List of pyrad datasets separated by dataset type#

VOL#

ATTENUATION#

description

Computes specific attenuation and specific differential attenuation using the Z-Phi method and corrects reflectivity and differential reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc”, and,

“ZDR” or “ZDRc”, and

“TEMP” or “H_ISO0” (Optional)
ATT_METHODfloat. Dataset keyword: The attenuation estimation method used. One of the following:

ZPhi, Philin. Default ZPhi
fzlfloat. 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.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used

to compute the freezing level, if no temperature field name is

specified, if the temperature field is in the radar object or if

no freezing_level is explicitely defined.

returns

new_datasetdict: dictionary containing the output fields “Ah” (spec. attenuation),

“PIA” (path-integrated attenuation) and “dBZc” (corr. refl.)

AZI_AVG#

description

Averages radar data in azimuth obtaining and RHI as a result

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
anglefloat or None. Dataset keyword: The center angle to average. If not set or set to -1 all

available azimuth angles will be used
delta_azifloat. Dataset keyword: The angle span to average. If not set or set to -1 all the

available azimuth angles will be used
avg_typestr. Dataset keyword: Average type. Can be mean or median. Default mean
nvalid_minint. Dataset keyword: the minimum number of valid points to consdier the average valid.

Default 1
lin_transdict or None: A dictionary specifying which data types have to be transformed

in linear units before averaging

returns

new_datasetdict: dictionary containing the gridded data

MOVING_AZI_AVG#

description

Applies a moving azimuthal average to the radar data

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
delta_azifloat. Dataset keyword: The angle span to average. Default 20
avg_typestr. Dataset keyword: Average type. Can be mean or median. Default mean
nvalid_minint. Dataset keyword: the minimum number of valid points to consdier the average valid.

Default 1
lin_transdict or None: A dictionary specifying which data types have to be transformed

in linear units before averaging

returns

new_datasetdict: dictionary containing the gridded data

BIAS_CORRECTION#

description

Corrects a bias on the data

[Source]

parameters

datatypestring. Dataset keyword: The data type to correct for bias, can be any datatype supported by pyrad
biasfloat. Dataset keyword: The bias to be corrected [dB]. Default 0

returns

new_datasetdict: dictionary containing the output field, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

BIRDS_ID#

description

identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Birds

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be

“dBZ” or “dBuZ”, and,

“ZDR” or “ZDRu”, and,

“RhoHV” or “uRhoHV”

returns

new_datasetdict: dictionary containing the output field “echoID”

BIRD_DENSITY#

description

Computes the bird density from the volumetric reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“eta_h” or “eta_v” (volumetric reflectivities)
sigma_birdfloat. Dataset keyword: The bird radar cross section

returns

new_datasetdict: dictionary containing the output field “bird_density”

CCOR#

description

Computes the Clutter Correction Ratio, i.e. the ratio between the signal without Doppler filtering and the signal with Doppler filtering

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZv”, and,

“dBuZ”, or “dBuZV”

returns

new_datasetdict: dictionary containing the output field “CCORh” or

“CCORv” (if vertical reflectivities were provided)

CDF#

description

Collects the fields necessary to compute the Cumulative Distribution Function

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be

“echoID” (if not provided, no clutter filtering is possible), and,

“hydro” (if not provided, no hydro filtering is possible), and,

“VIS” (if not provided no blocked gate filtering is possible), and,

any other field that will be used to compute CDF

returns

new_datasetdict: dictionary containing the output

CDR#

description

Computes approximation of Circular Depolarization Ratio

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“RhoHV” or “uRhoHV” or “RhoHVu”, and,

“ZDR” or “ZDRc”

returns

new_datasetdict: dictionary containing the output field “CDR”

CLT_TO_SAN#

description

Converts clutter exit code from rad4alp into pyrad echo ID

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be “CLT”

returns

new_datasetdict: dictionary containing the output field “echoID”

ICON_LOOKUP#

description

Gets icon data and put it in radar coordinates using look up tables computed or loaded when initializing

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad
lookup_tableint. Dataset keyword: if set a pre-computed look up table for the icon coordinates is

loaded. Otherwise the look up table is computed taking the first

radar object as reference
regular_gridint. 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 icon field in

memory to the current radar grid
icon_typestr. Dataset keyword: name of the icon field to process. Default TEMP
icon_variableslist of strings. Dataset keyword: Py-art name of the icon fields. Default temperature

returns

new_datasetdict: dictionary containing the output fields corresponding to icon_variables

DEM#

description

Gets DEM data and put it in radar coordinates

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad
keep_in_memoryint. 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_gridint. 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_fieldstr. Dataset keyword: name of the DEM field to process
demfilestr. Dataset keyword: Name of the file containing the DEM data

returns

new_datasetdict: dictionary containing the output field with name corresponding

to dem_field

DEALIAS_FOURDD#

description

Dealiases the Doppler velocity field using the 4DD technique from Curtis and Houze, 2001

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain

“V” or “Vc”
filtint. Dataset keyword: Flag controlling Bergen and Albers filter, 1 = yes, 0 = no.
signint. 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.

returns

new_datasetdict: dictionary containing the output field “dealV” or “dealVc” (if Vc was provided)

DEALIAS_REGION#

description

Dealiases the Doppler velocity field using a region based algorithm

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“V” or “Vc”
interval_splitsint, 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_rayint, 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.
centeredbool, 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.
nyquist_velfloat, optional: Nyquist velocity of the aquired radar velocity.

Usually this parameter is provided in the

Radar object intrument_parameters. If it is not available it can

be provided as a keyword here.

returns

new_datasetdict: dictionary containing the output field “dealV” or “dealVc” (if Vc was provided)

DEALIAS_UNWRAP#

description

Dealiases the Doppler velocity field using multi-dimensional phase unwrapping

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“V” or “Vc”
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.

returns

new_datasetdict: dictionary containing the output field “dealV” or “dealVc” (if Vc was provided)

DOPPLER_VELOCITY#

description

Compute the Doppler velocity from the spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“sdBZ” or “sdBZv” or “sdBuZ” or “sdBuZv”

returns

new_datasetdict: dictionary containing the output field

“V” if “sdBZ” was provided

“Vv” if “sdBZv” was provided

“Vu” if “sdBuZ” was provided

DOPPLER_VELOCITY_IQ#

description

Compute the Doppler velocity from the spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”
directionstr: The convention used in the Doppler mean field. Can be

negative_away or negative_towards

returns

new_datasetdict: dictionary containing the output field “V”

(if IQhhADU was provided) or “Vv” (if IQvvADU was provided)

DOPPLER_WIDTH#

description

Compute the Doppler spectrum width from the spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“sdBZ” or “sdBZv” or “sdBuZ” or “sdBuZv”

returns

new_datasetdict: dictionary containing the output field

“W” if “sdBZ” was provided

“Wv” if “sdBZv” was provided

“Wu” if “sdBuZ” was provided

DOPPLER_WIDTH_IQ#

description

Compute the Doppler spectrum width from the spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”
subtract_noiseBool: If True noise will be subtracted from the signals
lagint: Time lag used in the denominator of the computation

returns

new_datasetdict: dictionary containing the output field “W” (if IQhhADU was provided),

or “Wv” (if IQvvADU was provided)

ECHO_FILTER#

description

Masks all echo types that are not of the class specified in keyword echo_type

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be

“echoID” at minimum, as well as any other fields

that will be echo filtered (e.g. dBZ, ZDR)
echo_typeint or list of ints: The type of echoes to keep: 1 noise, 2 clutter, 3 precipitation.

Default 3

returns

new_datasetdict: dictionary containing the output field, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

FIELDS_DIFF#

description

Computes the field difference between RADAR001 and radar002, i.e. RADAR001-RADAR002. Assumes both radars have the same geometry

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types for each radar,

Any datatype supported by pyrad is supported

returns

new_datasetdict: dictionary containing a radar object containing the field differences

FIXED_RNG#

description

Obtains radar data at a fixed range

[Source]

parameters

datatypelist of strings. Dataset keyword: The fields we want to extract
rngfloat. Dataset keyword: The fixed range [m]
RngTolfloat. Dataset keyword: The tolerance between the nominal range and the radar range
ele_min, ele_max, azi_min, azi_maxfloats. Dataset keyword: The azimuth and elevation limits of the data [deg]

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

FIXED_RNG_SPAN#

description

For each azimuth-elevation gets the data within a fixed range span and computes a user-defined statistic: mean, min, max, mode, median

[Source]

parameters

datatypelist of strings. Dataset keyword: The fields we want to extract
rmin, rmaxfloat. Dataset keyword: The range limits [m]
ele_min, ele_max, azi_min, azi_maxfloats. Dataset keyword: The azimuth and elevation limits of the data [deg]

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

GATEFILTER#

description

filters out all available moments based on specified upper/lower bounds for moments based on the Py-ART gatefilter. Every value below upper bound or above upper bound will be filtered. To ignore lower/upper bound enter an impossible value such as -9999 or 9999.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any any data type supported by

pyrad, the number of datatypes must match the lower and upper bounds

dimensions
lower_boundslist of float: The list of lower bounds for every input data type
upper_boundslist of float: The list of upper bounds for every input data type

returns

new_datasetdict: dictionary containing the output field, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

GECSX#

description

Computes ground clutter RCS, radar visibility and many others using the GECSX algorithmn translated from IDL into python

[Source]

parameters

datatypelist of string. Dataset keyword: arbitrary data type supported by pyrad
range_discretizationfloat. Dataset keyword: Range discretization used when computing the Cartesian visibility field

the larger the better but the slower the processing will be
az_discretizationfloat. Dataset keyword: Azimuth discretization used when computing the Cartesian visibility

field, the larger the better but the slower the processing will be
kefloat. Dataset keyword: Equivalent earth-radius factor used in the computation of the radar

beam refraction
atm_attfloat. Dataset keyword: One-way atmospheric refraction in db / km
mosotti_kwfloat. Dataset keyword: Clausius-Mosotti factor K, depends on material (water) and wavelength

for water = sqrt(0.93)
raster_oversamplingint. Dataset keyword: The raster resolution of the DEM should be smaller than

the range resolution of the radar (defined by the pulse length).

If this is not the case, this keyword can be set to increase the

raster resolution. The values for the elevation, sigma naught,

visibility are repeated. The other values are recalculated.

Values for raster_oversampling:

0 or undefined: No oversampling is done

1: Oversampling is done. The factor N is automatically calculated

such that 2*dx/N < pulse length

2 or larger: Oversampling is done with this value as N
sigma0_methodstring. Dataset keyword: Which estimation method to use, either ‘Gabella’ or ‘Delrieu’
clipint. Dataset keyword: If set to true, the provided DEM will be clipped to the extent

of the polar radar domain. Increases computation speed a lot but

Cartesian output fields will be available only over radar domain

returns

new_datasetlist of dict: list of dictionaries containing the polar data output and the

Cartesian data output in this order

The first dictionary (polar) contains the following fields:

“rcs_clutter”, “dBm_clutter”, “dBZ_clutter” and “visibility_polar”

The second dictionary (cart) contains the following fields:

“bent_terrain_altitude”, “terrain_slope”, “terrain_aspect”,

“elevation_angle”, “min_vis_elevation”, “min_vis_altitude”,

“incident_angle”, “sigma_0”, “effective_area”

HYDROCLASS#

description

Classifies precipitation echoes

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“RhoHV”, or “uRhoHV”, or “RhoHVc”, and,

“KDP”, or “KDPc”, and,

“TEMP” or “H_ISO0” (optional)
HYDRO_METHODstring. Dataset keyword: The hydrometeor classification method. One of the following:

SEMISUPERVISED, UKMO
centroids_filestring or None. Dataset keyword: Used with HYDRO_METHOD SEMISUPERVISED. The name of the .csv file

that stores the centroids. The path is given by

[configpath]/centroids_hydroclass/

If None is provided default centroids are going to be used
compute_entropybool. Dataset keyword: Used with HYDRO_METHOD SEMISUPERVISED. If true the entropy is

computed and the field hydroclass_entropy is output
output_distancesbool. Dataset keyword: Used with HYDRO_METHOD SEMISUPERVISED. 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
vectorizebool. Dataset keyword: Used with HYDRO_METHOD SEMISUPERVISED. If true a vectorized

version of the algorithm is used
weightsarray of floats. Dataset keyword: Used with HYDRO_METHOD SEMISUPERVISED. The list of weights given

to each variable
hydropathstring. Dataset keyword: Used with HYDRO_METHOD UKMO. Directory of the UK MetOffice

hydrometeor classification code
mf_dirstring. Dataset keyword: Used with HYDRO_METHOD UKMO. Directory where the UK MetOffice

hydrometeor classification membership functions are stored
ml_depth: float. Dataset keyword: Used with HYDRO_METHOD UKMO. Depth of the melting layer [km].

Default 500.
perturb_ml_depth: float. Dataset keyword: Used with HYDRO_METHOD UKMO. if specified, the depth of the

melting layer can be varied by +/- this value [km], allowing a

less-rigidly defined melting layer. Default 0.
fzl: float or None. Dataset keyword: If desired, a single freezing level

height can be specified for the entire PPI domain. This will be used

only if no temperature field is available.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used to

compute the freezing level, if no temperature field name is specified,

if the temperature field is not in the radar object or if no

fzl is explicitely defined.
use_dualpol: Bool. Dataset keyword: Used with HYDRO_METHOD UKMO. If false no radar data is used and

the classification is performed using temperature information

only. Default True
use_temperature: Bool. Dataset keyword: Used with HYDRO_METHOD UKMO. If false no temperature information

is used and the classification is performed using radar data only.

