_field : str, optional

Name to use for snr metadata. None will use the default field name in the Py-ART configuration file.

toa : float, optional

Height above which to take noise floor measurements, in meters.

Returns:

snr : field dictionary

Field dictionary containing the signal to noise ratio.

pyart.retrieve.calculate_velocity_texture(radar, vel_field=None, wind_size=4, nyq=None, check_nyq_uniform=True)

Derive the texture of the velocity field.

Parameters:

radar: Radar

Radar object from which velocity texture field will be made.

vel_field : str, optional

Name of the velocity field. A value of None will force Py-ART to automatically determine the name of the velocity field.

wind_size : int, optional

The size of the window to calculate texture from. The window is defined to be a square of size wind_size by wind_size.

nyq : float, optional

The nyquist velocity of the radar. A value of None will force Py-ART to try and determine this automatically.

check_nyquist_uniform : bool, optional

True to check if the Nyquist velocities are uniform for all rays within a sweep, False will skip this check. This parameter is ignored when the nyq parameter is not None.

Returns:

vel_dict: dict

A dictionary containing the field entries for the radial velocity texture.

pyart.retrieve.compute_Doppler_velocity(spectra, sdBZ_field=None)

Computes the Doppler velocity from the spectral reflectivity

Parameters:

spectra : Radar spectra object

Object containing the required fields

sdBZ_field : str

Name of the field that contains the spectral reflectivity. None will use the default field name in the Py-ART configuration file.

Returns:

vel_dict : field dictionary

Field dictionary containing the Doppler velocity

pyart.retrieve.compute_Doppler_velocity_iq(radar, signal_field=None, direction='negative_away')

Computes the Doppler velocity from the IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

signal_field : str

Name of the field in the radar which contains the signal. None will use the default field name in the Py-ART configuration file.

direction : str

The convention used in the Doppler mean field. Can be negative_away or negative_towards

Returns:

vel_dict : field dictionary

Field dictionary containing the Doppler velocity

pyart.retrieve.compute_Doppler_width(spectra, sdBZ_field=None)

Computes the Doppler width from the spectral reflectivity

Parameters:

spectra : Radar spectra object

Object containing the required fields

sdBZ_field : str

Name of the field that contains the spectral reflectivity. None will use the default field name in the Py-ART configuration file.

Returns:

width_dict : field dictionary

Field dictionary containing the Doppler spectrum width

pyart.retrieve.compute_Doppler_width_iq(radar, subtract_noise=True, signal_field=None, noise_field=None, lag=1)

Computes the Doppler width from the IQ data

Parameters:

radar : Radar radar object

Object containing the required fields

subtract_noise : Bool

If True noise will be subtracted from the signals

lag : int

Time lag used in the denominator of the computation

signal_field, noise_field : str

Name of the field in the radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

width_dict : field dictionary

Field dictionary containing the Doppler spectrum width

pyart.retrieve.compute_bird_density(radar, sigma_bird=11, vol_refl_field=None, bird_density_field=None)

Computes the bird density from the volumetric reflectivity

Parameters:

radar : Radar

radar object

sigma_bird : float

Estimated bird radar cross-section

vol_refl_field : str

name of the volumetric reflectivity used for the calculations

bird_density_field : str

name of the bird density field

Returns:

bird_density_dict : dict

bird density data and metadata [birds/km^3]

pyart.retrieve.compute_ccor(radar, filt_field=None, unfilt_field=None, ccor_field=None)

Computes the clutter correction ratio (CCOR), i.e. the ratio between the signal without Doppler filtering and the signal with Doppler filtering

Parameters:

radar : Radar

Radar object

filt_field, unfilt_field : str

Name of Doppler filtered and unfiltered fields

ccor_field : str

Name of the CCOR field

Returns:

ccor_dict : field dictionary

Field dictionary containing the CCOR

pyart.retrieve.compute_cdr(radar, rhohv_field=None, zdr_field=None, cdr_field=None)

Computes the Circular Depolarization Ratio.

Parameters:

radar : Radar

Radar object.

rhohv_field : str, optional

Name of the RhoHV field.

zdr_field : str, optional

Name of the ZDR field.

cdr_field : str, optional

Name of the CDR field.

Returns:

cdr : dict

CDR field.

pyart.retrieve.compute_differential_phase(spectra, sdBZ_field=None, sPhiDP_field=None)

Computes the differential phase from the spectral differential phase and the spectral reflectivity

Parameters:

spectra : Radar spectra object

Object containing the required fields

sdBZ_field, sPhiDP_field : str

Name of the fields that contain the spectral reflectivity and the spectral differential phase. None will use the default field name in the Py-ART configuration file.

Returns:

PhiDP_dict : field dictionary

Field dictionary containing the differential phase

pyart.retrieve.compute_differential_phase_iq(radar, phase_offset=0.0, signal_h_field=None, signal_v_field=None)

Computes the differential phase from the horizontal and vertical channels IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

phase_offset : float

system phase offset to add

signal_h_field, signal_v_field : str

Name of the fields that contain the H and V IQ data. None will use the default field name in the Py-ART configuration file.

Returns:

phidp_dict : field dictionary

Field dictionary containing the differential phase

pyart.retrieve.compute_differential_reflectivity(spectra, sdBZ_field=None, sdBZv_field=None)

Computes the differential reflectivity from the horizontal and vertical spectral reflectivity

Parameters:

spectra : Radar spectra object

Object containing the required fields

sdBZ_field, sdBZv_field : str

Name of the fields that contain the spectral reflectivity. None will use the default field name in the Py-ART configuration file.

Returns:

ZDR_dict : field dictionary

Field dictionary containing the differential reflectivity

pyart.retrieve.compute_differential_reflectivity_iq(radar, subtract_noise=False, lag=0, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes the differential reflectivity from the horizontal and vertical IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

subtract_noise : Bool

If true the noise is subtracted from the power

lag : int

Time lag used to compute the differential reflectivity

signal_h_field, signal_v_field, noise_h_field, noise_v_field : str

Name of the signal and noise fields. None will use the default field name in the Py-ART configuration file.

Returns:

zdr_dict : field dictionary

Field dictionary containing the differential reflectivity

pyart.retrieve.compute_evp(radar, field_names, lon, lat, ref_time=None, latlon_tol=0.0005, delta_rng=15000.0, delta_azi=10, hmax=10000.0, hres=250.0, avg_type='mean', nvalid_min=1, interp_kind='none', qvp=None)

Computes enhanced vertical profiles.

Parameters:

radar : Radar

Radar object used.

field_names : list of str

list of field names to add to the QVP

lat, lon : float

latitude and longitude of the point of interest [deg]

ref_time : datetime object

reference time for current radar volume

latlon_tol : float

tolerance in latitude and longitude in deg.

delta_rng, delta_azi : float

maximum range distance [m] and azimuth distance [degree] from the central point of the evp containing data to average.

hmax : float

The maximum height to plot [m].

hres : float

The height resolution [m].

avg_type : str

The type of averaging to perform. Can be either “mean” or “median”

nvalid_min : int

Minimum number of valid points to accept average.

interp_kind : str

type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. ‘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

qvp : QVP object or None

If it is not None this is the QVP object where to store the data from the current time step. Otherwise a new QVP object will be created

Returns:

qvp : qvp object

The computed enhanced vertical profile

pyart.retrieve.compute_iq(spectra, fields_in_list, fields_out_list, window=None)

Computes the IQ data from the spectra through an inverse Fourier transform

Parameters:

spectra : Spectra radar object

Object containing the spectra

fields_in_list : list of str

list of input spectra fields names

fields_out_list : list of str

list with the output IQ fields names obtained from the input fields

window : string, tupple or None

Parameters of the window used to obtain the spectra. The parameters are the ones corresponding to function scipy.signal.windows.get_window. If it is not None the inverse will be used to multiply the IQ data obtained by the IFFT

Returns:

radar : IQ radar object

radar object containing the IQ fields

pyart.retrieve.compute_l(radar, rhohv_field=None, l_field=None)

Computes Rhohv in logarithmic scale according to L=-log10(1-RhoHV).

