pyart.retrieve#

Description

Radar retrievals.

Functions

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.

composite_reflectivity(radar[, field, ...])

Composite Reflectivity

compute_cdr(radar[, rhohv_field, zdr_field, ...])

Computes the Circular Depolarization Ratio.

compute_l(radar[, rhohv_field, l_field])

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

compute_noisedBZ(nrays, noisedBZ_val, ...[, ...])

Computes noise in dBZ from reference noise value.

compute_snr(radar[, refl_field, ...])

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

dealias_spectra(the_spectra, vel_bins, ...)

Dealias a spectra.

est_rain_rate_a(radar[, alpha, beta, ...])

Estimates rainfall rate from specific attenuation using alpha power law.

est_rain_rate_hydro(radar[, alphazr, ...])

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

est_rain_rate_kdp(radar[, alpha, beta, ...])

Estimates rainfall rate from kdp using alpha power law.

est_rain_rate_z(radar[, alpha, beta, ...])

Estimates rainfall rate from reflectivity using a power law.

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_zkdp(radar[, alphaz, betaz, ...])

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

est_rain_rate_zpoly(radar[, refl_field, ...])

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

fetch_radar_time_profile(sonde_dset, radar)

Extract the correct profile from a interpolated sonde.

get_freq_band(freq)

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

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.

hydroclass_semisupervised(radar[, ...])

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

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. (2012).

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.

map_profile_to_gates(profile, heights, radar)

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

quasi_vertical_profile(radar[, ...])

Quasi Vertical Profile.

spectra_moments(radar)

Retrieves the radar moments using a spectra radar object.

steiner_conv_strat(grid[, dx, dy, intense, ...])

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

texture_of_complex_phase(radar[, ...])

Calculate the texture of the differential phase field.

vad_browning(radar, velocity[, z_want, ...])

Velocity azimuth display.

vad_michelson(radar[, vel_field, z_want, ...])

Velocity azimuth display.