Default True
use_interpolation: Bool. Dataset keyword: Used with HYDRO_METHOD UKMO. If True gaps in the classification

are filled using a nearest-neighbour interpolation. Default False
map_to_semisupervised: Bool. Dataset keyword: Used with HYDRO_METHOD UKMO. If True the output is map to the same

categories as the semi-supervised classification. Default True
append_all_fields: Bool. Dataset keyword: Used with HYDRO_METHOD UKMO. If True auxiliary fields such as

confidence and probability for each class are going to be added to

the output

returns

new_datasetdict: dictionary containing the output fields “hydro”, “entropy” (if compute_entropy is 1),

and “propAG”, “propCR”, “propLR”, “propRP”, “propRN”, “propVI”, “propWS”, “propMH”,

“propIH” (if output_distances is 1)

HZT#

description

Gets iso0 degree data in HZT format and put it in radar coordinates

[Source]

parameters

metranet_read_libstr. Global keyword: Type of METRANET reader library used to read the data.

Can be ‘C’ or ‘python’
datatypestring. Dataset keyword: arbitrary data type supported by pyrad
keep_in_memoryint. Dataset keyword: if set keeps the icon data dict, the icon coordinates dict and

the icon field in radar coordinates in memory
regular_gridint. 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 icon field if the icon

data has not changed
icon_typestr. Dataset keyword: name of the icon field to process. Default TEMP
icon_variableslist of strings. Dataset keyword: Py-art name of the icon fields. Default temperature

returns

new_datasetdict: dictionary containing the output fields corresponding to

icon_variables

HZT_LOOKUP#

description

Gets HZT data and put it in radar coordinates using look up tables computed or loaded when initializing

[Source]

parameters

metranet_read_libstr. Global keyword: Type of METRANET reader library used to read the data.

Can be ‘C’ or ‘python’
datatypestring. Dataset keyword: arbitrary data type supported by pyrad
lookup_tableint. Dataset keyword: if set a pre-computed look up table for the icon coordinates is

loaded. Otherwise the look up table is computed taking the first

radar object as reference
regular_gridint. 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 icon field in

memory to the current radar grid

returns

new_datasetdict: dictionary containing the output field HISO0

ISO0_GRIB#

description

Gets iso0 degree data in GRIB format and put it in radar coordinates. This function is meant to process data received from the MeteoFrance NWP model. It can output the height over the iso0 of each gate or the iso0 height at each gate

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad
time_interpbool. Dataset keyword: whether to perform an interpolation in time between consecutive

model outputs. Default True
voltype: str. Dataset keyword: The type of data to output. Can be H_ISO0 or HZT. Default H_ISO0

returns

new_datasetdict: dictionary containing the output field H_ISO0

ISO0_MF#

description

Gets iso0 degree data in text format and put it in radar coordinates. This function is meant to process data received from the MeteoFrance NWP model. The model provides a maximum of 9 points at 0.5 degree lat/lon spacing surrounding a given radar. If a point is not provided it means that the iso0 for that point is at or below the ground level. Out of these points a single reference iso-0 is obtained according to the user defined iso0 statistic.

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by prad
iso0_statisticstr. Dataset keyword: The statistic used to weight the iso0 points. Can be avg_by_dist,

avg, min, max

returns

new_datasetdict: dictionary containing the output field “H_ISO0”

KDP_LEASTSQUARE_1W#

description

Computes specific differential phase using a piecewise least square method

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“PhiDP” or “PhiDPc” or “uPhiDP”
rwindfloat. Dataset keyword: The length of the segment for the least square method [m].

Default 6000.
vectorizebool. Dataset keyword: Whether to vectorize the KDP processing. Default false

returns

new_datasetdict: dictionary containing the output field “KDPc”

KDP_LEASTSQUARE_2W#

description

Computes specific differential phase using a piecewise least square method

[Source]

parameters

The input data types, must contain,: “PhiDP” or “PhiDPc” or “uPhiDP”, and,

“dBZ” or “dBZc”
rwindsfloat. Dataset keyword: The length of the short segment for the least square method [m].

Default 2000.
rwindlfloat. Dataset keyword: The length of the long segment for the least square method [m].

Default 6000.
Zthrfloat. Dataset keyword: The threshold defining which estimated data to use [dBZ]
vectorizeBool. Dataset keyword: Whether to vectorize the KDP processing. Default false

returns

new_datasetdict: dictionary containing the output field “KDPc”

KEEP_ROI#

description

keep only data within a region of interest and mask anything else

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
trtfilestr. Dataset keyword: TRT file from which to extract the region of interest
time_tolfloat. Dataset keyword: Time tolerance between the TRT file date and the nominal radar

volume time
lon_roi, lat_roifloat array. Dataset keyword: latitude and longitude positions defining a region of interest
alt_min, alt_maxfloat. Dataset keyword: Minimum and maximum altitude of the region of interest. Can be

None
cercleboolean. Dataset keyword: If True the region of interest is going to be defined as a cercle

centered at a particular point. Default False
lon_centre, lat_centreFloat. Dataset keyword: The position of the centre of the cercle. If None, that of the

radar will be used
rad_cercleFloat. Dataset keyword: The radius of the cercle in m. Default 1000.
res_cercleint. Dataset keyword: Number of points used to define a quarter of cercle. Default 16
boxboolean. Dataset keyword: If True the region of interest is going to be defined by a

rectangle
lon_point, lat pointFloat: The position of the point of rotation of the box. If None the

position of the radar is going to be used
rotationfloat: The angle of rotation. Positive is counterclockwise from North in

deg. Default 0.
we_offset, sn_offsetfloat: west-east and south-north offset from rotation position in m.

Default 0
we_length, sn_lengthfloat: west-east and south-north rectangle lengths in m. Default 1000.
originstr: origin of rotation. Can be center: center of the rectangle or

mid_south. East-west mid-point at the south of the rectangle.

Default center
use_latlonBool. Dataset keyword: If True the coordinates used to find the radar gates within the

ROI will be lat/lon. If false it will use Cartesian Coordinates

with origin the radar position. Default True

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

L#

description

Computes L parameter (logarithmic cross-correlation ratio)

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“RhoHV” or “RhoHVc” or “uRhoHV”

returns

new_datasetdict: dictionary containing the output field “L”

MEAN_PHASE_IQ#

description

Computes the mean phase from the horizontal or vertical IQ data

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”

returns

new_datasetdict: dictionary containing the output field “MPH” (mean_phase)

NCVOL#

description: Dummy function that allows to save the entire radar object

[Source]

parameters

returns

new_datasetdict: dictionary containing the output

NOISE_POWER#

description

Computes the noise power from the spectra

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“ShhADU” or “SvvADU” or “ShhADUu” or “SvvADUu”
unitsstr: The units of the returned signal. Can be ‘ADU’, ‘dBADU’ or ‘dBm’
navgint: Number of spectra averaged
rminint: Range from which the data is used to estimate the noise
nnoise_minint: Minimum number of samples to consider the estimated noise power

valid

returns

new_datasetdict: dictionary containing the output field

“NdBADUh” or “NdBADUv”, or

“NdBmh” or “NdBmv”, or

“Nh” or “Nv”

depending on which input datatype and units were provided

OUTLIER_FILTER#

description

filters out gates which are outliers respect to the surrounding

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any any data type supported by pyrad
thresholdfloat. 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
nbint. Dataset keyword: The number of neighbours (to one side) to analyse. i.e. 2 would

correspond to 24 gates
nb_minint. Dataset keyword: Minimum number of neighbouring gates to consider the examined gate

valid
percentile_min, percentile_maxfloat. Dataset keyword: gates below (above) these percentiles (computed over the sweep) are

considered potential outliers and further examined

returns

new_datasetdict: dictionary containing the output, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

PHIDP0_CORRECTION#

description

corrects phidp of the system phase

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc” or “uPhiDP”
rminfloat. Dataset keyword: The minimum range where to look for valid data [m]. Default 1000.
rmaxfloat. Dataset keyword: The maximum range where to look for valid data [m]. Default 50000.
rcellfloat. Dataset keyword: The length of a continuous cell to consider it valid precip [m].

Default 1000.
Zminfloat. Dataset keyword: The minimum reflectivity [dBZ]. Default 20.
Zmaxfloat. Dataset keyword: The maximum reflectivity [dBZ]. Default 40.

returns

new_datasetdict: dictionary containing the output field “PhiDPc”

PHIDP0_ESTIMATE#

description

estimates the system differential phase offset at each ray

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc” or “uPhiDP”
rminfloat. Dataset keyword: The minimum range where to look for valid data [m]
rmaxfloat. Dataset keyword: The maximum range where to look for valid data [m]
rcellfloat. Dataset keyword: The length of a continuous cell to consider it valid precip [m]
Zminfloat. Dataset keyword: The minimum reflectivity [dBZ]
Zmaxfloat. Dataset keyword: The maximum reflectivity [dBZ]

returns

new_datasetdict: dictionary containing the output fields “PhiDP0” (system diff. phase) and

“PhiDP0_bin” (first gate diff. phase)

PHIDP_KDP_KALMAN#

description

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

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“PhiDP” or “PhiDPc” or “uPhiDP”
parallelboolean. Dataset keyword: if set use parallel computing
get_phidpboolean. Datset keyword: if set the PhiDP computed by integrating the resultant KDP is

added to the radar field
frequencyfloat. 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

returns

new_datasetdict: dictionary containing the output field “PhiDPc” and “KDPc”

PHIDP_KDP_LP#

description

Estimates PhiDP and KDP using a linear programming algorithm. This method only retrieves data in rain (i.e. below the melting layer). The method has 3 steps: PhiDP unfolding (including clutter suppression), Processing PhiDP using the LP algorithm, Obtaining KDP by convoluting PhiDP with a Sobel window. Required inputs for the LP algorithm are PsiDP and reflectivity. RhoHV and SNR are used for the clutter suppression in the PhiDP unfolding step (note that SNR is used instead of Normalized Coherent Power used by the original algorithm). If they are not provided a gate_filter based on the values of reflectivity is used instead. Freezing level height can be retrieved from iso-0 or temperature fields, from radio sounding or set by the user.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc” or “uPhiDP”, and,

“RhoHV” or “RhoHVc”, (Optional, used when min_rhv is specified) and,

“SNRh” (Optional, used when min_snr is specified), and

“TEMP” or “H_ISO0” (Optional)
fzlfloat. Dataset keyword: The freezing level height [m]. Default 2000.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used

to compute the freezing level, if no temperature field name is

specified, if the temperature field is not in the radar object or

if no freezing_level is explicitely defined.
ml_thicknessfloat. Dataset keyword: The melting layer thickness in meters. Default 700.
beamwidthfloat. 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
LP_solverstr.: The LP solver to use. Can be pyglpk, cvxopt, cylp, cylp_mp.

Default cvxopt
procint: Number of worker processes. Only used when LP_solver is cylp_mp.

Default 1
min_snr, min_rhvfloat: Minimum SNR and RhoHV. Used to filter out non-meteorological

echoes when performing the unfolding of the differential phase.

Default 10 and 0.6
sys_phasefloat: Default system differential phase offset in deg. Default 0.
overide_sys_phasebool: If True the dynamic sys_phase not computed and the default

sys_phase is used. If false a dynamic sys_phase is computed. If

no dynamic value is found the default sys_phase is used.

Default False
first_gate_syspint: First gate to use when determining the system differential phase

offset. Default 0
nowrapint or None: Gate number where to begin the phase unwrapping. None will unwrap

from gate 0. Default None
ncptsint: Minimum number of continuous valid PhiDP points. Segments below

this number or starting at a gate below this number are going to

be excluded from the unfolding. Default 2.
z_biasfloat: reflectivity bias. Default 0 dBZ
low_z, high_zfloat: mininum and maximum reflectivity values. Values beyond this range

are going to be set to this range limits. The modified

reflectivity is used in the LP algorithm. Default 10 and 53 dBZ
min_phidpfloat: minimum differential phase. PhiDP values below this threshold are

going to be set to the threshold values. The modified PhiDP is

used in the LP algorithm. Default 0.1 deg
docint: Number of gates to doc at the end of the ray. Used in the LP

algorithm. Default 0
self_constfloat: selfconsistency factor. Used in the LP algorithm. Default 60000
coeffloat: Exponent linking Z to KDP in selfconsistency. Used in the LP

algorithm. kdp = (10**(0.1z))**coef. Default 0.914
window_lenint: Length of Sobel Window applied to PhiDP field before computing

KDP. Default 35

returns

new_datasetdict: dictionary containing the output field “PhiDPc” and “KDPc”

PHIDP_KDP_VULPIANI#

description

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

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“PhiDP” or “PhiDPc” or “uPhiDP”
rwindfloat. Dataset keyword: The length of the segment [m]. Default 2000.
n_iterint. Dataset keyword: number of iterations. Default 3.
interpboolean. Dataset keyword: if set non valid values are interpolated using neighbouring valid

values. Default 0 (False)
parallelboolean. Dataset keyword: if set use parallel computing. Default 1 (True)
get_phidpboolean. Datset keyword: if set the PhiDP computed by integrating the resultant KDP is

added to the radar field. Default 0 (False)
frequencyfloat. 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

returns

new_datasetdict: dictionary containing the output field “PhiDPc” and “KDPc”

PHIDP_KDP_MAESAKA#

description

Estimates PhiDP and KDP using the method by Maesaka. This method only retrieves data in rain (i.e. below the melting layer)

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc” or “uPhiDP”, and

“TEMP” or “H_ISO0” (Optional)
rminfloat. Dataset keyword: The minimum range where to look for valid data [m]. Default 1000.
rmaxfloat. Dataset keyword: The maximum range where to look for valid data [m]. Default 50000.
rcellfloat. Dataset keyword: The length of a continuous cell to consider it valid precip [m].