Parameters:

radar : Radar

Radar object.

rhohv_field : str, optional

Name of the RhoHV field to use.

l_field : str, optional

Name of the L field.

Returns:

l : dict

L field.

pyart.retrieve.compute_mean_phase_iq(radar, signal_field=None)

Computes the differential phase from the horizontal or vertical channel IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

signal_field : str

Name of the field that contain the H or V IQ data. None will use the default field name in the Py-ART configuration file.

Returns:

mph_dict : field dictionary

Field dictionary containing the mean phase

pyart.retrieve.compute_noise_power(spectra, units='dBADU', navg=1, rmin=0.0, nnoise_min=1, signal_field=None)

Computes the noise power from the complex spectra in ADU. Requires key dBADU_to_dBm_hh or dBADU_to_dBm_vv in radar_calibration if the units are to be dBm. The noise is computed using the method described in Hildebrand and Sehkon, 1974.

Parameters:

spectra : Radar spectra object

Object containing the required fields

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

signal_field : str, optional

Name of the field in radar which contains the signal. None will use the default field name in the Py-ART configuration file.

Returns:

noise_dict : field dictionary

Field dictionary containing the noise power

References

P. H. Hildebrand and R. S. Sekhon, Objective Determination of the Noise Level in Doppler Spectra. Journal of Applied Meteorology, 1974, 13, 808-811.

pyart.retrieve.compute_noisedBZ(nrays, noisedBZ_val, _range, ref_dist, noise_field=None)

Computes noise in dBZ from reference noise value.

Parameters:

nrays : int

Number of rays in the reflectivity field.

noisedBZ_val : float

Estimated noise value in dBZ at reference distance.

_range : np array of floats

Range vector in m.

ref_dist : float

Reference distance in Km.

noise_field : str, optional

Name of the noise field.

Returns:

noisedBZ : dict

The noise field.

pyart.retrieve.compute_pol_variables(spectra, fields_list, use_pwr=False, subtract_noise=False, smooth_window=None, srhohv_field=None, pwr_h_field=None, pwr_v_field=None, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes the polarimetric variables from the complex spectra in ADU or the spectral powers and spectral RhoHV

Parameters:

spectra : Radar spectra object

Object containing the required fields

fields_list : list of str

list of fields to compute

use_pwr : Bool

If True the polarimetric variables will be computed from the spectral power and the spectral RhoHV. Otherwise from the complex spectra

subtract_noise : Bool

If True noise will be subtracted from the signals

smooth_window : int or None

Size of the moving Gaussian smoothing window. If none no smoothing will be applied

srhohv_field, pwr_h_field, pwr_v_field, signal_h_field, signal_v_field,

noise_h_field, noise_v_field : str

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

radar : radar object

Object containing the computed fields

pyart.retrieve.compute_pol_variables_iq(radar, fields_list, subtract_noise=False, lag=0, direction='negative_away', phase_offset=0.0, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes the polarimetric variables from the IQ signals in ADU

Parameters:

radar : IQ radar object

Object containing the required fields

fields_list : list of str

list of fields to compute

subtract_noise : Bool

If True noise will be subtracted from the signals

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

phase_offset : float. Dataset keyword

The system differential phase offset to remove

signal_h_field, signal_v_field, noise_h_field, noise_v_field : str

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

radar : radar object

Object containing the computed fields

pyart.retrieve.compute_qvp(radar, field_names, ref_time=None, angle=0.0, ang_tol=1.0, hmax=10000.0, hres=50.0, avg_type='mean', nvalid_min=30, interp_kind='none', qvp=None)

Computes quasi vertical profiles.

Parameters:

radar : Radar

Radar object used.

field_names : list of str

list of field names to add to the QVP

ref_time : datetime object

reference time for current radar volume

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.

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].

hres : float

The height resolution [m].

avg_type : str

The type of averaging to perform. Can be either “mean” or “median”

nvalid_min : int

Minimum number of valid points to accept average.

interp_kind : str

type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. ‘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

qvp : QVP object or None

If it is not None this is the QVP object where to store the data from the current time step. Otherwise a new QVP object will be created

Returns:

qvp : qvp object

The computed QVP object

pyart.retrieve.compute_radial_noise_hs(radar, ind_rmin=0, nbins_min=1, max_std_pwr=2.0, pwr_field=None, noise_field=None, get_noise_pos=False)

Computes radial noise in dBm from signal power using the algorithm from Hildebrand and Sekhon 1974

Parameters:

radar: radar object

radar object containing the signal power in dBm

ind_rmin: int

index of the gate nearest to the radar where start looking for noisy gates

nbins_min: int

min number of noisy gates to consider the estimation valid

max_std_pwr: float

max standard deviation of the noise power to consider the noise valid

pwr_field: str

Name of the input signal power field

noise_field: str

name of the noise field to use

get_noise_pos : bool

If true an additional field with gates containing noise according to the algorithm is produced

Returns:

noise_dict : dict

the noise field in dBm

noise_pos_dict : dict or None

a dictionary containing a field where the gates with noise are set to 2 and those without are set to 1 (0 reserved)

References

P. H. Hildebrand and R. S. Sekhon, Objective Determination of the Noise Level in Doppler Spectra. Journal of Applied Meteorology, 1974, 13, 808-811.

pyart.retrieve.compute_radial_noise_ivic(radar, npulses_ray=30, flat_reg_wlen=96, ngates_min=800, iterations=10, pwr_field=None, noise_field=None, get_noise_pos=False)

Computes radial noise in dBm from signal power using the algorithm described in Ivic et al. 2013

Parameters:

radar: radar object

radar object containing the signal power in dBm

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

flat_reg_wlen : int

number of gates considered to find flat regions. The number represents 8 km length with a 83.3 m resolution

ngates_min: int

minimum number of gates with noise to consider the retrieval valid

iterations: int

number of iterations in step 7

pwr_field: str

Name of the input signal power field

noise_field: str

name of the noise field to use

get_noise_pos : bool

If true an additional field with gates containing noise according to the algorithm is produced

Returns:

noise_dict : dict

the noise field in dBm

noise_pos_dict : dict

the position of the noisy gates

get_noise_pos : bool

If true an additional field with gates containing noise according to the algorithm is produced

References

I.R. Ivic, C. Curtis and S.M. Torres, Radial-Based Noise Power Estimation for Weather Radars. Journal of Atmospheric and Oceanic Technology, 2013, 30, 2737-2753.

pyart.retrieve.compute_rcs(radar, kw2=0.93, pulse_width=None, beamwidth=None, freq=None, refl_field=None, rcs_field=None)

Computes the radar cross-section (assuming a point target) from radar reflectivity.

Parameters:

radar : Radar

radar object

kw2 : float

water constant

pulse_width : float

pulse width [s]

beamwidth : float

beamwidth [degree]

freq : float

radar frequency [Hz]. If none it will be obtained from the radar metadata

refl_field : str

name of the reflectivity used for the calculations

rcs_field : str

name of the RCS field

Returns:

rcs_dict : dict

RCS field and metadata

pyart.retrieve.compute_rcs_from_pr(radar, lmf=None, attg=None, radconst=None, tx_pwr=None, antenna_gain=None, lrx=0.0, ltx=0.0, lradome=0.0, freq=None, refl_field=None, rcs_field=None, neglect_gas_att=False)

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:

radar : Radar

radar object

lmf : float

matched filter losses. If None it will be obtained from the attribute radar_calibration of the radar object

attg : float

1-way gas attenuation

radconst : float

radar constant

tx_pwr : float

radar transmitted power [dBm]

antenna_gain : float

antenna gain [dB]. If None it will be obtain from the instrument_parameters attribute of the radar object

lrx : float

receiver losses from the antenna feed to the reference point (positive value) [dB]

lradome : float

1-way losses due to the radome (positive value) [dB]

freq : float

radar frequency [Hz]. If none it will be obtained from the radar metadata

refl_field : str

name of the reflectivity used for the calculations

rcs_field : str

name of the RCS field

neglect_gas_att : bool

Whether to neglect or not gas attenuation in the estimation of the RCS

Returns:

rcs_dict : dict

RCS field and metadata

pyart.retrieve.compute_reflectivity(spectra, sdBZ_field=None)

Computes the reflectivity from the spectral reflectivity

Parameters:

spectra : Radar spectra object

Object containing the required fields

sdBZ_field : str

Name of the field that contains the spectral reflectivity. None will use the default field name in the Py-ART configuration file.

Returns:

dBZ_dict : field dictionary

Field dictionary containing the reflectivity

pyart.retrieve.compute_reflectivity_iq(radar, subtract_noise=False, signal_field=None, noise_field=None)

Computes the reflectivity from the IQ signal data

Parameters:

radar : IQ radar object

Object containing the required fields

subtract_noise : Bool

If true the noise is subtracted from the power

signal_field, noise_field : str

Name of the signal and noise fields. None will use the default field name in the Py-ART configuration file.

Returns:

dBZ_dict : field dictionary

Field dictionary containing the reflectivity

pyart.retrieve.compute_rhohv(spectra, use_rhohv=False, subtract_noise=False, srhohv_field=None, pwr_h_field=None, pwr_v_field=None, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes RhoHV from the horizontal and vertical spectral reflectivity or from sRhoHV and the spectral powers

Parameters:

spectra : Radar spectra object

Object containing the required fields

use_rhohv : Bool

If true the RhoHV will be computed from sRho_hv. Otherwise it will be computed using the complex spectra

subtract_noise : Bool

If True noise will be subtracted from the signals

srhohv_field, pwr_h_field, pwr_v_field, signal_h_field, signal_v_field,

noise_h_field, noise_v_field : str

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

RhoHV_dict : field dictionary

Field dictionary containing the RhoHV

pyart.retrieve.compute_rhohv_iq(radar, subtract_noise=False, lag=0, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes RhoHV from the horizontal and vertical channels IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

subtract_noise : Bool

If True noise will be subtracted from the signals

lag : int

Time lag used in the computation

signal_h_field, signal_v_field, noise_h_field, noise_v_field : str

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

rhohv_dict : field dictionary

Field dictionary containing the RhoHV

pyart.retrieve.compute_rqvp(radar, field_names, ref_time=None, hmax=10000.0, hres=2.0, avg_type='mean', nvalid_min=30, interp_kind='nearest', rmax=50000.0, weight_power=2.0, qvp=None)

Computes range-defined quasi vertical profiles.

Parameters:

radar : Radar

Radar object used.

field_names : list of str

list of field names to add to the QVP

ref_time : datetime object

reference time for current radar volume

hmax : float

The maximum height to plot [m].

hres : float

The height resolution [m].

avg_type : str

The type of averaging to perform. Can be either “mean” or “median”

nvalid_min : int

Minimum number of valid points to accept average.

interp_kind : str

type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. ‘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].

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.

qvp : QVP object or None

If it is not None this is the QVP object where to store the data from the current time step. Otherwise a new QVP object will be created

Returns:

qvp : qvp object

The computed range defined quasi vertical profile

pyart.retrieve.compute_signal_power(radar, lmf=None, attg=None, radconst=None, lrx=0.0, lradome=0.0, refl_field=None, pwr_field=None)

Computes received signal power OUTSIDE THE RADOME in dBm from a reflectivity field.

Parameters:

radar : Radar

radar object

lmf : float

matched filter losses

attg : float

1-way gas attenuation

radconst : float

radar constant

lrx : float

receiver losses from the antenna feed to the reference point (positive value) [dB]

lradome : float

1-way losses due to the radome (positive value) [dB]

refl_field : str

name of the reflectivity used for the calculations

pwr_field : str

name of the signal power field

Returns:

s_pwr_dict : dict

power field and metadata

pyart.retrieve.compute_snr(radar, refl_field=None, noise_field=None, snr_field=None)

Computes SNR from a reflectivity field and the noise in dBZ.

Parameters:

radar : Radar

Radar object

refl_field : str, optional

Name of the reflectivity field to use.

noise_field : str, optional

Name of the noise field to use.

snr_field : str, optional

Name of the SNR field.

Returns:

snr : dict

The SNR field.

pyart.retrieve.compute_spectra(radar, fields_in_list, fields_out_list, window=None)

Computes the spectra from IQ data through a Fourier transform

Parameters:

radar : radar object

Object containing the IQ data

fields_in_list : list of str

list of input IQ data fields names

fields_out_list : list of str

list with the output spectra fields names obtained from the input fields

window : string, tupple or None

Parameters of the window used to obtain the spectra. The parameters are the ones corresponding to function scipy.signal.windows.get_window. If None no window will be used

Returns:

spectra : spectra radar object

radar object containing the spectra fields

pyart.retrieve.compute_spectral_differential_phase(spectra, use_rhohv=False, srhohv_field=None, signal_h_field=None, signal_v_field=None)

Computes the spectral differential reflectivity from the complex spectras in ADU or sRhoHV

Parameters:

spectra : Radar spectra object

Object containing the required fields

use_rhohv : Bool

If true sRhoHV is going to be used to compute the differential phase. Otherwise the complex signals are used

signal_h_field, signal_v_field : str

Name of the fields in radar which contains the signal. None will use the default field name in the Py-ART configuration file.

Returns:

sPhiDP_dict : field dictionary

Field dictionary containing the spectral differential phase

pyart.retrieve.compute_spectral_differential_reflectivity(spectra, compute_power=True, subtract_noise=False, smooth_window=None, pwr_h_field=None, pwr_v_field=None, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes the spectral differential reflectivity from the complex spectras or the power in ADU

Parameters:

spectra : Radar spectra object

Object containing the required fields

compute_power : Bool

If True the signal power will be computed. Otherwise the field given by the user will be used

subtract_noise : Bool

If True noise will be subtracted from the signals

smooth_window : int or None

Size of the moving Gaussian smoothing window. If none no smoothing will be applied

pwr_h_field, pwr_v_field, signal_h_field, signal_v_field, noise_h_field,

noise_v_field : str

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

sZDR_dict : field dictionary

Field dictionary containing the spectral differential reflectivity

pyart.retrieve.compute_spectral_noise(spectra, units='dBADU', navg=1, rmin=0.0, nnoise_min=1, signal_field=None)

Computes the spectral noise power from the complex spectra in ADU. Requires key dBADU_to_dBm_hh or dBADU_to_dBm_vv in radar_calibration if the units are to be dBm. The noise is computed using the method described in Hildebrand and Sehkon, 1974.

Parameters:

spectra : Radar spectra object

Object containing the required fields

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

signal_field : str, optional

Name of the field in radar which contains the signal. None will use the default field name in the Py-ART configuration file.

Returns:

noise_dict : field dictionary

Field dictionary containing the spectral noise power

References

P. H. Hildebrand and R. S. Sekhon, Objective Determination of the Noise Level in Doppler Spectra. Journal of Applied Meteorology, 1974, 13, 808-811.

pyart.retrieve.compute_spectral_phase(spectra, signal_field=None)

Computes the spectral phase from the complex spectra in ADU

Parameters:

spectra : Radar spectra object

Object containing the required fields

signal_field : str, optional

Name of the field in radar which contains the signal. None will use the default field name in the Py-ART configuration file.