Default 1000.
Zminfloat. Dataset keyword: The minimum reflectivity [dBZ]. Default 20
Zmaxfloat. Dataset keyword: The maximum reflectivity [dBZ]. Default 40
fzlfloat. Dataset keyword: The freezing level height [m]. Default 2000.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used

to compute the freezing level, if no temperature field name is

specified, if the temperature field isin the radar object or if no

freezing_level is explicitely defined.
ml_thicknessfloat. Dataset keyword: The melting layer thickness in meters. Default 700.
beamwidthfloat. 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

returns

new_datasetdict: dictionary containing the output field “PhiDPc” and “KDPc”

PHIDP_SMOOTH_1W#

description

corrects phidp of the system phase and smoothes it using one window

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc” or “uPhiDP”
rminfloat. Dataset keyword: The minimum range where to look for valid data [m]. Default 1000.
rmaxfloat. Dataset keyword: The maximum range where to look for valid data [m]. Default 50000.
rcellfloat. Dataset keyword: The length of a continuous cell to consider it valid precip [m].

Default 1000.
rwindfloat. Dataset keyword: The length of the smoothing window [m]. Default 6000.
Zminfloat. Dataset keyword: The minimum reflectivity [dBZ]. Default 20.
Zmaxfloat. Dataset keyword: The maximum reflectivity [dBZ]. Default 40.

returns

new_datasetdict: dictionary containing the output field “PhiDPc”

PHIDP_SMOOTH_2W#

description

corrects phidp of the system phase and smoothes it using one window

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBZc”, and,

“PhiDP” or “PhiDPc” or “uPhiDP”
rminfloat. Dataset keyword: The minimum range where to look for valid data [m]
rmaxfloat. Dataset keyword: The maximum range where to look for valid data [m]
rcellfloat. Dataset keyword: The length of a continuous cell to consider it valid precip [m]
rwindsfloat. Dataset keyword: The length of the short smoothing window [m]
rwindlfloat. Dataset keyword: The length of the long smoothing window [m]
Zminfloat. Dataset keyword: The minimum reflectivity [dBZ]
Zmaxfloat. Dataset keyword: The maximum reflectivity [dBZ]
Zthrfloat. Dataset keyword: The threshold defining wich smoothed data to used [dBZ]

returns

new_datasetdict: dictionary containing the output field “PhiDPc”

POL_VARIABLES#

description

Computes the polarimetric variables from the complex spectra

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

either a combination of signal and noise

(“ShhADU” and “SvvADU”) or (“ShhADUu” and “SvvADUu”), and,

(“sNADUh” and “sNADUv”), or

the power signal

(“sPhhADU” and “sPvvADU”) or (“sPhhADUu” and “sPvvADUu”), and

(“sRhoHV” or “sRhoHVu”)
subtract_noiseBool: If True noise will be subtracted from the signal. Default False
smooth_windowint or None: Size of the moving Gaussian smoothing window. If none no smoothing

will be applied. Default None
variableslist of str: list of variables to compute. Default dBZ

returns

new_datasetdict: dictionary containing the all outputs fields, that correspond to the

specified “variables” keyword

POL_VARIABLES_IQ#

description

Computes the polarimetric variables from the IQ data

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU”, “IQvvADU”, “IQNADUh” and “IQNADUv”
subtract_noiseBool: If True noise will be subtracted from the signal
lagint: The time lag to use in the estimators
directionstr: The convention used in the Doppler mean field. Can be

negative_away or negative_towards
variableslist of str: list of variables to compute. Default dBZ
phase_offsetfloat. Dataset keyword: The system differential phase offset to remove

returns

new_datasetdict: dictionary containing the output fields corresponding to the specified

“variables”

“”

PWR#

description

Computes the signal power in dBm

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBuZ” or “dBZc” or “dBuZc” or “dBZv” or “dBuZv” or “dBuZvc”
mflossh, mflossvfloat. 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, radconstvfloat. Dataset keyword: The horizontal (vertical) channel radar constant. If None it will

be obtained from the attribute radar_calibration of the radar

object
lrxh, lrxvfloat. Global keyword: The horizontal (vertical) receiver losses from the antenna feed to

the reference point. [dB] positive value. Default 0
lradomeh, lradomevfloat. Global keyword: The 1-way dry radome horizontal (vertical) channel losses.

[dB] positive value. Default 0.
attgfloat. 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.

returns

new_datasetdict: dictionary containing the output field “dBm” or “dBmv” (if

vert. refl. was provided)

RADAR_RESAMPLING#

description: Resamples the radar data to mimic another radar with different geometry and antenna pattern

[Source]

parameters

returns

new_datasetdict: dictionary containing the new radar

RADIAL_NOISE_HS#

description

Computes the radial noise from the signal power using the Hildebrand and Sekhon 1974 method

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“dBm” or “dBmv”
rminfloat. Dataset keyword: The minimum range from which to start the computation
nbins_minint. Dataset keyword: The minimum number of noisy gates to consider the estimation valid
max_std_pwrfloat. Dataset keyword: The maximum standard deviation of the noise power to consider the

estimation valid
get_noise_posbool. Dataset keyword: If True a field flagging the position of the noisy gets will be

returned

returns

new_datasetdict: dictionary containing the output field “NdBmh” and “noise_pos_h” or

“NdBmh” and “noise_pos_v” (if vert. refl. were provided)

RADIAL_NOISE_IVIC#

description

Computes the radial noise from the signal power using the Ivic 2013 method

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“dBm” or “dBmv”
npulses_rayint: 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_posbool: If true an additional field with gates containing noise according

to the algorithm is produced

returns

new_datasetdict: dictionary containing the output field “NdBmh” and “noise_pos_h” or

“NdBmh” and “noise_pos_v” (if vert. refl. were provided)

RADIAL_VELOCITY#

description

Estimates the radial velocity respect to the radar from the wind velocity

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain

WIND_SPEED, and,

WIND_DIRECTION, and,

wind_vel_v
latitude, longitudefloat: arbitrary coordinates [deg] from where to compute the radial

velocity. If any of them is None it will be the radar position
altitudefloat: arbitrary altitude [m MSL] from where to compute the radial

velocity. If None it will be the radar altitude

returns

new_datasetdict: dictionary containing the output field “V”

RAINRATE#

description

Estimates rainfall rate from polarimetric moments

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

If RR_METHOD == “Z” or “ZPoly”:

“dBZ” or “dBZc”

If RR_METHOD == “KDP”:

“KDP” or “KDPc”

If RR_METHOD == “A”:

“Ah” or “Ahc”

If RR_METHOD == “ZKDP”:

“dBZ” or “dBZc”, and,

“KDP” or “KDPc”

IF RR_METHOD == “ZA”:

“dBZ” or “dBZc”, and,

“Ah” or “Ahc”

IF RR_METHID == “hydro”:

“dBZ” or “dBZc”, and,

“Ah” or “Ahc”, and,

“hydro”
RR_METHODstring. Dataset keyword: The rainfall rate estimation method. One of the following:

Z, ZPoly, KDP, A, ZKDP, ZA, hydro
alpha, betafloat: 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, betazfloat: factor and exponent of the R-Z power law R = alpha*Z^Beta.

Default value (0.0376, 0.6112)
alphazr, betazrfloat: 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, betazsfloat: 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, betakdpfloat: factor and exponent of the R-KDP power law R = alpha*KDP^Beta.

Default value (None, None)
alphaa, betaafloat: factor and exponent of the R-Ah power law R = alpha*Ah^Beta.

Default value (None, None)
threshfloat: 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_factorfloat: Factor by which the Z-R relation is multiplied in the melting layer

in method hydro. Default 0.6

returns

new_datasetdict: dictionary containing the output field “RR” (rain rate)

RAW#

description: Dummy function that returns the initial input data set

[Source]

parameters

returns

new_datasetdict: dictionary containing the output

REFLECTIVITY#

description

Computes reflectivity from the spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“sdBZ” or sdBZv” or “sdBuZ” or “sdBuZv”

returns

new_datasetdict: dictionary containing the output field “dBZ”, “dBZv”,

“dBuZ” or “dBuZv” depending on the provided input datatype

REFLECTIVITY_IQ#

description

Computes reflectivity from the IQ data

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”, and,

“IQNADUh” or “IQNADUv”
subtract_noiseBool: If True noise will be subtracted from the signal

returns

new_datasetdict: dictionary containing the output field

“dBZ” if “IQhhADU” and “IQNADUh” are specified

“dBZv” if “IQvvADU” and “IQNADUv” are specified

RCS#

description

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

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBuZ” or “dBZc” or “dBuZc” or “dBZv” or “dBuZv” or “dBuZvc”
kw2float. Dataset keyowrd: The water constant
pulse_widthfloat. Dataset keyowrd: The pulse width [s]
beamwidthvfloat. Global keyword: The vertical polarization antenna beamwidth [deg]. Used if input

is vertical reflectivity
beamwidthhfloat. Global keyword: The horizontal polarization antenna beamwidth [deg]. Used if input

is horizontal reflectivity

returns

new_datasetdict: dictionary containing the output field “rcs_h” or “rcs_v” (if vert. refl. were

provided)

RCS_PR#

description

Computes the radar cross-section (assuming a point target) from radar reflectivity by first computing the received power and then the RCS from it.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ” or “dBuZ” or “dBZc” or “dBuZc” or “dBZv” or “dBuZv” or “dBuZvc”
AntennaGainH, AntennaGainVfloat. 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, txpwrvfloat. 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, mflossvfloat. 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, radconstvfloat. Dataset keyword: The horizontal (vertical) channel radar constant. If None it will

be obtained from the attribute radar_calibration of the radar

object
lrxh, lrxvfloat. Global keyword: The horizontal (vertical) receiver losses from the antenna feed to

the reference point. [dB] positive value. Default 0
ltxh, ltxvfloat. 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, lradomevfloat. Global keyword: The 1-way dry radome horizontal (vertical) channel losses.

[dB] positive value. Default 0.
attgfloat. 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.

returns

new_datasetdict: dictionary containing the output field “rcs_h” or “rcs_v” (if vert. refl. were

provided)

RHOHV_CORRECTION#

description

identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Precipitation

[Source]

parameters

datatypelist of string. Dataset keyword: The data types used in the correction, it must contain

“uRhoHV”, and,

“SNRh”, and,

“ZDRc”, and,

“Nh”, and,

“Nv”

returns

new_datasetdict: dictionary containing the output field “RhoHV”

RHOHV_RAIN#

description

Keeps only suitable data to evaluate the 80 percentile of RhoHV in rain

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“RhoHV” or “RhoHVc” or “uRhoHV”, and,

“dBZ” or “dBZc”, and

“TEMP” (Optional), or

“H_ISO0” (Optional)
rminfloat. Dataset keyword: minimum range where to look for rain [m]. Default 1000.
rmaxfloat. Dataset keyword: maximum range where to look for rain [m]. Default 50000.
Zminfloat. Dataset keyword: minimum reflectivity to consider the bin as precipitation [dBZ].

Default 20.
Zmaxfloat. Dataset keyword: maximum reflectivity to consider the bin as precipitation [dBZ]

Default 40.
ml_thicknessfloat. Dataset keyword: assumed thickness of the melting layer. Default 700.
fzlfloat. 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.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used to

compute the freezing level, if no temperature field name is specified,

if the temperature field isin the radar object or if no freezing_level

is explicitely defined.

returns

new_datasetdict: dictionary containing the output field “RhoHV_rain” (RhoHV in rain)

ROI#

description

Obtains the radar data at a region of interest defined by a TRT file or by the user.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
trtfilestr. Dataset keyword: TRT file from which to extract the region of interest
time_tolfloat. Dataset keyword: Time tolerance between the TRT file date and the nominal radar

volume time
lon_roi, lat_roifloat array. Dataset keyword: latitude and longitude positions defining a region of interest
alt_min, alt_maxfloat. Dataset keyword: Minimum and maximum altitude of the region of interest. Can be

None
cercleboolean. Dataset keyword: If True the region of interest is going to be defined as a cercle

centered at a particular point. Default False
lon_centre, lat_centreFloat. Dataset keyword: The position of the centre of the cercle
rad_cercleFloat. Dataset keyword: The radius of the cercle in m. Default 1000.
res_cercleint. Dataset keyword: Number of points used to define a quarter of cercle. Default 16
lon_point, lat pointFloat: The position of the point of rotation of the box. If None the

position of the radar is going to be used
rotationfloat: The angle of rotation. Positive is counterclockwise from North in

deg. Default 0.
we_offset, sn_offsetfloat: west-east and south-north offset from rotation position in m.

Default 0
we_length, sn_lengthfloat: west-east and south-north rectangle lengths in m. Default 1000.
originstr: origin of rotation. Can be center: center of the rectangle or

mid_south. East-west mid-point at the south of the rectangle.

Default center
use_latlonBool. Dataset keyword: If True the coordinates used to find the radar gates within the

ROI will be lat/lon. If false it will use Cartesian Coordinates

with origin the radar position. Default True

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

ROI2#

description

Obtains the radar data at a region of interest defined by a TRT file or by the user. More information is kept

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
trtfilestr. Dataset keyword: TRT file from which to extract the region of interest
time_tolfloat. Dataset keyword: Time tolerance between the TRT file date and the nominal radar

volume time
lon_roi, lat_roifloat array. Dataset keyword: latitude and longitude positions defining a region of interest
alt_min, alt_maxfloat. Dataset keyword: Minimum and maximum altitude of the region of interest. Can be

None
cercleboolean. Dataset keyword: If True the region of interest is going to be defined as a cercle

centered at a particular point. Default False
lon_centre, lat_centreFloat. Dataset keyword: The position of the centre of the cercle
rad_cercleFloat. Dataset keyword: The radius of the cercle in m. Default 1000.
res_cercleint. Dataset keyword: Number of points used to define a quarter of cercle. Default 16
boxboolean. Dataset keyword: If True the region of interest is going to be defined by a

rectangle
lon_point, lat pointFloat: The position of the point of rotation of the box. If None the

position of the radar is going to be used
rotationfloat: The angle of rotation. Positive is counterclockwise from North in

deg. Default 0.
we_offset, sn_offsetfloat: west-east and south-north offset from rotation position in m.