Returns:

phase_dict : field dictionary

Field dictionary containing the spectral phase

pyart.retrieve.compute_spectral_power(spectra, units='dBADU', subtract_noise=False, smooth_window=None, signal_field=None, noise_field=None)

Computes the spectral power from the complex spectra in ADU. Requires key dBADU_to_dBm_hh or dBADU_to_dBm_vv in radar_calibration if the units are to be dBm

Parameters:

spectra : Radar spectra object

Object containing the required fields

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

signal_field, noise_field : str, optional

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

pwr_dict : field dictionary

Field dictionary containing the spectral power

pyart.retrieve.compute_spectral_reflectivity(spectra, compute_power=True, subtract_noise=False, smooth_window=None, pwr_field=None, signal_field=None, noise_field=None)

Computes the spectral reflectivity from the complex spectra in ADU or from the signal power in ADU. Requires keys dBADU_to_dBm_hh or dBADU_to_dBm_vv in radar_calibration if the to be computed

Parameters:

spectra : Radar spectra object

Object containing the required fields

compute_power : Bool

If True the signal power will be computed. Otherwise the field given by the user will be used

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

pwr_field, signal_field, noise_field : str, optional

Name of the fields in radar which contains the signal power, complex signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

sdBZ_dict : field dictionary

Field dictionary containing the spectral reflectivity

pyart.retrieve.compute_spectral_rhohv(spectra, subtract_noise=False, signal_h_field=None, signal_v_field=None, noise_h_field=None, noise_v_field=None)

Computes the spectral RhoHV from the complex spectras in ADU

Parameters:

spectra : Radar spectra object

Object containing the required fields

subtract_noise : Bool

If True noise will be subtracted from the signals

signal_h_field, signal_v_field, noise_h_field, noise_v_field : str

Name of the fields in radar which contains the signal and noise. None will use the default field name in the Py-ART configuration file.

Returns:

sRhoHV_dict : field dictionary

Field dictionary containing the spectral RhoHV

pyart.retrieve.compute_st1_iq(radar, signal_field=None)

Computes the statistical test one lag fluctuation from the horizontal or vertical channel IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

signal_field : str

Name of the field that contain the H or V IQ data. None will use the default field name in the Py-ART configuration file.

Returns:

st1_dict : field dictionary

Field dictionary containing the st1

pyart.retrieve.compute_st2_iq(radar, signal_field=None)

Computes the statistical test two lag fluctuation from the horizontal or vertical channel IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

signal_field : str

Name of the field that contain the H or V IQ data. None will use the default field name in the Py-ART configuration file.

Returns:

st2_dict : field dictionary

Field dictionary containing the st2

pyart.retrieve.compute_svp(radar, field_names, lon, lat, angle, ref_time=None, ang_tol=1.0, latlon_tol=0.0005, delta_rng=15000.0, delta_azi=10, hmax=10000.0, hres=250.0, avg_type='mean', nvalid_min=1, interp_kind='none', qvp=None)

Computes slanted vertical profiles.

Parameters:

radar : Radar

Radar object used.

field_names : list of str

list of field names to add to the QVP

lat, lon : float

latitude and longitude of the point of interest [deg]

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.

ref_time : datetime object

reference time for current radar volume

ang_tol : float

If the radar object contains an RHI volume, the tolerance in the elevation angle for the conversion into PPI

latlon_tol : float

tolerance in latitude and longitude in deg.

delta_rng, delta_azi : float

maximum range distance [m] and azimuth distance [degree] from the central point of the evp containing data to average.

hmax : float

The maximum height to plot [m].

hres : float

The height resolution [m].

avg_type : str

The type of averaging to perform. Can be either “mean” or “median”

nvalid_min : int

Minimum number of valid points to accept average.

interp_kind : str

type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. ‘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

qvp : QVP object or None

If it is not None this is the QVP object where to store the data from the current time step. Otherwise a new QVP object will be created

Returns:

qvp : qvp object

The computed slanted vertical profile

pyart.retrieve.compute_ts_along_coord(radar, field_name, mode='ALONG_AZI', fixed_range=None, fixed_azimuth=None, fixed_elevation=None, ang_tol=1.0, rng_tol=50.0, value_start=None, value_stop=None, ref_time=None, acoord=None)

Computes time series along a particular antenna coordinate, i.e. along azimuth, elevation or range

Parameters:

radar : Radar

Radar object used.

field_name : str

Name of the field

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

ref_time : datetime object

reference time for current radar volume

acoord : acoord object or None

If it is not None this is the object where to store the data from the current time step. Otherwise a new acoord object will be created

Returns:

acoord : acoord object

The computed data along a coordinate

pyart.retrieve.compute_vol_refl(radar, kw=0.93, freq=None, refl_field=None, vol_refl_field=None)

Computes the volumetric reflectivity from the effective reflectivity factor

Parameters:

radar : Radar

radar object

kw : float

water constant

freq : None or float

radar frequency

refl_field : str

name of the reflectivity used for the calculations

vol_refl_field : str

name of the volumetric reflectivity

Returns:

vol_refl_dict : dict

volumetric reflectivity and metadata in 10log10(cm^2 km^-3)

pyart.retrieve.compute_vp(radar, field_names, lon, lat, ref_time=None, latlon_tol=0.0005, hmax=10000.0, hres=50.0, interp_kind='none', qvp=None)

Computes vertical profiles.

Parameters:

radar : Radar

Radar object used.

field_names : list of str

list of field names to add to the QVP

lat, lon : float

latitude and longitude of the point of interest [deg]

ref_time : datetime object

reference time for current radar volume

latlon_tol : float

tolerance in latitude and longitude in deg.

hmax : float

The maximum height to plot [m].

hres : float

The height resolution [m].

interp_kind : str

type of interpolation when projecting to vertical grid: ‘none’, or ‘nearest’, etc. ‘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

qvp : QVP object or None

If it is not None this is the QVP object where to store the data from the current time step. Otherwise a new QVP object will be created

Returns:

qvp : qvp object

The computed vertical profile

pyart.retrieve.compute_wbn_iq(radar, signal_field=None)

Computes the wide band noise from the horizontal or vertical channel IQ data

Parameters:

radar : IQ radar object

Object containing the required fields

signal_field : str

Name of the field that contain the H or V IQ data. None will use the default field name in the Py-ART configuration file.

Returns:

wbn_dict : field dictionary

Field dictionary containing the wide band noise

pyart.retrieve.detect_ml(radar, gatefilter=None, fill_value=None, refl_field=None, rhohv_field=None, ml_field=None, ml_pos_field=None, iso0_field=None, max_range=20000, detect_threshold=0.02, interp_holes=False, max_length_holes=250, check_min_length=True, get_iso0=False)

Detects the melting layer (ML) using the reflectivity and copolar correlation coefficient. Internally it uses RHIs

Returns:

ml_obj : radar-like object

A radar-like object containing the field melting layer height with the bottom (at range position 0) and top (at range position one) of the melting layer at each ray

ml_dict : dict

A dictionary containg the position of the range gate respect to the melting layer and metadata

iso0_dict : dict or None

A dictionary containing the distance respect to the melting layer and metadata

all_ml : dict

Dictionary containing internal parameters in polar and cartesian coordinates

pyart.retrieve.est_rain_rate_a(radar, alpha=None, beta=None, a_field=None, rr_field=None)

Estimates rainfall rate from specific attenuation using alpha power law.

Parameters:

radar : Radar

Radar object.

alpha, beta : floats, optional

Factor (alpha) and exponent (beta) of the power law. If not set the factors are going to be determined according to the radar frequency.

a_field : str, optional

Name of the specific attenuation field to use.

rr_field : str, optional

Name of the rainfall rate field.

Returns:

rain : dict

Field dictionary containing the rainfall rate.

References

Diederich M., Ryzhkov A., Simmer C., Zhang P. and Tromel S., 2015: Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelenghts. Part I: Radar Calibration and Partial Beam Blockage Estimation. Journal of Hydrometeorology, 16, 487-502.

Ryzhkov A., Diederich M., Zhang P. and Simmer C., 2014: Potential Utilization of Specific Attenuation for Rainfall Estimation, Mitigation of Partial Beam Blockage, and Radar Networking. Journal of Atmospheric and Oceanic Technology, 31, 599-619.

pyart.retrieve.est_rain_rate_hydro(radar, alphazr=0.0376, betazr=0.6112, alphazs=0.1, betazs=0.5, alphaa=None, betaa=None, mp_factor=0.6, refl_field=None, a_field=None, hydro_field=None, rr_field=None, master_field=None, thresh=None, thresh_max=False)

Estimates rainfall rate using different relations between R and the polarimetric variables depending on the hydrometeor type.

Parameters:

radar : Radar

Radar object.

alphazr, betazr : floats, optional

Factor (alpha) and exponent (beta) of the z-r power law for rain.

alphazs, betazs : floats, optional

Factor (alpha) and exponent (beta) of the z-s power law for snow.

alphaa, betaa : floats, optional

Factor (alpha) and exponent (beta) of the a-r power law. If not set the factors are going to be determined according to the radar frequency.

mp_factor : float, optional

Factor applied to z-r relation in the melting layer.

refl_field : str, optional

Name of the reflectivity field to use.

a_field : str, optional

Name of the specific attenuation field to use.

hydro_field : str, optional

Name of the hydrometeor classification field to use.

rr_field : str, optional

Name of the rainfall rate field.

master_field : str, optional

Name of the field that is going to act as master. Has to be either refl_field or kdp_field. Default is refl_field.

thresh : float, optional

Value of the threshold that determines when to use the slave field.

thresh_max : Bool, optional

If true the master field is used up to the thresh value maximum. Otherwise the master field is not used below thresh value.

Returns:

rain : dict

Field dictionary containing the rainfall rate.

pyart.retrieve.est_rain_rate_kdp(radar, alpha=None, beta=None, kdp_field=None, rr_field=None)

Estimates rainfall rate from kdp using alpha power law.

Parameters:

radar : Radar

Radar object.

alpha, beta : floats, optional

Factor (alpha) and exponent (beta) of the power law. If not set the factors are going to be determined according to the radar frequency.

kdp_field : str, optional

Name of the specific differential phase field to use.

rr_field : str, optional

Name of the rainfall rate field.

Returns:

rain : dict

Field dictionary containing the rainfall rate.

pyart.retrieve.est_rain_rate_z(radar, alpha=0.0376, beta=0.6112, refl_field=None, rr_field=None)

Estimates rainfall rate from reflectivity using a power law.

Parameters:

radar : Radar

Radar object.

alpha, beta : floats, optional

Factor (alpha) and exponent (beta) of the power law.

refl_field : str, optional

Name of the reflectivity field to use.

rr_field : str, optional

Name of the rainfall rate field.

Returns:

rain : dict

Field dictionary containing the rainfall rate.

pyart.retrieve.est_rain_rate_za(radar, alphaz=0.0376, betaz=0.6112, alphaa=None, betaa=None, refl_field=None, a_field=None, rr_field=None, master_field=None, thresh=None, thresh_max=True)

Estimates rainfall rate from a blending of power law r-alpha and r-z relations.

Parameters:

radar : Radar

Radar object

alphaz, betaz : floats, optional

Factor (alpha) and exponent (beta) of the z-r power law.

alphaa,betaa : floats, optional

Factor (alpha) and exponent (beta) of the a-r power law. If not set the factors are going to be determined according to the radar frequency.

refl_field : str, optional

Name of the reflectivity field to use.

a_field : str, optional

Name of the specific attenuation field to use.

rr_field : str, optional

Name of the rainfall rate field.

master_field : str, optional

Name of the field that is going to act as master. Has to be either refl_field or kdp_field. Default is refl_field.

thresh : float, optional

Value of the threshold that determines when to use the slave field.

thresh_max : Bool, optional

If true the master field is used up to the thresh value maximum. Otherwise the master field is not used below thresh value.

Returns:

rain_master : dict

Field dictionary containing the rainfall rate.

pyart.retrieve.est_rain_rate_zkdp(radar, alphaz=0.0376, betaz=0.6112, alphakdp=None, betakdp=None, refl_field=None, kdp_field=None, rr_field=None, master_field=None, thresh=None, thresh_max=True)

Estimates rainfall rate from a blending of power law r-kdp and r-z relations.

Parameters:

radar : Radar

Radar object.

alphaz, betaz : floats, optional

Factor (alpha) and exponent (beta) of the z-r power law.

alphakdp, betakdp : floats, optional

Factor (alpha) and exponent (beta) of the kdp-r power law. If not set the factors are going to be determined according to the radar frequency.

refl_field : str, optional

Name of the reflectivity field to use.

kdp_field : str, optional

Name of the specific differential phase field to use.

rr_field : str, optional

Name of the rainfall rate field.

master_field : str, optional

Name of the field that is going to act as master. Has to be either refl_field or kdp_field. Default is refl_field.

thresh : float, optional

Value of the threshold that determines when to use the slave field [mm/h].

thresh_max : Bool, optional

If true the master field is used up to the thresh value maximum. Otherwise the master field is not used below thresh value.

Returns:

rain_master : dict

Field dictionary containing the rainfall rate.

pyart.retrieve.est_rain_rate_zpoly(radar, refl_field=None, rr_field=None)

Estimates rainfall rate from reflectivity using a polynomial Z-R relation developed at McGill University.

Parameters:

radar : Radar

Radar object.

refl_field : str, optional

Name of the reflectivity field to use.

rr_field : str, optional

Name of the rainfall rate field.

Returns:

rain : dict

Field dictionary containing the rainfall rate.

pyart.retrieve.est_vertical_windshear(radar, az_tol=0.5, wind_field=None, windshear_field=None)

Estimates wind shear.

Parameters:

radar : Radar

Radar object

az_tol : float

azimuth tolerance to consider gate on top of selected one

wind_field : str

name of the horizontal wind velocity field

windshear_field : str

name of the vertical wind shear field

Returns:

windshear : dict

Field dictionary containing the wind shear field

pyart.retrieve.est_wind_profile(radar, npoints_min=6, azi_spacing_max=45.0, vel_diff_max=10.0, sign=1, rad_vel_field=None, u_vel_field=None, v_vel_field=None, w_vel_field=None, vel_est_field=None, vel_std_field=None, vel_diff_field=None)

Estimates the vertical wind profile using VAD techniques

Parameters:

radar : Radar

Radar object

npoints_min : int

Minimum number of points in the VAD to retrieve wind components. 0 will retrieve them regardless

azi_spacing_max : float

Maximum spacing between valid gates in the VAD to retrieve wind components. 0 will retrieve them regardless.

vel_diff_max : float

Maximum velocity difference allowed between retrieved and measured radial velocity at each range gate. Gates exceeding this threshold will be removed and VAD will be recomputed. If -1 there will not be a second pass.

sign : int, optional

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.

rad_vel_field : str

name of the measured radial velocity field

u_vel_field, v_vel_field, w_vel_field : str

names of the 3 wind components fields

vel_est_field : str

name of the retrieved radial Doppler velocity field

vel_std_field : str

name of the standard deviation of the velocity retrieval field

vel_diff_field : str

name of the diference between retrieved and measured radial velocity field

Returns:

wind : dict

Field dictionary containing the estimated wind velocity

pyart.retrieve.est_wind_vel(radar, vert_proj=False, vel_field=None, wind_field=None)

Estimates wind velocity. Projects the radial wind component to the horizontal or vertical of the azimuth plane. It assumes that the orthogonal component is negligible.

The horizontal wind component is given by:

v = v_r*cos(el)-v_el*sin(el)+v_az

where:

v_r is the radial wind component (measured by the radar) v_el is the perpendicular wind component in the azimuth plane. v_az is the horizontal component perpendicular to the radial direction and the azimuth plane el is the elevation

The horizontal wind component in the azimuth plane is given by:

v_h = v_r*cos(el) - v_el*sin(el)

which since we do not know v_el we assume:

v_h ~ v_r*cos(el)

This assumption holds for small elevation angles

The vertical wind component in the azimuth plane is given by:

v_h = v_r*sin(el) - v_el*cos(el)

which since we do not know v_el we assume:

v_h ~ v_r*sin(el)

This assumption holds for angles close to 90 deg

Parameters:

radar : Radar

Radar object

vert_proj : Boolean

If true estimates the vertical projection, otherwise the horizontal

vel_field : str

name of the velocity field

wind_field : str

name of the velocity field

Returns:

wind : dict

Field dictionary containing the estimated wind velocity

pyart.retrieve.fetch_radar_time_profile(sonde_dset, radar, time_key='time', height_key='height', nvars=None)

Extract the correct profile from a interpolated sonde.

This is an ARM specific method which extract the correct profile out of netCDF Variables from a Interpolated Sonde VAP for the volume start time of a radar object.