Default 0
we_length, sn_lengthfloat: west-east and south-north rectangle lengths in m. Default 1000.
originstr: origin of rotation. Can be center: center of the rectangle or

mid_south. East-west mid-point at the south of the rectangle.

Default center
use_latlonBool. Dataset keyword: If True the coordinates used to find the radar gates within the

ROI will be lat/lon. If false it will use Cartesian Coordinates

with origin the radar position. Default True

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

SAN#

description

identifies echoes as 0: No data, 1: Noise, 2: Clutter, 3: Precipitation

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be

“dBZ” or “dBuZ”, and,

“ZDR” or “ZDRu”, and,

“RhoHV” or “uRhoHV”, and,

“PhiDP” or “uPhiDP”
wind_sizeint: Size of the moving window used to compute the ray texture

(number of gates). Default 7
max_textphi, max_textrhv, max_textzdr, max_textreflfloat: Maximum value for the texture of the differential phase, texture

of RhoHV, texture of Zdr and texture of reflectivity. Gates in

these. Default 20, 0.3, 2.85, 8
min_rhvfloat: Minimum value for the RhoHV. Default 0.6

returns

new_datasetdict: dictionary containing the output field “echoID”

SELFCONSISTENCY_BIAS#

description

Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“PhiDP” or “PhiDPc”, and,

“uRhoHV” or “RhoHV” or “RhoHVc”, and,

“TEMP” (Optional), and,

“H_ISO0” (Optional), and,

“hydro” (Optional, only used if filter_rain)
parametrizationstr: 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’.
fzlfloat. Dataset keyword: Default freezing level height. Default 2000.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used to

compute the freezing level, if no temperature field name is specified,

if the temperature field isin the radar object or if no freezing_level

is explicitely defined.
rsmoothfloat. Dataset keyword: length of the smoothing window [m]. Default 2000.
min_rhohvfloat. Dataset keyword: minimum valid RhoHV. Default 0.92
filter_rainBool. Dataset keyword: If True the hydrometeor classification is used to filter out gates

that are not rain. Default True
max_phidpfloat. Dataset keyword: maximum valid PhiDP [deg]. Default 20.
ml_thicknessfloat. Dataset keyword: Melting layer thickness [m]. Default 700.
rcellfloat. Dataset keyword: length of continuous precipitation to consider the precipitation

cell a valid phidp segment [m]. Default 15000.
dphidp_minfloat. Dataset keyword: minimum phase shift [deg]. Default 2.
dphidp_maxfloat. Dataset keyword: maximum phase shift [deg]. Default 16.
frequencyfloat. 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_radomeBool. 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_reflFloat. 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_maxFloat. 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_minint: Minimum number of valid gates to consider that the radome is wet.

Default 180
valid_gates_onlyBool: 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_pointsBool: 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
rkdpfloat: The length of the window used to compute KDP with the single

window least square method [m]. Default 6000.

returns

new_datasetdict: dictionary containing the output field “dBZ_bias” (refl. bias)

SELFCONSISTENCY_BIAS2#

description

Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“PhiDP” or “PhiDPc”, and,

“uRhoHV” or “RhoHV” or “RhoHVc”, and,

“TEMP” (Optional), and,

“H_ISO0” (Optional), and,

“hydro” (Optional, only used if filter_rain)
parametrizationstr: 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’.
fzlfloat. Dataset keyword: Default freezing level height. Default 2000.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used to

compute the freezing level, if no temperature field name is specified,

if the temperature field isin the radar object or if no freezing_level

is explicitely defined.
rsmoothfloat. Dataset keyword: length of the smoothing window [m]. Default 2000.
min_rhohvfloat. Dataset keyword: minimum valid RhoHV. Default 0.92
filter_rainBool. Dataset keyword: If True the hydrometeor classification is used to filter out gates

that are not rain. Default True
max_phidpfloat. Dataset keyword: maximum valid PhiDP [deg]. Default 20.
ml_thicknessfloat. Dataset keyword: Melting layer thickness [m]. Default 700.
rcellfloat. Dataset keyword: length of continuous precipitation to consider the precipitation

cell a valid phidp segment [m]. Default 15000.
frequencyfloat. 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_radomeBool. 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_reflFloat. 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_maxFloat. 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_minint: Minimum number of valid gates to consider that the radome is wet.

Default 180
keep_pointsBool: 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_gateBool: If True the bias per gate will be computed

returns

new_datasetdict: dictionary containing the output field “dBZ_bias” (refl. bias)

SELFCONSISTENCY_KDP_PHIDP#

description

Computes specific differential phase and differential phase in rain using the selfconsistency between Zdr, Zh and KDP

[Source]

parameters

datatypelist of strings. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“PhiDP” or “PhiDPc”, and,

“uRhoHV” or “RhoHV” or “RhoHVc”, and,

“TEMP” (Optional), and,

“H_ISO0” (Optional), and,

“hydro” (Optional, only used if filter_rain)
parametrizationstr: 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’.
rsmoothfloat. Dataset keyword: length of the smoothing window [m]. Default 2000.
min_rhohvfloat. Dataset keyword: minimum valid RhoHV. Default 0.92
filter_rainBool. Dataset keyword: If True the hydrometeor classification is used to filter out gates

that are not rain. Default True
max_phidpfloat. Dataset keyword: maximum valid PhiDP [deg]. Default 20.
ml_thicknessfloat. Dataset keyword: assumed melting layer thickness [m]. Default 700.
fzlfloat. 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.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used to

compute the freezing level, if no temperature field name is specified,

if the temperature field isin the radar object or if no freezing_level

is explicitely defined.
frequencyfloat. 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

returns

new_datasetdict: dictionary containing the output fields KDP and PhiDP

“”

SNR#

description

Computes SNR

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“dBZ” or “dBuZ” or “dBZv” or “dBuZv”, and,

“Nh” or “Nv”
output_typestring. Dataset keyword: The output data type. Either SNRh or SNRv

returns

new_datasetdict: dictionary containing the output field “SNRh” or “SNRv” (if vert.

refl. were provided)

SNR_FILTER#

description

filters out low SNR echoes

[Source]

parameters

datatypelist of string. Dataset keyword: The input data typesm, must contain

“SNRh”, “SNRv”, “SNR” or “CNR” as well

as any other datatype supported by pyrad that

will be SNR filtered.
SNRminfloat. Dataset keyword: The minimum SNR to keep the data.

returns

new_datasetdict: dictionary containing the output field, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

ST1_IQ#

description

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

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”

returns

new_datasetdict: dictionary containing the output field “ST1” (stat_test_lag1)

ST2_IQ#

description

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

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”

returns

new_datasetdict: dictionary containing the output field “ST2”

TRAJ_TRT#

description

Processes data according to TRT trajectory

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any datatype supported by pyrad

and available in the radar data
time_tolfloat. Dataset keyword: tolerance between reference time of the radar volume and that of

the TRT cell [s]. Default 100.
alt_min, alt_maxfloat. Dataset keyword: Minimum and maximum altitude of the data inside the TRT cell to

retrieve [m MSL]. Default None
cell_centerBool. Dataset keyword: If True only the range gate closest to the center of the cell is

extracted. Default False
latlon_tolFloat. Dataset keyword: Tolerance in lat/lon when extracting data only from the center of

the TRT cell. Default 0.01

returns

new_datasetdictionary: Dictionary containing radar_out, a radar object containing only data

from inside the TRT cell

TRAJ_TRT_CONTOUR#

description

Gets the TRT cell contour corresponding to each radar volume

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any datatype supported by pyrad

and available in the radar data
time_tolfloat. Dataset keyword: tolerance between reference time of the radar volume and that of

the TRT cell [s]. Default 100.

returns

new_datasetdict: Dictionary containing radar_out and roi_dict. Radar out is the current

radar object. roi_dict contains the positions defining the TRT cell

contour

TURBULENCE#

description

Computes turbulence from the Doppler spectrum width and reflectivity using the PyTDA package

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“dBuZ” or “dBZ” or “dBZc” or “dBuZv” or “dBZv” or “dBZvc” or “CNRc”, and,

“W” or “Wv” or “Wu” or “Wvu” or “WD” or “WDc”
radiusfloat. Dataset keyword: Search radius for calculating Eddy Dissipation Rate (EDR).

Default 2
split_cutBool. Dataset keyword: Set to True for split-cut volumes. Default False
max_split_cutInt. Dataset keyword: Total number of tilts that are affected by split cuts. Only

relevant if split_cut=True. Default 2
xran, yranfloat array. Dataset keyword: Spatial range in X,Y to consider. Default [-100, 100] for both

X and Y
use_ntdaBool. Dataset keyword: Wether to use NCAR Turbulence Detection Algorithm (NTDA). Default

True
beamwidthFloat. 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_posBool. 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
verboseBool. Dataset keyword: True for verbose output. Default False

returns

new_datasetdict: dictionary containing the output field “EDR”

VAD#

description

Estimates vertical wind profile using the VAD (velocity Azimuth Display) technique

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain

“V” or “Vc”

returns

new_datasetdict: dictionary containing the output fields

“wind_vel_h_u”, “wind_vel_h_v”, “wind_vel_v”,

“estV”, “stdV”, and “diffV”

VEL_FILTER#

description

filters out range gates that could not be used for Doppler velocity estimation

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“diffV”, as well

as any other datatype supported by pyrad that

will be filtered where no Doppler velocity could be estimated.

returns

new_datasetdict: dictionary containing the output field, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

VIS#

description

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.

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad
offsetfloat. Dataset keyword: The offset above the minimum visibility that must be filtered

returns

new_datasetdict: dictionary containing the output field

“visibility”

VIS_FILTER#

description

filters out rays gates with low visibility and corrects the reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data typesm, must contain

“VIS” or “visibility_polar”, as well

as any other datatype supported by pyrad that

will be filtered where the visibility is poor.
VISminfloat. Dataset keyword: The minimum visibility to keep the data.

returns

new_datasetdict: dictionary containing the output, it will contain

the corrected version of the provided datatypes

For example dBZ -> dBZc, ZDR -> ZDRc, RhoHV -> RhoHVc

VOL_REFL#

description

Computes the volumetric reflectivity eta in 10log10(cm^2 km^-3)

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“dBZ”, “dBuZ”, “dBZc”, “dBuZc”, “dBZv”, “dBZvc” “dBuZv”, or “dBuZvc”
freqfloat. Dataset keyword: The radar frequency
kwfloat. Dataset keyword: The water constant

returns

new_datasetdict: dictionary containing the output field “eta_h” or “eta_v” (if vert. refl. were

provided)

VOL2BIRD_FILTER#

description

Masks all echo types that have been identified as non-biological by vol2bird

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“VOL2BIRD_CLASS”, as well

as any other datatype supported by pyrad that

will be filtered where vol2bird detected non-biological echoes

returns

new_datasetdict: dictionary containing the output

VOL2BIRD_GATE_FILTER#

description

Adds filter on range gate values to the vol2bird filter

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“VOL2BIRD_CLASS”, and,

“dBZ” or “dBZc”, and,

“V” or “Vc”
dBZ_maxfloat: Maximum reflectivity of biological scatterers
V_minfloat: Minimum Doppler velocity of biological scatterers

returns

new_datasetdict: dictionary containing the output

VSTATUS_TO_SAN#

description

Converts velocity status from lidar data into pyrad echo ID

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be “wind_vel_rad_status”

returns

new_datasetdict: dictionary containing the output field “echoID”

WBN#

description

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

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU” or “IQvvADU”

returns

new_datasetdict: dictionary containing the output field “WBN” (wide-band noise)

WIND_VEL#

description

Estimates the horizontal or vertical component of the wind from the radial velocity

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain

“V” or “Vc”
vert_projBoolean: If true the vertical projection is computed. Otherwise the

horizontal projection is computed

returns

new_datasetdict: dictionary containing the output field

“wind_vel_h_az”, (if vert_proj is False), or,

“wind_vel_v” (if vert_proj is True)

WINDSHEAR#

description

Estimates the wind shear from the wind velocity

[Source]

parameters

datatypestring. Dataset keyword: The input data type
az_tolfloat: The tolerance in azimuth when looking for gates on top

of the gate when computation is performed

returns

new_datasetdict: dictionary containing the output field “windshear_v”

WINDSHEAR_LIDAR#

description

Estimates the wind shear from the wind velocity of lidar scans

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain

“V” or “Vc”
az_tolfloat: The tolerance in azimuth when looking for gates on top

of the gate when computation is performed

returns

new_datasetdict: dictionary containing the output field “windshear_v”

ZDR#

description

Computes differential reflectivity from the horizontal and vertical spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“sdBZ” and “sdBZv”, or

“sdBuZ” and “sdBZuv”

returns

new_datasetdict: dictionary containing the output fields

“ZDR” or “ZDRu” depending on the specified input datatype

ZDR_IQ#

description

Computes differential reflectivity from the horizontal and vertical IQ data

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“IQhhADU”, “IQvvADU”, “IQNADUh” and “IQNADUv”
subtract_noiseBool: If True noise will be subtracted from the signal
lagint: The time lag to use in the estimators

returns

new_datasetdict: dictionary containing the output field “ZDR”

ZDR_PREC#

description

Keeps only suitable data to evaluate the differential reflectivity in moderate rain or precipitation (for vertical scans)

[Source]

parameters

datatypelist of strings. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“PhiDP” or “PhiDPc”, and,

“uRhoHV” or “RhoHV” or “RhoHVc”, and,

“TEMP” (Optional), and,

“H_ISO0” (Optional)
ml_filterboolean. Dataset keyword: indicates if a filter on data in and above the melting layer is

applied. Default True.
rminfloat. Dataset keyword: minimum range where to look for rain [m]. Default 1000.
rmaxfloat. Dataset keyword: maximum range where to look for rain [m]. Default 50000.
Zminfloat. Dataset keyword: minimum reflectivity to consider the bin as precipitation [dBZ].