Parameters:

sonde_dset : Dataset

Interpolate sonde Dataset.

radar : Radar

Radar object from which the nearest profile will be found.

time_key : string, optional

Key to find a CF startard time variable.

height_key : string, optional

Key to find profile height data.

nvars : list, optional

NetCDF variable to generated profiles for. If None (the default) all variables with dimension of time, height will be found in ncvars.

Returns:

return_dic : dict

Profiles at the start time of the radar.

pyart.retrieve.get_coeff_attg(freq)

get the 1-way gas attenuation for a particular frequency

Parameters:

freq : float

radar frequency [Hz]

Returns:

attg : float

1-way gas attenuation

pyart.retrieve.get_freq_band(freq)

Returns the frequency band name (S, C, X, …).

Parameters:

freq : float

Radar frequency [Hz].

Returns:

freq_band : str

Frequency band name.

pyart.retrieve.grid_displacement_pc(grid1, grid2, field, level, return_value='pixels')

Calculate the grid displacement using phase correlation.

See: http://en.wikipedia.org/wiki/Phase_correlation

Implementation inspired by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/code/imreg.py.html

Note that the grid must have the same dimensions in x and y and assumed to have constant spacing in these dimensions.

Parameters:

grid1, grid2 : Grid

Py-ART Grid objects separated in time and square in x/y.

field : string

Field to calculate advection from. Field must be in both grid1 and grid2.

level : integer

The vertical (z) level of the grid to use in the calculation.

return_value : str, optional

‘pixels’, ‘distance’ or ‘velocity’. Distance in pixels (default) or meters or velocity vector in m/s.

Returns:

displacement : two-tuple

Calculated displacement in units of y and x. Value returned in integers if pixels, otherwise floats.

pyart.retrieve.grid_shift(grid, advection, trim_edges=0, field_list=None)

Shift a grid by a certain number of pixels.

Parameters:

grid: Grid

Py-ART Grid object.

advection : two-tuple of floats

Number of Pixels to shift the image by.

trim_edges: integer, optional

Edges to cut off the grid and axes, both x and y. Defaults to zero.

field_list : list, optional

List of fields to include in new grid. None, the default, includes all fields from the input grid.

Returns:

shifted_grid : Grid

Grid with fields shifted and, if requested, subset.

pyart.retrieve.hydroclass_semisupervised(radar, mass_centers=None, weights=array([1., 1., 1., 0.75, 0.5 ]), value=50.0, refl_field=None, zdr_field=None, rhv_field=None, kdp_field=None, temp_field=None, iso0_field=None, hydro_field=None, entropy_field=None, temp_ref='temperature', compute_entropy=False, output_distances=False, vectorize=False)

Classifies precipitation echoes following the approach by Besic et al (2016).

Parameters:

radar : radar

Radar object.

mass_centers : ndarray 2D, optional

The centroids for each variable and hydrometeor class in (nclasses, nvariables).

weights : ndarray 1D, optional

The weight given to each variable.

value : float

The value controlling the rate of decay in the distance transformation

refl_field, zdr_field, rhv_field, kdp_field, temp_field, iso0_field : str

Inputs. Field names within the radar object which represent the horizonal reflectivity, the differential reflectivity, the copolar correlation coefficient, the specific differential phase, the temperature and the height respect to the iso0 fields. A value of None for any of these parameters will use the default field name as defined in the Py-ART configuration file.

hydro_field : str

Output. Field name which represents the hydrometeor class field. A value of None will use the default field name as defined in the Py-ART configuration file.

temp_ref : str

the field use as reference for temperature. Can be either temperature or height_over_iso0

compute_entropy : bool

If true, the entropy is computed

output_distances : bool

If true, the normalized distances to the centroids for each hydrometeor are provided as output

vectorize : bool

If true, a vectorized version of the class assignation is going to be used

Returns:

fields_dict : dict

Dictionary containing the retrieved fields

References

Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425-4445, doi:10.5194/amt-9-4425-2016, 2016

pyart.retrieve.kdp_leastsquare_double_window(radar, swind_len=11, smin_valid=6, lwind_len=31, lmin_valid=16, zthr=40.0, phidp_field=None, refl_field=None, kdp_field=None, vectorize=False)

Compute the specific differential phase (KDP) from differential phase data using a piecewise least square method. For optimal results PhiDP should be already smoothed and clutter filtered out.

Parameters:

radar : Radar

Radar object.

swind_len : int

The lenght of the short moving window.

smin_valid : int

Minimum number of valid bins to consider the retrieval valid when using the short moving window

lwind_len : int

The lenght of the long moving window.

lmin_valid : int

Minimum number of valid bins to consider the retrieval valid when using the long moving window

zthr : float

reflectivity value above which the short window is used

phidp_field : str

Field name within the radar object which represent the differential phase shift. A value of None will use the default field name as defined in the Py-ART configuration file.

refl_field : str

Field name within the radar object which represent the reflectivity. A value of None will use the default field name as defined in the Py-ART configuration file.

kdp_field : str

Field name within the radar object which represent the specific differential phase shift. A value of None will use the default field name as defined in the Py-ART configuration file.

vectorize : bool

whether to use a vectorized version of the least square method

Returns:

kdp_dict : dict

Retrieved specific differential phase data and metadata.

pyart.retrieve.kdp_leastsquare_single_window(radar, wind_len=11, min_valid=6, phidp_field=None, kdp_field=None, vectorize=False)

Compute the specific differential phase (KDP) from differential phase data using a piecewise least square method. For optimal results PhiDP should be already smoothed and clutter filtered out.

Parameters:

radar : Radar

Radar object.

wind_len : int

The lenght of the moving window.

min_valid : int

Minimum number of valid bins to consider the retrieval valid

phidp_field : str

Field name within the radar object which represent the differential phase shift. A value of None will use the default field name as defined in the Py-ART configuration file.

kdp_field : str

Field name within the radar object which represent the specific differential phase shift. A value of None will use the default field name as defined in the Py-ART configuration file.

vectorize : bool

whether to use a vectorized version of the least square method

Returns:

kdp_dict : dict

Retrieved specific differential phase data and metadata.

pyart.retrieve.kdp_maesaka(radar, gatefilter=None, method='cg', backscatter=None, Clpf=1.0, length_scale=None, first_guess=0.01, finite_order='low', fill_value=None, proc=1, psidp_field=None, kdp_field=None, phidp_field=None, debug=False, verbose=False, **kwargs)

Compute the specific differential phase (KDP) from corrected (e.g., unfolded) total differential phase data based on the variational method outlined in Maesaka et al. (2012). This method assumes a monotonically increasing propagation differential phase (PHIDP) with increasing range from the radar, and therefore is limited to rainfall below the melting layer and/or warm clouds at weather radar frequencies (e.g., S-, C-, and X-band). This method currently only supports radar data with constant range resolution.

Following the notation of Maesaka et al. (2012), the primary control variable k is proportional to KDP,

k**2 = 2 * KDP * dr

which, because of the square, assumes that KDP always takes a positive value.

Parameters:

radar : Radar

Radar containing differential phase field.

gatefilter : GateFilter

A GateFilter indicating radar gates that should be excluded when analysing differential phase measurements.

method : str, optional

Type of scipy.optimize method to use when minimizing the cost functional. The default method uses a nonlinear conjugate gradient algorithm. In Maesaka et al. (2012) they use the Broyden-Fletcher- Goldfarb-Shanno (BFGS) algorithm, however for large functional size (e.g., 100K+ variables) this algorithm is considerably slower than a conjugate gradient algorithm.

backscatter : optional

Define the backscatter differential phase. If None, the backscatter differential phase is set to zero for all range gates. Note that backscatter differential phase can be parameterized using attentuation corrected differential reflectivity.