Default 20.
Zmaxfloat. Dataset keyword: maximum reflectivity to consider the bin as precipitation [dBZ]

Default 22.
RhoHVminfloat. Dataset keyword: minimum RhoHV to consider the bin as precipitation

Default 0.97
PhiDPmaxfloat. Dataset keyword: maximum PhiDP to consider the bin as precipitation [deg]

Default 10.
elmaxfloat. Dataset keyword: maximum elevation angle where to look for precipitation [deg]

Default None.
ml_thicknessfloat. Dataset keyword: assumed thickness of the melting layer. Default 700.
fzlfloat. 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.
soundingstr. Dataset keyword: The nearest radiosounding WMO code (5 int digits). It will be used to

compute the freezing level, if no temperature field name is specified,

if the temperature field isin the radar object or if no freezing_level

is explicitely defined.

returns

new_datasetdict: dictionary containing the output field “ZDR_prec”

ZDR_SNOW#

description

Keeps only suitable data to evaluate the differential reflectivity in snow

[Source]

parameters

datatypelist of strings. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“PhiDP” or “PhiDPc”, and,

“uRhoHV” or “RhoHV” or “RhoHVc”, and,

“hydro”, and,

“TEMP” (Optional), and,

“SNRh” or “SNRv” (Optional, used to filter with SNRmin and SNRmax)
rminfloat. Dataset keyword: minimum range where to look for rain [m]. Default 1000.
rmaxfloat. Dataset keyword: maximum range where to look for rain [m]. Default 50000.
Zminfloat. Dataset keyword: minimum reflectivity to consider the bin as snow [dBZ].

Default 0.
Zmaxfloat. Dataset keyword: maximum reflectivity to consider the bin as snow [dBZ]

Default 30.
SNRminfloat. Dataset keyword: minimum SNR to consider the bin as snow [dB].

Default 10.
SNRmaxfloat. Dataset keyword: maximum SNR to consider the bin as snow [dB]

Default 50.
RhoHVminfloat. Dataset keyword: minimum RhoHV to consider the bin as snow

Default 0.97
PhiDPmaxfloat. Dataset keyword: maximum PhiDP to consider the bin as snow [deg]

Default 10.
elmaxfloat. Dataset keyword: maximum elevation angle where to look for snow [deg]

Default None.
KDPmaxfloat. Dataset keyword: maximum KDP to consider the bin as snow [deg]

Default None
TEMPminfloat. Dataset keyword: minimum temperature to consider the bin as snow [deg C].

Default None
TEMPmaxfloat. Dataset keyword: maximum temperature to consider the bin as snow [deg C]

Default None
hydroclasslist of ints. Dataset keyword: list of hydrometeor classes to keep for the analysis

Default [2] (dry snow)

returns

new_datasetdict: dictionary containing the output field “ZDR_snow”

SPECTRA#

FFT#

description

Compute the Doppler spectra form the IQ data with a Fourier transform

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“IQNdBADUh” and/or “IQNdBADUv” and/or

“IQNADUh” and/or “IQNADUv” (see new_dataset below)
windowlist 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’].

returns

new_datasetdict: dictionary containing the output fields

“ShhADUu” (unfiltered_complex_spectra_hh_ADU) if IQNdBADUh was provided,

“SvvADUu” (unfiltered_complex_spectra_vv_ADU) if IQNdBADUv was provided,

“sNADUh” (spectral_noise_power_hh_ADU) if IQNADUh was provided,

“sNADUv” (spectral_noise_power_vv_ADU) if IQNADUv was provided,

FILTER_0DOPPLER#

description

Function to filter the 0-Doppler line bin and neighbours of the Doppler spectra

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any of the spectral fields supported by pyrad
filter_widthfloat: The Doppler filter width. Default 0.
filter_unitsstr: Can be ‘m/s’ or ‘Hz’. Default ‘m/s’

returns

new_datasetdict: dictionary containing the output field, the names of the output fields

is the same as the provided datatypes, except for unfiltered fields which are renamed in the following

“dBuZ” => “dBZ”

FILTER_SPECTRA_NOISE#

description

Filter the noise of the Doppler spectra by clipping any data below the noise level plus a margin

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“ShhADU” or “SvvADU” or “ShhADUu” or “SvvADUu”, and,

“sNADUh” or “sNADUv”
clipping_levelfloat: The clipping level [dB above noise level]. Default 10.

returns

new_datasetdict: dictionary containing the output field, the names of the output fields

is the same as the provided datatypes

IFFT#

description

Compute the Doppler spectrum width from the spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“ShhADU” or “ShhADUu”, or,

“SvvADU” or “SvvADUu”, or,

“sNADUh” or “sNADUv”

returns

new_datasetdict: dictionary containing the output fields

“IQhhADU” if “ShhADU” or “ShhADUu” were provided,

“IQhvvDU” if “SvvADU” or “SvvADUu” were provided,

“IQNADUh” if “sNADUh” was provided,

“IQNADUv” if “sNADUv” was provided.

RAW_IQ#

description: Dummy function that returns the initial input data set

[Source]

parameters

returns

new_datasetdict: dictionary containing the output

RAW_SPECTRA#

description: Dummy function that returns the initial input data set

[Source]

parameters

returns

new_datasetdict: dictionary containing the output

SPECTRA_ANGULAR_AVERAGE#

description

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.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types,

any spectral datatype supported by pyrad
navgint: Number of spectra to average. If -1 all spectra will be averaged.

Default -1.

returns

new_datasetdict: dictionary containing the same output fields as the provided datatypes

SPECTRA_POINT#

description

Obtains the spectra or IQ data at a point location.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
single_pointboolean. 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
latlonboolean. Dataset keyword: if True position is obtained from latitude, longitude information,

otherwise position is obtained from antenna coordinates

(range, azimuth, elevation). Default False
truealtboolean. 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
lonfloat. Dataset keyword: the longitude [deg]. Use when latlon is True.
latfloat. Dataset keyword: the latitude [deg]. Use when latlon is True.
altfloat. Dataset keyword: altitude [m MSL]. Use when latlon is True. Default 0.
elefloat. Dataset keyword: radar elevation [deg]. Use when latlon is False or when latlon is

True and truealt is False
azifloat. Dataset keyword: radar azimuth [deg]. Use when latlon is False
rngfloat. Dataset keyword: range from radar [m]. Use when latlon is False
AziTolfloat. Dataset keyword: azimuthal tolerance to determine which radar azimuth to use [deg].

Default 0.5
EleTolfloat. Dataset keyword: elevation tolerance to determine which radar elevation to use

[deg]. Default 0.5
RngTolfloat. Dataset keyword: range tolerance to determine which radar bin to use [m]. Default

50.

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

SPECTRAL_NOISE#

description

Computes the spectral noise

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“ShhADU” or “SvvADU” or “ShhADUu” or “SvvADUu”
unitsstr: The units of the returned signal. Can be ‘ADU’, ‘dBADU’ or ‘dBm’
navgint: Number of spectra averaged
rminint: Range from which the data is used to estimate the noise
nnoise_minint: Minimum number of samples to consider the estimated noise power

valid

returns

new_datasetdict: dictionary containing the output field

“sNADUh” or

“sNADUv” or

“sNdBADUh” or

“sNdBADUv” or

“sNdBmh” or

“sNdBmv”

depending on which input datatype and units were provided

SPECTRAL_PHASE#

description

Computes the spectral phase

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“ShhADU” or “SvvADU” or “ShhADUu” or “SvvADUu”

returns

new_datasetdict: dictionary containing the output field

“SPhasehh” if “ShhADU” was provided as input

“SPhasehhu” if “ShhADUu” was provided as input

“SPhasevv” if “SvvADU” was provided as input

“SPhasevvu” if “SvvADUu” was provided as input

SPECTRAL_POWER#

description

Computes the spectral power

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“ShhADU” or “SvvADU” or “ShhADUu” or “SvvADUu”, and,

“sNADUh”, or “sNADUv”
unitsstr: The units of the returned signal. Can be ‘ADU’, ‘dBADU’ or ‘dBm’
subtract_noiseBool: If True noise will be subtracted from the signal
smooth_windowint or None: Size of the moving Gaussian smoothing window. If none no smoothing

will be applied

returns

new_datasetdict: dictionary containing the output field

“sPhhADU” or “sPhhADUu”, or

“sPvvADU” or “sPvvADUu”, or

“sPhhdBADU” or “sPhhdBADUu”, or

“sPvvdBADU” or “sPvvdBADUu”, or

“sPhhAdBm” or “sPhhdBmu”, or

“sPvvdBm” or “sPvvdBmu”,

depending on which input datatype and units were provided

SPECTRAL_REFLECTIVITY#

description

Computes spectral reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

either a combination of signal and noise

“ShhADU” or “SvvADU” or “ShhADUu” or “SvvADUu”, and,

“sNADUh” or “sNADUv”, or

the power signal

“sPhhADU” or “sPvvADU” or “sPhhADUu” or “sPvvADUu”
subtract_noiseBool: If True noise will be subtracted from the signal
smooth_windowint or None: Size of the moving Gaussian smoothing window. If none no smoothing

will be applied

returns

new_datasetdict: dictionary containing the output field

“sdBZ” if “ShhADU” (or “sPhhADU”) was provided as input

“sdBuZ” if “ShhADUu” (or “sPhhADUu”) was provided as input

“sdBZv” if “SvvADU” (or “sPvvADU”) was provided as input

“sdBuZv” if “SvvADUu” (or “sPvvADUu”) was provided as input

SRHOHV_FILTER#

description

Filter Doppler spectra as a function of spectral RhoHV

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“sRhoHV” or “sRhoHVu”,

as well as any spectral field supported by pyrad
sRhoHV_thresholdfloat: Data with sRhoHV module above this threshold will be filtered.

Default 1.

returns

new_datasetdict: dictionary containing the output field, the names of the output fields

is the same as the provided datatypes, except for unfiltered fields which are renamed in the following

“dBuZ” => “dBZ”

CENTROIDS#

description

Computes centroids for the semi-supervised hydrometeor classification

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“RhoHV”, or “uRhoHV”, or “RhoHVc”, and,

“KDP”, or “KDPc”, and,

“TEMP” or “H_ISO0” (optional)
samples_per_volint. Dataset keyword: Maximum number of samples per volume kept for further analysis.

Default 20000
nbinsint.: Number of bins of the histogram used to make the data platykurtic.

Default 110
pdf_zh_maxint: Multiplicative factor to the Guassian function used to make the

distribution of the reflectivity platykurtic that determines the

number of samples for each bin. Default 10000
pdf_relh_maxint: Multiplicative factor to the Guassian function used to make the

distribution of the height relative to the iso-0 platykurtic that

determines the number of samples for each bin. Default 20000
sigma_zh, sigma_relhfloat: sigma of the respective Gaussian functions. Defaults 0.75 and 1.5
randomizebool: If True the data is randomized to avoid the effects of the

quantization. Default True
platykurtic_dBZbool: If True makes the reflectivity distribution platykurtic. Default

True
platykurtic_H_ISO0bool: If True makes the height respect to the iso-0 distribution

platykurtic. Default True
relh_slopefloat. Dataset keyword: The slope used to transform the height relative to the iso0 into

a sigmoid function. Default 0.001
external_iterationsint. Dataset keywords: Number of iterations of the external loop. This number will

determine how many medoids are computed for each hydrometeor

class. Default 30
internal_iterationsint. Dataset keyword: Maximum number of iterations of the internal loop. Default 10
sample_dataBool.: If True the data is going to be sampled prior to each external

iteration. Default False
nsamples_iterint.: Number of samples per iteration. Default 20000
alphafloat: Minimum value to accept the cluster according to p. Default 0.01
cv_approachBool: If true it is used a critical value approach to reject or accept

similarity between observations and reference. If false it is used

a p-value approach. Default True
n_samples_synint: Number of samples drawn from reference to compare it with

observations in the KS test. Default 50
num_samples_arrarray of int: Number of observation samples used in the KS test to choose from.

Default (30, 35, 40)
acceptance_thresholdfloat. Dataset keyword: Threshold on the inter-quantile coefficient of dispersion of the

medoids above which the medoid of the class is not acceptable.

Default 0.5
nmedoids_minint: Minimum number of intermediate medoids to compute the final

result. Default 1
var_namestupple: The names of the features. Default (‘dBZ’, ‘ZDR’, ‘KDP’, ‘RhoHV’,

‘H_ISO0’)
hydro_names: tupple: The name of the hydrometeor types. Default (‘AG’, ‘CR’, ‘LR’,

‘RP’, ‘RN’, ‘VI’, ‘WS’, ‘MH’, ‘IH/HDG’)
weighttupple: The weight given to each feature when comparing to the reference.

It is in the same order as var_names. Default (1., 1., 1., 1.,

0.75)
parallelizedbool: If True the centroids search is going to be parallelized. Default

False
kmax_iterint: Maximum number of iterations of the k-medoids algorithm. Default

100
nsamples_smallint: Maximum number before using the k-medoids CLARA algorithm. If this

number is exceeded the CLARA algorithm will be used. Default 40000
sampling_size_claraint: Number of samples used in each iteration of the k-medoids CLARA

algorithm. Default 10000
niter_claraint: Number of iterations performed by the k-medoids CLARA algorithm.

Default 5
keep_labeled_databool: If True the labeled data is going to be kept for storage. Default

True
use_medianbool: If True the intermediate centroids are computed as the median

of the observation variables and the final centroids are computed

as the median of the intermediate centroids. If false they are

computed using the kmedoids algorithm. Default false
allow_label_duplicatesbool: If True allow to label multiple clusters with the same label.

Default True

returns

new_datasetdict: dictionary containing the output centroids

COLOCATED_GATES#

description

Find colocated gates within two radars

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types to use to check colocated gates (one for every radar)

Any datatype supported by pyrad and available in both radars is accepted.

If visibility filtering is desired, the fields

“visibility” or “visibility_polar” must be specified for both radars.
h_tolfloat. Dataset keyword: Tolerance in altitude difference between radar gates [m].

Default 100.
latlon_tolfloat. Dataset keyword: Tolerance in latitude and longitude position between radar gates

[deg]. Default 0.0005
vol_d_tolfloat. Dataset keyword: Tolerance in pulse volume diameter [m]. Default 100.
visminfloat. Dataset keyword: Minimum visibility [percent]. Default None.
hminfloat. Dataset keyword: Minimum altitude [m MSL]. Default None.
hmaxfloat. Dataset keyword: Maximum altitude [m MSL]. Default None.
rminfloat. Dataset keyword: Minimum range [m]. Default None.
rmaxfloat. Dataset keyword: Maximum range [m]. Default None.
elminfloat. Dataset keyword: Minimum elevation angle [deg]. Default None.
elmaxfloat. Dataset keyword: Maximum elevation angle [deg]. Default None.
azrad1minfloat. Dataset keyword: Minimum azimuth angle [deg] for radar 1. Default None.
azrad1maxfloat. Dataset keyword: Maximum azimuth angle [deg] for radar 1. Default None.
azrad2minfloat. Dataset keyword: Minimum azimuth angle [deg] for radar 2. Default None.
azrad2maxfloat. Dataset keyword: Maximum azimuth angle [deg] for radar 2. Default None.

returns

new_datasetdict: dictionary containing the field “colocated_gates”

ICON_COORD#

description

Gets the icon indices corresponding to each icon coordinates

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad
iconpathstring. General keyword: path where to store the look up table
modelstring. Dataset keyword: The icon model to use. Can be icon-1, icon-1e, icon-2, icon-7

returns

new_datasetdict: dictionary containing the output field

“icon_index”

HZT_COORD#

description

Gets the HZT indices corresponding to each HZT coordinates

[Source]

parameters

metranet_read_libstr. Global keyword: Type of METRANET reader library used to read the data.

Can be ‘C’ or ‘python’
datatypestring. Dataset keyword: arbitrary data type supported by pyrad
iconpathstring. General keyword: path where to store the look up table

returns

new_datasetdict: dictionary containing the output field “icon_index”

ICON2RADAR#

description

Gets icon data and put it in radar coordinates using look up tables

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad
icon_typestr. Dataset keyword: name of the icon field to process. Default TEMP
icon_variableslist of strings. Dataset keyword: Py-art name of the icon fields. Default temperature
icon_time_index_min, icon_time_index_maxint: minimum and maximum indices of the icon 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

returns

new_datasetdict: dictionary containing the output fields corresponding to icon_variables

GRID#

RAW_GRID#

description

Dummy function that returns the initial input data set

[Source]

parameters

datatypestring. Dataset keyword: arbitrary data type supported by pyrad and contained in the grid data

returns

new_datasetdict: dictionary containing the output with field corresponding to datatype

GECSX#

description

Computes ground clutter RCS, radar visibility and many others using the GECSX algorithmn translated from IDL into python

[Source]

parameters

datatypelist of string. Dataset keyword: arbitrary data type supported by pyrad
range_discretizationfloat. Dataset keyword: Range discretization used when computing the Cartesian visibility field

the larger the better but the slower the processing will be
az_discretizationfloat. Dataset keyword: Azimuth discretization used when computing the Cartesian visibility

field, the larger the better but the slower the processing will be
kefloat. Dataset keyword: Equivalent earth-radius factor used in the computation of the radar

beam refraction
atm_attfloat. Dataset keyword: One-way atmospheric refraction in db / km
mosotti_kwfloat. Dataset keyword: Clausius-Mosotti factor K, depends on material (water) and wavelength

for water = sqrt(0.93)
raster_oversamplingint. Dataset keyword: The raster resolution of the DEM should be smaller than

the range resolution of the radar (defined by the pulse length).

If this is not the case, this keyword can be set to increase the

raster resolution. The values for the elevation, sigma naught,

visibility are repeated. The other values are recalculated.

Values for raster_oversampling:

0 or undefined: No oversampling is done

1: Oversampling is done. The factor N is automatically calculated

such that 2*dx/N < pulse length

2 or larger: Oversampling is done with this value as N
sigma0_methodstring. Dataset keyword: Which estimation method to use, either ‘Gabella’ or ‘Delrieu’
clipint. Dataset keyword: If set to true, the provided DEM will be clipped to the extent

of the polar radar domain. Increases computation speed a lot but

Cartesian output fields will be available only over radar domain

returns

new_datasetlist of dict: list of dictionaries containing the polar data output and the

Cartesian data output in this order

The first dictionary (polar) contains the following fields:

“rcs_clutter”, “dBm_clutter”, “dBZ_clutter” and “visibility_polar”

The second dictionary (cart) contains the following fields:

“bent_terrain_altitude”, “terrain_slope”, “terrain_aspect”,

“elevation_angle”, “min_vis_elevation”, “min_vis_altitude”,

“incident_angle”, “sigma_0”, “effective_area”

GRID#

description

Puts the radar data in a regular grid

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
gridconfigdictionary. 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.

latmin, latmax, lonmin, lonmax : floats

minimum and maximum latitude and longitude [deg], if specified

xmin, xmax, ymin, ymax, latorig, lonorig will be ignored

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
wfuncstr. 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_funcstr. Dataset keyword: the function used to compute the region of interest.

Possible values: dist_beam, constant
roifloat. Dataset keyword: the (minimum) radius of the region of interest in m. Default half

the largest resolution
beamwidthfloat. 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_spacingfloat. 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

returns

new_datasetdict: dictionary containing the gridded data with fields corresponding to

datatype

GRID_FIELDS_DIFF#

description

Computes grid field differences

[Source]

parameters

datatypelist of string. Dataset keyword: The two input data types to compare.

Can any two datatypes supported by pyrad

returns

new_datasetdict: dictionary containing a radar object containing the field differences

GRID_MASK#

description

Mask data. Puts True if data is within thresholds, False if it is not. Thresholds can be min, max or both min and max

[Source]

parameters

datatypelist of string. Dataset keyword: The input data type, can be any datatype supported by pyrad
threshold_minfloat or None: Threshold used for the mask. Values below threshold are set to False.

Above threshold are set to True. Default None.
threshold_maxfloat or None: Threshold used for the mask. Values above threshold are set to False.

Below threshold are set to True. Default None.
x_dir_ext, y_dir_extint: Number of pixels by which to extend the mask on each side of the

west-east direction and south-north direction

returns

new_datasetdict: dictionary containing the output field “mask”

GRID_TEXTURE#

description

Computes the 2D texture of a gridded field

[Source]

parameters

datatypelist of string. Dataset keyword: The input data type, can be any datatype supported by pyrad
xwind, ywindint: The size of the local window in the x and y axis. Default 7
fill_valuefloat: The value with which to fill masked data. Default np.NaN

returns

new_datasetdict: dictionary containing a radar object containing the field

“texture”

NORMALIZE_LUMINOSITY#

description

Normalize the data by the sinus of the sun elevation. The sun elevation is computed at the central pixel.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data type, can be any datatype supported by pyrad

returns

new_datasetdict: dictionary containing the normalized field, the name

of the field is datatype_norm

PIXEL_FILTER#

description

Masks all pixels that are not of the class specified in keyword pixel_type

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“mask”, as well as

any datatypes supported by pyrad
pixel_typeint or list of ints: The type of pixels to keep: 0 No data, 1 Below threshold, 2 Above

threshold. Default 2

returns

new_datasetdict: dictionary containing the output datatypes masked

VOL2GRID#

description

Function to convert polar data into a Cartesian grid

[Source]

parameters

xmin, xmax, ymin, ymaxfloat: Horizontal limits of the grid [m from origin]. Default +-20000.
zmin, zmaxfloat: vertical limits of the grid [masl]. Default 1000.
hres, vresfloat: horizontal and vertical resolution [m]. Default 1000.
lat0, lon0float: Grid origin [deg]. The default will be the radar position
alt0float: Grid origin altitude [masl]. Default is 0
wfuncstr: Weighting function. Default NEAREST

returns

new_datasetdict: dictionary containing the output

DDA#

description

Estimates horizontal wind speed and direction with a multi-doppler approach This method uses the python package pyDDA

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain

“V” or “Vc”, and,

“dBuZ”, “dBZ”, or “dBZc”
gridconfigdictionary. 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.

latmin, latmax, lonmin, lonmax : floats

minimum and maximum latitude and longitude [deg], if specified

xmin, xmax, ymin, ymax will be ignored

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
wfuncstr. 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_funcstr. Dataset keyword: the function used to compute the region of interest.

Possible values: dist_beam, constant
roifloat. Dataset keyword: the (minimum) radius of the region of interest in m. Default half

the largest resolution
beamwidthfloat. 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_spacingfloat. 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
signslist of integers: The sign of the velocity field for every radar object.

A value of 1 represents when

positive values velocities are towards the radar, -1 represents

when negative velocities are towards the radar.
Cofloat: Weight for cost function related to observed radial velocities.

Default: 1.
Cmfloat: Weight for cost function related to the mass continuity equation.

Default: 1500.
Cx: float: Smoothing coefficient for x-direction
Cy: float: Smoothing coefficient for y-direction
Cz: float: Smoothing coefficient for z-direction
Cb: float: Coefficient for sounding constraint
Cv: float: Weight for cost function related to vertical vorticity equation.
Cmod: float: Coefficient for model constraint
Cpoint: float: Coefficient for point constraint
wind_tol: float: Stop iterations after maximum change in winds is less than this

value.
frzfloat: The freezing level in meters. This is to tell PyDDA where to use

ice particle fall speeds in the wind retrieval verus liquid.

returns

new_datasetdict: dictionary containing the output fields

“wind_vel_h_u”, “wind_vel_h_v” and “wind_vel_v”

GRID_TIMEAVG#

GRID_TIME_STATS#

description

computes the temporal statistics of a field

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any datatype supported by pyrad
periodfloat. Dataset keyword: the period to average [s]. If -1 the statistics are going to be

performed over the entire data. Default 3600.
start_averagefloat. 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_nanbool. Dataset keyword: If true non valid data will be used
nan_valuefloat. 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

returns

new_datasetdict: dictionary containing the output fields corresponding to datatypes

GRID_TIME_STATS2#

description

computes temporal statistics of a field

[Source]

parameters

datatypelist of string. Dataset keyword: The input data type, can be any datatype supported by pyrad
periodfloat. Dataset keyword: the period to average [s]. If -1 the statistics are going to be

performed over the entire data. Default 3600.
start_averagefloat. 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_nanbool. Dataset keyword: If true non valid data will be used
nan_valuefloat. Dataset keyword: The value of the non valid data. Default 0

returns

new_datasetdict: dictionary containing the output fields corresponding to

datatypes

GRID_RAIN_ACCU#

description

computes rainfall accumulation fields

[Source]

parameters

datatypelist of string. Dataset keyword: The input data type, can be any data type supported by pyrad

but typically RR is used
periodfloat. Dataset keyword: the period to average [s]. If -1 the statistics are going to be

performed over the entire data. Default 3600.
start_averagefloat. Dataset keyword: when to start the average [s from midnight UTC]. Default 0.
use_nanbool. Dataset keyword: If true non valid data will be used
nan_valuefloat. Dataset keyword: The value of the non valid data. Default 0

returns

new_datasetdict: dictionary containing the output field corresponding to datatype

INTERCOMP#

description

intercomparison between two radars at co-located gates. The variables compared must be of the same type.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types (one for every radar).

Any arbitrary datatype supported by pyrad and available

in both radar is accepted.
colocgatespathstring.: base path to the file containing the coordinates of the co-located

gates
coloc_radars_namestring. Dataset keyword: string identifying the radar names
rays_are_indexedbool. Dataset keyword: If True it is considered that the rays are indexed and that the

data can be selected simply looking at the ray number.

Default false
azi_tolfloat. Dataset keyword: azimuth tolerance between the two radars. Default 0.5 deg
ele_tolfloat. Dataset keyword: elevation tolerance between the two radars. Default 0.5 deg
rng_tolfloat. Dataset keyword: range tolerance between the two radars. Default 50 m
coloc_data_dirstring. Dataset keyword: name of the directory containing the csv file with colocated data

returns

new_datasetdict: dictionary containing a dictionary with intercomparison data and the

key “final” which contains a boolean that is true when all volumes

have been processed

INTERCOMP_FIELDS#

description

intercomparison between two radars

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types for each radar,

Any datatype supported by pyrad and available in both radars is supported

returns

new_datasetdict: dictionary containing a dictionary with intercomparison data

INTERCOMP_TIME_AVG#

description

intercomparison between the average reflectivity of two radars

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

dBZ” or “dBZc” or “dBuZ” or “dBZv” or “dBZvc” or “dBuZv, and,

“PhiDP” or “PhiDPc”, and,

“time_avg_flag”

for the two radars
colocgatespathstring.: base path to the file containing the coordinates of the co-located

gates
coloc_data_dirstring. Dataset keyword: name of the directory containing the csv file with colocated data
coloc_radars_namestring. Dataset keyword: string identifying the radar names
rays_are_indexedbool. Dataset keyword: If True it is considered that the rays are indexed and that the

data can be selected simply looking at the ray number.