Clpf : float, optional

The low-pass filter (radial smoothness) constraint weight as in equation (15) of Maesaka et al. (2012).

length_scale : float, optional

Length scale in meters used to bring the dimension and magnitude of the low-pass filter cost functional in line with the observation cost functional. If None, the length scale is set to the range resolution.

first_guess : float, optional

First guess for control variable k. Since k is proportional to the square root of KDP, the first guess should be close to zero to signify a KDP field close to 0 deg/km everywhere. However, the first guess should not be exactly zero in order to avoid convergence criteria after the first iteration. In fact it is recommended to use a value closer to one than zero.

finite_order : ‘low’ or ‘high’, optional

The finite difference accuracy to use when computing derivatives.

maxiter : int, optional

Maximum number of iterations to perform during cost functional minimization. The maximum number of iterations are only performed if convergence criteria are not met. For variational schemes such as this one, it is generally not recommended to try and achieve convergence criteria since the values of the cost functional and/or its gradient norm are somewhat arbitrary.

fill_value : float, optional

Value indicating missing or bad data in differential phase field.

proc : int, optional

The number of parallel threads (CPUs) to use. Currently no multiprocessing capability exists.

psidp_field : str, optional

Total differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

kdp_field : str, optional

Specific differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

phidp_field : str, optional

Propagation differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

debug : bool, optional

True to print debugging information, False to suppress.

verbose : bool, optional

True to print relevant information, False to suppress.

Returns:

kdp_dict : dict

Retrieved specific differential phase data and metadata.

phidpf_dict, phidpr_dict : dict

Retrieved forward and reverse direction propagation differential phase data and metadata.

References

Maesaka, T., Iwanami, K. and Maki, M., 2012: “Non-negative KDP Estimation by Monotone Increasing PHIDP Assumption below Melting Layer”. The Seventh European Conference on Radar in Meteorology and Hydrology.

pyart.retrieve.kdp_schneebeli(radar, gatefilter=None, fill_value=None, psidp_field=None, kdp_field=None, phidp_field=None, band='C', rcov=0, pcov=0, prefilter_psidp=False, filter_opt=None, parallel=True)

Estimates Kdp with the Kalman filter method by Schneebeli and al. (2014) for a set of psidp measurements.

Parameters:

radar : Radar

Radar containing differential phase field.

gatefilter : GateFilter, optional

A GateFilter indicating radar gates that should be excluded when analysing differential phase measurements.

fill_value : float, optional

Value indicating missing or bad data in differential phase field, if not specified, the default in the Py-ART configuration file will be used.

psidp_field : str, optional

Total differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

kdp_field : str, optional

Specific differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

phidp_field : str, optional

Propagation differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

band : char, optional

Radar frequency band string. Accepted “X”, “C”, “S” (capital or not). The band is used to compute intercepts -c and slope b of the delta = b*Kdp+c relation.

rcov : 3x3 float array, optional

Measurement error covariance matrix.

pcov : 4x4 float array, optional

Scaled state transition error covariance matrix.

prefilter_psidp : bool, optional

If set, the psidp measurements will first be filtered with the filter_psidp method, which can improve the quality of the final Kdp.

filter_opt : dict, optional

The arguments for the prefilter_psidp method, if empty, the defaults arguments of this method will be used.

parallel : bool, optional

Flag to enable parallel computation (one core for every psidp profile).

Returns:

kdp_dict : dict

Retrieved specific differential phase data and metadata.

kdp_std_dict : dict

Estimated specific differential phase standard dev. data and metadata.

phidpr_dict,: dict

Retrieved differential phase data and metadata.

References

Schneebeli, M., Grazioli, J., and Berne, A.: Improved Estimation of the Specific Differential Phase SHIFT Using a Compilation of Kalman Filter Ensembles, IEEE T. Geosci. Remote Sens., 52, 5137-5149, doi:10.1109/TGRS.2013.2287017, 2014.

pyart.retrieve.kdp_vulpiani(radar, gatefilter=None, fill_value=None, psidp_field=None, kdp_field=None, phidp_field=None, band='C', windsize=10, n_iter=10, interp=False, prefilter_psidp=False, filter_opt=None, parallel=False)

Estimates Kdp with the Vulpiani method for a 2D array of psidp measurements with the first dimension being the distance from radar and the second dimension being the angles (azimuths for PPI, elev for RHI).The input psidp is assumed to be pre-filtered (for ex. with the filter_psidp function)

Parameters:

radar : Radar

Radar containing differential phase field.

gatefilter : GateFilter, optional

A GateFilter indicating radar gates that should be excluded when analysing differential phase measurements.

fill_value : float, optional

Value indicating missing or bad data in differential phase field, if not specified, the default in the Py-ART configuration file will be used

psidp_field : str, optional

Total differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

kdp_field : str, optional

Specific differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

phidp_field : str, optional

Propagation differential phase field. If None, the default field name must be specified in the Py-ART configuration file.

band : char, optional

Radar frequency band string. Accepted “X”, “C”, “S” (capital or not). It is used to set default boundaries for expected values of Kdp.

windsize : int, optional

Size in # of gates of the range derivative window. Should be even.

n_iter : int, optional

Number of iterations of the method. Default is 10.

interp : bool, optional

If True, all the nans are interpolated.The advantage is that less data are lost (the iterations in fact are “eating the edges”) but some non-linear errors may be introduced.

prefilter_psidp : bool, optional

If set, the psidp measurements will first be filtered with the filter_psidp method, which can improve the quality of the final Kdp.

filter_opt : dict, optional

The arguments for the prefilter_psidp method, if empty, the defaults arguments of this method will be used.

parallel : bool, optional

Flag to enable parallel computation (one core for every psidp profile).

Returns:

kdp_dict : dict

Retrieved specific differential phase data and metadata.

phidpr_dict,: dict

Retrieved differential phase data and metadata.

References

Gianfranco Vulpiani, Mario Montopoli, Luca Delli Passeri, Antonio G. Gioia, Pietro Giordano, and Frank S. Marzano, 2012: On the Use of Dual-Polarized C-Band Radar for Operational Rainfall Retrieval in Mountainous Areas. J. Appl. Meteor. Climatol., 51, 405-425, doi: 10.1175/JAMC-D-10-05024.1.

pyart.retrieve.map_profile_to_gates(profile, heights, radar, toa=None, profile_field=None, height_field=None)

Given a profile of a variable map it to the gates of radar assuming 4/3Re.

Parameters:

profile : array

Profile array to map.

heights : array

Monotonically increasing heights in meters with same shape as profile.

radar : Radar

Radar to map to.

toa : float, optional

Top of atmosphere, where to use profile up to. If None check for mask and use lowest element, if no mask uses whole profile.

height_field : str, optional

Name to use for height field metadata. None will use the default field name from the Py-ART configuration file.

profile_field : str, optional

Name to use for interpolate profile field metadata. None will use the default field name from the Py-ART configuration file.

Returns:

height_dict, profile_dict : dict

Field dictionaries containing the height of the gates and the profile interpolated onto the radar gates.

pyart.retrieve.melting_layer_giangrande(radar, nVol=3, maxh=6000.0, hres=50.0, rmin=1000.0, elmin=4.0, elmax=10.0, rhomin=0.75, rhomax=0.94, zhmin=20.0, hwindow=500.0, mlzhmin=30.0, mlzhmax=50.0, mlzdrmin=1.0, mlzdrmax=5.0, htol=500.0, ml_bottom_diff_max=1000.0, time_accu_max=1800.0, nml_points_min=None, wlength=20.0, percentile_bottom=0.3, percentile_top=0.9, interpol=True, time_nodata_allowed=3600.0, refl_field=None, zdr_field=None, rhv_field=None, temp_field=None, iso0_field=None, ml_field=None, ml_pos_field=None, temp_ref=None, get_iso0=False, ml_global=None)

Detects the melting layer following the approach by Giangrande et al (2008)

Parameters:

radar : radar

radar object

Returns:

ml_obj : radar-like object

A radar-like object containing the field melting layer height with the bottom (at range position 0) and top (at range position one) of the melting layer at each ray

ml_dict : dict

A dictionary containg the position of the range gate respect to the melting layer and metadata

iso0_dict : dict or None

A dictionary containing the distance respect to the melting layer and metadata

ml_global : dict or None

stack of previous volume data to introduce some time dependency. Its max size is controlled by the nVol parameter. It is always in (pseudo-)RHI mode.