Default false
azi_tolfloat. Dataset keyword: azimuth tolerance between the two radars. Default 0.5 deg
ele_tolfloat. Dataset keyword: elevation tolerance between the two radars. Default 0.5 deg
rng_tolfloat. Dataset keyword: range tolerance between the two radars. Default 50 m
clt_maxint. Dataset keyword: maximum number of samples that can be clutter contaminated.

Default 100 i.e. all
phi_excess_maxint. Dataset keyword: maximum number of samples that can have excess instantaneous

PhiDP. Default 100 i.e. all
non_rain_maxint. Dataset keyword: maximum number of samples that can be no rain. Default 100 i.e. all
phi_avg_maxfloat. Dataset keyword: maximum average PhiDP allowed. Default 600 deg i.e. any

returns

new_datasetdict: dictionary containing a dictionary with intercomparison data and the

key “final” which contains a boolean that is true when all volumes

have been processed

ML#

ML_DETECTION#

description

Detects the melting layer

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBZ” or “dBZc”, and,

“ZDR” or “ZDRc”, and,

“RhoHV” or “RhoHVc”, and,

“TEMP” or “H_ISO0” (optional)

returns

new_datasetdict: dictionary containing the output field “ml”

VPR#

description

Computes the vertical profile of reflectivity using the Meteo-France operational algorithm

[Source]

parameters

datatypestring. Dataset keyword: The input data type, must contain,

“dBZ” or “dBZc”, and,

“H_ISO0” or “H_ISO0c” or “TEMP” or “TEMPc”
nvalid_minint: Minimum number of rays with data to consider the azimuthal average

valid. Default 20.
angle_min, angle_maxfloat: Minimum and maximum elevation angles used to compute the ratios of

reflectivity. Default 0. and 4.
ml_thickness_min, ml_thickness_max, ml_thickness_stepfloat: Minimum, maximum and step of the melting layer thickness of the

models to explore [m]. Default 200., 800. and 200.
iso0_maxfloat: maximum iso0 altitude of the profile. Default 5000.
ml_top_diff_max, ml_top_stepfloat: maximum difference +- between iso-0 and top of the melting layer

[m] of the models to explore. Step. Default 200. and 200.
ml_peak_min, ml_peak_max, ml_peak_step: float: min, max and step of the value at the peak of the melting layer of

the models to explore. Default 1., 6. and 1.
dr_min, dr_max, dr_stepfloat: min, max and step of the decreasing ratio above the melting layer.

Default -6., -1.5 and 1.5
dr_defaultfloat: default decreasing ratio to use if a proper model could not be

found. Default -4.5
dr_altfloat: altitude above the melting layer top (m) where theoretical profile

needs to be defined to be able to compute DR. If the theoretical

profile is not defined up to the resulting altitude a default DR

is used. Default 800.
h_maxfloat: maximum altitude [masl] where to compute the model profile.

Default 6000.
h_corr_maxfloat: maximum altitude [masl] considered for the VPR correction
h_resfloat: resolution of the model profile (m). Default 1.
max_weightfloat: Maximum weight of the antenna pattern. Default 9.
rmin_obs, rmax_obsfloat: minimum and maximum range (m) of the observations that are

compared with the model. Default 5000. and 150000.
use_mlbool: If True the retrieved ML will be used to select the range of

variability the meltin layer top and thickness
vpr_memory_maxfloat: The maximum time range to average reflectivity (min)
filter_vpr_memory_maxfloat: The maximum time range where to look for previous VPR retrievals
ml_datatypestr: Melting layer data type descriptor
z_datatypestr: descriptor used get the linear reflectivity information
vpr_theo_datatypestr: descriptor used to get the retrieved theoretical VPR
filter_paramsbool: If True the current theoretical VPR profile is averaged with the

past VPR profile by averaging the 4 parameters that define the

profile, otherwise the shape of the profiles is averaged. Default

false. Used only in non-spatialised VPR correction
weight_memfloat: Weight given to past VPR when filtering the current VPR
spatializedbool: If True the VPR correction is spatialized
correct_iso0bool: If True the iso0 field is corrected by a bias constant computed as

the difference between the retrieved melting layer top and the

average iso0 and areas with precipitation. Default True. Used only

in the spatialised VPR correction

returns

new_datasetdict: dictionary containing the output fields “dBZc” and “VPRcorr”

MONITORING#

GC_MONITORING#

description

computes ground clutter monitoring statistics

[Source]

parameters

excessgatespathstr. Config keyword: The path to the gates in excess of quantile location
excessgates_fnamestr. Dataset keyword: The name of the gates in excess of quantile file
datatypelist of string. Dataset keyword: The input data types, it must contain

“echoID” (Optional allows filter_prec),

as well as any other fields supported by pyrad
stepfloat. 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_gridBoolean. Dataset keyword: Whether the radar has a Boolean grid or not. Default False
val_minFloat. Dataset keyword: Minimum value to consider that the gate has signal. Default None
filter_precstr. Dataset keyword: Give which type of volume should be filtered. None, no filtering;

keep_wet, keep wet volumes; keep_dry, keep dry volumes.
rmax_precfloat. Dataset keyword: Maximum range to consider when looking for wet gates [m]
percent_prec_maxfloat. Dataset keyword: Maxim percentage of wet gates to consider the volume dry

returns

new_datasetRadar: radar object containing histogram data with fields corresponding

to specified datatypes

MONITORING#

description

computes monitoring statistics

[Source]

parameters

datatypelist of string. Dataset keyword: Arbitrary datatype supported by pyrad
stepfloat. 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_raysint. 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

returns

new_datasetRadar: radar object containing histogram data

OCCURRENCE#

description

computes the frequency of occurrence of data. It looks only for gates where data is present.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, it must contain

“echoID” (Optional allows filter_prec),

as well as any other fields supported by pyrad
regular_gridBoolean. Dataset keyword: Whether the radar has a Boolean grid or not. Default False
rmin, rmaxfloat. Dataset keyword: minimum and maximum ranges where the computation takes place. If

-1 the whole range is considered. Default is -1
val_minFloat. Dataset keyword: Minimum value to consider that the gate has signal. Default None
filter_precstr. Dataset keyword: Give which type of volume should be filtered. None, no filtering;

keep_wet, keep wet volumes; keep_dry, keep dry volumes.
rmax_precfloat. Dataset keyword: Maximum range to consider when looking for wet gates [m]
percent_prec_maxfloat. Dataset keyword: Maxim percentage of wet gates to consider the volume dry

returns

new_datasetdict: radar object containing frequency of occurence data with fields corresponding

to specified datatypes

OCCURRENCE_PERIOD#

description

computes the frequency of occurrence over a long period of time by adding together shorter periods

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“occurence” and,

“nsamples”
regular_gridBoolean. Dataset keyword: Whether the radar has a Boolean grid or not. Default False
rmin, rmaxfloat. Dataset keyword: minimum and maximum ranges where the computation takes place. If

-1 the whole range is considered. Default is -1

returns

new_datasetdict: dictionary containing the output fields “occurence” and

“nsamples”

TIMEAVG_STD#

description

computes the average and standard deviation of data. It looks only for gates where data is present.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, it must contain

“echoID” (Optional allows filter_prec),

“dBZ” or “dBZc” or “dBZv” or “dBZvc” or “dBuZ” or “dBuZc” (Optional, allows val_min)

as well as any other fields supported by pyrad
regular_gridBoolean. Dataset keyword: Whether the radar has a Boolean grid or not. Default False
rmin, rmaxfloat. Dataset keyword: minimum and maximum ranges where the computation takes place. If

-1 the whole range is considered. Default is -1
val_minFloat. Dataset keyword: Minimum reflectivity value to consider that the gate has signal.

Default None
filter_precstr. Dataset keyword: Give which type of volume should be filtered. None, no filtering;

keep_wet, keep wet volumes; keep_dry, keep dry volumes.
rmax_precfloat. Dataset keyword: Maximum range to consider when looking for wet gates [m]
percent_prec_maxfloat. Dataset keyword: Maxim percentage of wet gates to consider the volume dry
lin_transBoolean. Dataset keyword: If True the data will be transformed into linear units. Default

False

returns

new_datasetdict: dictionary containing the average and standard deviation for every field

specified as datatype

QVP#

EVP#

description

Computes enhanced vertical profiles, by averaging over height levels PPI data.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement,

can be any datatype supported by pyrad and available in the data
lat, lonfloat: latitude and longitude of the point of interest [deg]
latlon_tolfloat: tolerance in latitude and longitude in deg. Default 0.0005
delta_rng, delta_azifloat: maximum range distance [m] and azimuth distance [degree] from the

central point of the evp containing data to average. Default 5000.

and 10.
hmaxfloat: The maximum height to plot [m]. Default 10000.
hresfloat: The height resolution [m]. Default 250.
avg_typestr: The type of averaging to perform. Can be either “mean” or “median”

Default “mean”
nvalid_minint: Minimum number of valid points to consider the data valid when

performing the averaging. Default 1
interp_kindstr: 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

returns

new_datasetdict: dictionary containing the EVP and a keyword stating whether the

processing has finished or not.

Kaltenboeck R., Ryzhkov A. 2016: A freezing rain storm explored with a

C-band polarimetric weather radar using the QVP methodology.

Meteorologische Zeitschrift vol. 26 pp 207-222

QVP#

description

Computes quasi vertical profiles, by averaging over height levels PPI data.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement,

can be any datatype supported by pyrad and available in the data
angleint 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_tolfloat: If the radar object contains an RHI volume, the tolerance in the

elevation angle for the conversion into PPI
hmaxfloat: The maximum height to plot [m]. Default 10000.
hresfloat: The height resolution [m]. Default 50
avg_typestr: The type of averaging to perform. Can be either “mean” or “median”

Default “mean”
nvalid_minint: Minimum number of valid points to accept average. Default 30.
interp_kindstr: 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

returns

new_datasetdict: dictionary containing the QVP and a keyword stating whether the

processing has finished or not.

Ryzhkov A., Zhang P., Reeves H., Kumjian M., Tschallener T., Trömel S.,

Simmer C. 2016: Quasi-Vertical Profiles: A New Way to Look at Polarimetric

Radar Data. JTECH vol. 33 pp 551-562

SVP#

description

Computes slanted vertical profiles, by averaging over height levels PPI data.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement,

can be any datatype supported by pyrad and available in the data
angleint 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_tolfloat: If the radar object contains an RHI volume, the tolerance in the

elevation angle for the conversion into PPI. Default 1.
lat, lonfloat: latitude and longitude of the point of interest [deg]
latlon_tolfloat: tolerance in latitude and longitude in deg. Default 0.0005
delta_rng, delta_azifloat: maximum range distance [m] and azimuth distance [degree] from the

central point of the svp containing data to average. Default 5000.

and 10.
hmaxfloat: The maximum height to plot [m]. Default 10000.
hresfloat: The height resolution [m]. Default 250.
avg_typestr: The type of averaging to perform. Can be either “mean” or “median”

Default “mean”
nvalid_minint: Minimum number of valid points to consider the data valid when

performing the averaging. Default 1
interp_kindstr: 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

returns

new_datasetdict: dictionary containing the svp and a keyword stating whether the

processing has finished or not.

Bukovcic P., Zrnic D., Zhang G. 2017: Winter Precipitation Liquid-Ice

Phase Transitions Revealed with Polarimetric Radar and 2DVD Observations

in Central Oklahoma. JTECH vol. 56 pp 1345-1363

TIME_HEIGHT#

description

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.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement,

can be any datatype supported by pyrad and available in the data
lat, lonfloat: latitude and longitude of the point of interest [deg]
latlon_tolfloat: tolerance in latitude and longitude in deg. Default 0.0005
hmaxfloat: The maximum height to plot [m]. Default 10000.
hresfloat: The height resolution [m]. Default 50
interp_kindstr: 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

returns

new_datasetdict: dictionary containing the QVP and a keyword stating whether the

processing has finished or not.

TIME_ALONG_COORD#

description

Produces time series along a particular antenna coordinate

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the time series,

can be any datatype supported by pyrad and available in the data
modestr: coordinate to extract data along. Can be ALONG_AZI, ALONG_ELE or

ALONG_RNG
fixed_range, fixed_azimuth, fixed_elevationfloat: 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_tolfloat: The angle tolerance [deg] and range tolerance [m] around the fixed

range or azimuth/elevation
value_start, value_stopfloat: The minimum and maximum value at which the data along a coordinate

start and stop

returns

new_datasetdict: dictionary containing the data and a keyword stating whether the

processing has finished or not.

SPARSE_GRID#

ZDR_COLUMN#

description

Detects ZDR columns

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“ZDR” or “ZDRc”, and,

“RhoHV” or “RhoHVc”, and,

“TEMP” or “H_ISO0”

returns

new_datasetdict: dictionary containing the output field “ZDR_col”

SUN_HITS#

description

monitoring of the radar using sun hits

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBm”, and,

“dBmv”, and,

“ZDR”, or “ZDRu”, or “ZDRu”
delev_maxfloat. Dataset keyword: maximum elevation distance from nominal radar elevation where to

look for a sun hit signal [deg]. Default 1.5
dazim_maxfloat. Dataset keyword: maximum azimuth distance from nominal radar elevation where to

look for a sun hit signal [deg]. Default 1.5
elminfloat. Dataset keyword: minimum radar elevation where to look for sun hits [deg].