Other Parameters:  

nVol : int

Number of volume scans to aggregate

maxh : float

Maximum possible height of the melting layer [m MSL]

hres : float

Step of the height of the melting layer [m]

rmin : float

Minimum range from radar where to look for melting layer contaminated range gates [m]

elmin, elmax : float

Minimum and maximum elevation angles where to look for melting layer contaminated range gates [degree]

rhomin, rhomax : float

min and max rhohv to consider pixel potential melting layer pixel

zhmin : float

Minimum reflectivity level of a range gate to consider it a potential melting layer gate [dBZ]

hwindow : float

Maximum distance (in range) from potential melting layer gate where to look for a maximum [m]

mlzhmin, mlzhmax : float

Minimum and maximum values that a peak in reflectivity within the melting layer may have to consider the range gate melting layer contaminated [dBZ]

mlzdrmin, mlzdrmax : float

Minimum and maximum values that a peak in differential reflectivity within the melting layer may have to consider the range gate melting layer contaminated [dB]

htol : float

maximum distance from the iso0 coming from model allowed to consider the range gate melting layer contaminated [m]

ml_bottom_dif_max : float

Maximum distance from the bottom of the melting layer computed in the previous time step to consider a range gate melting layer contaminated [m]

time_accu_max : float

Maximum time allowed to accumulate data from consecutive scans [s]

nml_points_min : int

minimum number of melting layer points to consider valid melting layer detection

wlength : float

length of the window to select the azimuth angles used to compute the melting layer limits at a particular azimuth [degree]

percentile_bottom, percentile_top : float [0,1]

percentile of ml points above which is considered that the bottom of the melting layer starts and the top ends

interpol : bool

Whether to interpolate the obtained results in order to get a value for each azimuth

time_nodata_allowed : float

The maximum time allowed for no data before considering the melting layer not valid [s]

refl_field, zdr_field, rhv_field, temp_field, iso0_field : str

Inputs. Field names within the radar object which represent the horizonal reflectivity, the differential reflectivity, the copolar correlation coefficient, the temperature and the height respect to the iso0 fields. A value of None for any of these parameters will use the default field name as defined in the Py-ART configuration file.

ml_field : str

Output. Field name which represents the melting layer field. A value of None will use the default field name as defined in the Py-ART configuration file.

ml_pos_field : str

Output. Field name which represents the melting layer top and bottom height field. A value of None will use the default field name as defined in the Py-ART configuration file.

temp_ref : str

the field use as reference for temperature. Can be temperature or height_over_iso0. If None, it excludes model data from the algorithm.

get_iso0 : bool

returns height w.r.t. freezing level top for each gate in the radar volume.

ml_global :

stack of previous volume data to introduce some time dependency. Its max size is controlled by the nVol parameter. It is always in (pseudo-)RHI mode.

References

Giangrande, S.E., Krause, J.M., Ryzhkov, A.V.: Automatic Designation of the Melting Layer with a Polarimetric Prototype of the WSR-88D Radar, J. of Applied Meteo. and Clim., 47, 1354-1364, doi:10.1175/2007JAMC1634.1, 2008

pyart.retrieve.melting_layer_hydroclass(radar, hydro_field=None, ml_field=None, ml_pos_field=None, iso0_field=None, force_continuity=True, dist_max=350.0, get_iso0=False)

Using the results of the hydrometeor classification by Besic et al. estimates the position of the range gates respect to the melting layer, the melting layer top and bottom height and the distance of the range gate with respect to the freezing level.

Parameters:

radar : Radar

Radar object. Must have and hydrometeor classification field

hydro_field : str

Name of the hydrometeor classification field. A value of None will use the default field name as defined in the Py-ART configuration file.

ml_field, ml_pos_field, iso0_field : str

Name of the melting layer, melting layer heightand iso0 field. A value of None for any of these parameters will use the default field names as defined in the Py-ART configuration file.

force_continuity : Bool

If True, the melting layer is forced to be continuous in range

dist_max : float

The maximum distance between range gates flagged as inside the melting layer to consider them as gates in the melting layer.

Returns:

ml_obj : radar-like object

A radar-like object containing the field melting layer height with the bottom (at range position 0) and top (at range position one) of the melting layer at each ray

ml_dict : dict

A dictionary containg the position of the range gate respect to the melting layer and metadata

iso0_dict : dict or None

A dictionary containing the distance respect to the melting layer and metadata

pyart.retrieve.quasi_vertical_profile(radar, desired_angle=None, fields=None, gatefilter=None)

Quasi Vertical Profile.

Creates a QVP object containing fields from a radar object that can be used to plot and produce the quasi vertical profile

Parameters:

radar : Radar

Radar object used.

field : string

Radar field to use for QVP calculation.

desired_angle : float

Radar tilt angle to use for indexing radar field data. None will result in wanted_angle = 20.0

Returns:

qvp : Dictonary

A quasi vertical profile object containing fields from a radar object

Other Parameters:  

gatefilter : GateFilter

A GateFilter indicating radar gates that should be excluded from the import qvp calculation

References

Troemel, S., M. Kumjian, A. Ryzhkov, and C. Simmer, 2013: Backscatter differential phase - estimation and variability. J Appl. Meteor. Clim.. 52, 2529 - 2548.

Troemel, S., A. Ryzhkov, P. Zhang, and C. Simmer, 2014: Investigations of backscatter differential phase in the melting layer. J. Appl. Meteorol. Clim. 54, 2344 - 2359.

Ryzhkov, A., P. Zhang, H. Reeves, M. Kumjian, T. Tschallener, S. Tromel, C. Simmer, 2015: Quasi-vertical profiles - a new way to look at polarimetric radar data. Submitted to J. Atmos. Oceanic Technol.

pyart.retrieve.steiner_conv_strat(grid, dx=None, dy=None, intense=42.0, work_level=3000.0, peak_relation='default', area_relation='medium', bkg_rad=11000.0, use_intense=True, fill_value=None, refl_field=None)

Partition reflectivity into convective-stratiform using the Steiner et al. (1995) algorithm.

Parameters:

grid : Grid

Grid containing reflectivity field to partition.

dx, dy : float, optional

The x- and y-dimension resolutions in meters, respectively. If None the resolution is determined from the first two axes values.

intense : float, optional

The intensity value in dBZ. Grid points with a reflectivity value greater or equal to the intensity are automatically flagged as convective. See reference for more information.

work_level : float, optional

The working level (separation altitude) in meters. This is the height at which the partitioning will be done, and should minimize bright band contamination. See reference for more information.

peak_relation : ‘default’ or ‘sgp’, optional

The peakedness relation. See reference for more information.

area_relation : ‘small’, ‘medium’, ‘large’, or ‘sgp’, optional

The convective area relation. See reference for more information.

bkg_rad : float, optional

The background radius in meters. See reference for more information.

use_intense : bool, optional

True to use the intensity criteria.

fill_value : float, optional

Missing value used to signify bad data points. A value of None will use the default fill value as defined in the Py-ART configuration file.

refl_field : str, optional

Field in grid to use as the reflectivity during partitioning. None will use the default reflectivity field name from the Py-ART configuration file.

Returns:

eclass : dict

Steiner convective-stratiform classification dictionary.

References

Steiner, M. R., R. A. Houze Jr., and S. E. Yuter, 1995: Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data. J. Appl. Meteor., 34, 1978-2007.

pyart.retrieve.velocity_azimuth_display(radar, vel_field=None, z_want=None, gatefilter=None)

Velocity azimuth display.

Creates a VAD object containing U Wind, V Wind and height that can then be used to plot and produce the velocity azimuth display.

Parameters:

radar : Radar

Radar object used.

vel_field : string, optional

Velocity field to use for VAD calculation.

z_want : array, optional

Heights for where to sample vads from. None will result in np.linespace(0, 10000, 100).

gatefilter : GateFilter, optional

A GateFilter indicating radar gates that should be excluded from the import vad calculation.

Returns:

vad: HorizontalWindProfile

A velocity azimuth display object containing height, speed, direction, u_wind, v_wind from a radar object.