Default 1.
attgfloat. Dataset keyword: gaseous attenuation. Default None
sun_positionstring. Datset keyword: The function to compute the sun position to use. Can be ‘MF’ or

‘pysolar’
sun_hit_methodstr. Dataset keyword: Method used to estimate the power of the sun hit. Can be HS

(Hildebrand and Sekhon 1974) or Ivic (Ivic 2013)
rminfloat. Dataset keyword: minimum range where to look for a sun hit signal [m]. Used in HS

method. Default 50000.
hminfloat. 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_minint. 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_rayint: 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_pwrfloat. 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_zdrfloat. Dataset keyword: maximum standard deviation of the ZDR to consider the

data a sun hit [dB]. Default 2.
az_width_cofloat. Dataset keyword: co-polar antenna azimuth width (convoluted with sun width) [deg].

Default None
el_width_cofloat. Dataset keyword: co-polar antenna elevation width (convoluted with sun width)

[deg]. Default None
az_width_crossfloat. Dataset keyword: cross-polar antenna azimuth width (convoluted with sun width)

[deg]. Default None
el_width_crossfloat. Dataset keyword: cross-polar antenna elevation width (convoluted with sun width)

[deg]. Default None
ndaysint. Dataset keyword: number of days used in sun retrieval. Default 1
coeff_bandfloat. Dataset keyword: multiplicate coefficient to transform pulse width into receiver

bandwidth
frequencyfloat. 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
beamwidthfloat. 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_widthfloat. 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_resfloat. 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, AntennaGainVfloat. 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

returns

sun_hits_dictdict: dictionary containing a radar object, a sun_hits dict and a

sun_retrieval dictionary

SUNSCAN#

description

Processing of automatic sun scans for monitoring purposes of the radar system.

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain

“dBm”, and,

“dBmv”, and,

“ZDR”, or “ZDRu”, or “ZDRu”
delev_maxfloat. Dataset keyword: maximum elevation distance from nominal radar elevation where to

look for a sun hit signal [deg]. Default 1.5
dazim_maxfloat. Dataset keyword: maximum azimuth distance from nominal radar elevation where to

look for a sun hit signal [deg]. Default 1.5
elminfloat. Dataset keyword: minimum radar elevation where to look for sun hits [deg].

Default 1.
attgfloat. Dataset keyword: gaseous attenuation. Default None
sun_positionstring. Datset keyword: The function to compute the sun position to use. Can be ‘MF’ or

‘pysolar’
sun_hit_methodstr. Dataset keyword: Method used to estimate the power of the sun hit. Should be PSR. HS

(Hildebrand and Sekhon 1974) or Ivic (Ivic 2013) are implemented

but not tested.
n_noise_binsint. Dataset keyword: Number of bins to use for noise estimation
noise_thresholdfloat. Dataset keyword: Distance over the noise level in [dBm]
min_num_samplesint. Dataset keyword: Minimal number of samples above the noise level
max_fit_stddevfloat. Dataset keyword: Maximal allowed standard deviation for a valid sun fit [dBm]
do_second_noise_eststring (‘Yes’ or ‘No’). Dataset keyword: Used to trigger a second noise estimation based on the first fit

Requires another dataset keyword: n_indfar_bins
n_indfar_binsint. Dataset keyword: Number of samples most remote from the sun center
az_width_cofloat. Dataset keyword: co-polar antenna azimuth width (convoluted with sun width) [deg].

Default None
el_width_cofloat. Dataset keyword: co-polar antenna elevation width (convoluted with sun width)

[deg]. Default None
az_width_crossfloat. Dataset keyword: cross-polar antenna azimuth width (convoluted with sun width)

[deg]. Default None
el_width_crossfloat. Dataset keyword: cross-polar antenna elevation width (convoluted with sun width)

[deg]. Default None
rminfloat. Dataset keyword: minimum range where to look for a sun hit signal [m]. Used in HS

method. Default 50000.
hminfloat. 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_minint. 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_rayint: 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
flat_reg_wlenint: Length of the flat region window [m]. Used in Ivic method. Default

8000.
iterations: int: number of iterations in step 7 of Ivic method. Default 10.
max_std_pwrfloat. 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_zdrfloat. Dataset keyword: maximum standard deviation of the ZDR to consider the

data a sun hit [dB]. Default 2.
ndaysint. Dataset keyword: number of days used in sun retrieval. Default 1
coeff_bandfloat. Dataset keyword: multiplicate coefficient to transform pulse width into receiver

bandwidth
frequencyfloat. 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
beamwidthfloat. 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_widthfloat. 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_resfloat. 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, AntennaGainVfloat. 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

returns

sunscan_datasetdict: dictionary containing a radar object, a sun_hits dict, a

sun_retrieval dictionary, field_name and timeinfo

TIMEAVG#

FLAG_TIME_AVG#

description

computes a flag field describing the conditions of the data used while averaging

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must be

“PhiDP” or “PhiDPc” (Optional, for PhiDP flagging), and,

“echoID” (Optional, for echoID flagging), and,

“hydro” (Optional, for no rain flagging), and,

“TEMP” (Optional, for solid precip flagging), and,

“H_ISO0” (Optional, also for solid precip flagging)
periodfloat. Dataset keyword: the period to average [s]. Default 3600.
start_averagefloat. Dataset keyword: when to start the average [s from midnight UTC]. Default 0.
phidpmax: float. Dataset keyword: maximum PhiDP
beamwidthfloat. 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

returns

new_datasetdict: dictionary containing the field “time_avg_flag”

TIME_AVG#

description

computes the temporal mean of a field

[Source]

parameters

datatypelist of string. Dataset keyword: Arbitrary data type supported by pyrad and contained in the radar data
periodfloat. Dataset keyword: the period to average [s]. Default 3600.
start_averagefloat. 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

returns

new_datasetdict: dictionary containing the statistic computed on the input field, as well as

“nsamples”

WEIGHTED_TIME_AVG#

description

computes the temporal mean of a field weighted by the reflectivity

[Source]

parameters

datatypelist of string. Dataset keyword: Arbitrary data type supported by pyrad and contained in the radar data, as well as

“dBZ” or “dBZc” or “dBuZ” or “dBZv” or “dBZvc” or “dBuZv” (refl. weighting)
periodfloat. Dataset keyword: the period to average [s]. Default 3600.
start_averagefloat. Dataset keyword: when to start the average [s from midnight UTC]. Default 0.

returns

new_datasetdict: dictionary containing the statistic computed on the input field

TIME_STATS#

description

computes the temporal statistics of a field

[Source]

parameters

datatypelist of string. Dataset keyword: Arbitrary data type supported by pyrad and contained in the radar data
periodfloat. Dataset keyword: the period to average [s]. If -1 the statistics are going to be

performed over the entire data. Default 3600.
start_averagefloat. 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_nanbool. Dataset keyword: If true non valid data will be used
nan_valuefloat. 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

returns

new_datasetdict: dictionary containing the statistic computed on the input field, as well as

“nsamples”, as well as

“sum2” (sum-squared) if stat in (cov, std), as well as

TIME_STATS2#

description

computes the temporal mean of a field

[Source]

parameters

datatypelist of string. Dataset keyword: Arbitrary data type supported by pyrad and contianed in the radar data
periodfloat. Dataset keyword: the period to average [s]. If -1 the statistics are going to be

performed over the entire data. Default 3600.
start_averagefloat. 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_nanbool. Dataset keyword: If true non valid data will be used
nan_valuefloat. Dataset keyword: The value of the non valid data. Default 0

returns

new_datasetdict: dictionary containing the statistic computed on the input field, as well as

“nsamples”

RAIN_ACCU#

description

Computes rainfall accumulation fields

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, must contain,

“RR”
periodfloat. Dataset keyword: the period to average [s]. If -1 the statistics are going to be

performed over the entire data. Default 3600.
start_averagefloat. Dataset keyword: when to start the average [s from midnight UTC]. Default 0.
use_nanbool. Dataset keyword: If true non valid data will be used
nan_valuefloat. Dataset keyword: The value of the non valid data. Default 0

returns

new_datasetdict: dictionary containing the output field “Raccu”

TIMESERIES#

GRID_POINT_MEASUREMENT#

description

Obtains the grid data at a point location.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
latlonboolean. Dataset keyword: if True position is obtained from latitude, longitude information,

otherwise position is obtained from grid index (iz, iy, ix).
lonfloat. Dataset keyword: the longitude [deg]. Use when latlon is True.
latfloat. Dataset keyword: the latitude [deg]. Use when latlon is True.
altfloat. Dataset keyword: altitude [m MSL]. Use when latlon is True.
iz, iy, ixint. Dataset keyword: The grid indices. Use when latlon is False
latlonTolfloat. Dataset keyword: latitude-longitude tolerance to determine which grid point to use

[deg]
altTolfloat. Dataset keyword: Altitude tolerance to determine which grid point to use [deg]

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

GRID_MULTIPLE_POINTS#

description

Obtains the grid data at a point location.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement
coord_fnamestring: File name containing the points coordinates
latlonTolfloat. Dataset keyword: latitude-longitude tolerance to determine which grid point to use

[deg]

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

MULTIPLE_POINTS#

description

Obtains the radar data at multiple points. The points are defined in a file

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement,

can be any datatype supported by pyrad and available in the data
truealtboolean. 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 is False.
coord_fnamestring: File name containing the points coordinates
alt_pointsfloat. Dataset keyword: altitude [m MSL]. Use when latlon is True.
ele_pointsfloat. Dataset keyword: radar elevation [deg]. Use when latlon is False or when latlon is

True and truealt is False
AziTolfloat. Dataset keyword: azimuthal tolerance to determine which radar azimuth to use [deg]
EleTolfloat. Dataset keyword: elevation tolerance to determine which radar elevation to use [deg]
RngTolfloat. Dataset keyword: range tolerance to determine which radar bin to use [m]

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

POINT_MEASUREMENT#

description

Obtains the radar data at a point location.

[Source]

parameters

datatypestring. Dataset keyword: The data type where we want to extract the point measurement,

can be any datatype supported by pyrad and available in the data
agg_methodstring. Dataset keyword: Which aggregation method to use to combine data that is within the

tolerance in AziTol, EleTol and RngTol

‘nearest’ : will only get nearest point to prescribed lon/lat/alt or

ele/azi/rng

‘nearest_valid’ : will only get the nearest valid point to prescribed

lon/lat/alt or ele/azi/rng (ignore missing values)

‘average’ : will average (while ignore missing values), all values

that fall within the tolerance in AziTol, EleTol and RngTol

‘none’ : will not perform any averaging and will get all values that

fall within the tolerance in AziTol, EleTol and RngTol, each

with its individual timestamp

Default is ‘nearest’
latlonboolean. Dataset keyword: if True position is obtained from latitude, longitude information,

otherwise position is obtained from antenna coordinates

(range, azimuth, elevation).
truealtboolean. 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 is False.
lonfloat. Dataset keyword: the longitude [deg]. Use when latlon is True.
latfloat. Dataset keyword: the latitude [deg]. Use when latlon is True.
altfloat. Dataset keyword: altitude [m MSL]. Use when latlon is True and truealt is True
elefloat. Dataset keyword: radar elevation [deg]. Use when latlon is False or when latlon is

True and truealt is False
azifloat. Dataset keyword: radar azimuth [deg]. Use when latlon is False
rngfloat. Dataset keyword: range from radar [m]. Use when latlon is False
AziTolfloat. Dataset keyword: azimuthal tolerance to determine which radar azimuth to use [deg]
EleTolfloat. Dataset keyword: elevation tolerance to determine which radar elevation to use [deg]
RngTolfloat. Dataset keyword: range tolerance to determine which radar bin to use [m]
fill_valuefloat or None: If not None masked values are going to be filled by this value

returns

new_datasetdict: dictionary containing the data and metadata at the point of interest

TRAJ_ANTENNA_PATTERN#

description: 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.

[Source]

parameters

returns

trajectoryTrajectory object: Object holding time series

TRAJ_ATPLANE#

description

Return time series according to trajectory

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any datatype supported by pyrad

and available in the radar data
data_is_logdict. Dataset keyword: Dictionary specifying for each field if it is in log (True) or

linear units (False). Default False
ang_tolfloat. Dataset keyword: Factor that multiplies the angle resolution. Used when determining

the neighbouring rays. Default 1.2
az_tol, el_tolfloat: azimuth and elevation tolerance (deg). Samples that have values

beyond this tolerance from the limits in azimuth and elevation of

the radar will be considered outside the sector. Default 3.
timeformatstr or None: time format of the time series output file

returns

trajectoryTrajectory object: Object holding time series

TRAJ_LIGHTNING#

description

Return time series according to lightning trajectory

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any datatype supported by pyrad

and available in the radar data
data_is_logdict. Dataset keyword: Dictionary specifying for each field if it is in log (True) or

linear units (False). Default False
ang_tolfloat. Dataset keyword: Factor that multiplies the angle resolution. Used when determining

the neighbouring rays. Default 1.2
az_tol, el_tolfloat: azimuth and elevation tolerance (deg). Samples that have values

beyond this tolerance from the limits in azimuth and elevation of

the radar will be considered outside the sector. Default 3.

returns

trajectoryTrajectory object: Object holding time series

TRAJ_ONLY#

TRAJ#

description

Return trajectory

[Source]

parameters

datatypelist of string. Dataset keyword: The input data types, can be any datatype supported by pyrad

and available in the radar data

returns

new_datasetTrajectory object: radar object