PYIWR

Importing the pyiwr Library

import pyiwr


## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly
## supported by the U.S. Department of Energy as part of the Atmospheric
## Radiation Measurement (ARM) Climate Research Facility, an Office of
## Science user facility.
##
## If you use this software to prepare a publication, please cite:
##
##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119

Radar object formating for dual-pol raw dwr file

File location

filename = '/media/sigma/Backup Plus/Nitig/radar/Nitig_data/rawdwr/Files/cherrapunjee_weather_2021_08_06_00_37_26_dsfisro6n.dwr'

filename = 'cherrapunjee_weather_2021_08_06_00_37_26_dsfisro6n.dwr'

radar = pyiwr.format_transform.raw2nc (filename, save_file = True)

Processing file: cherrapunjee_weather_2021_08_06_00_37_26_dsfisro6n.dwr
File cherrapunjee_weather_2021_08_06_00_37_26_dsfisro6n.dwr converted successfully and saved in the "radar_ncfiles" folder

radar.info()

altitude:
	data: <ndarray of type: int32 and shape: (1,)>
	long_name: Altitude
	units: meters
	standard_name: Altitude
	positive: up
altitude_agl: None
antenna_transition:
	data: <ndarray of type: float64 and shape: (3600,)>
azimuth:
	data: <ndarray of type: float32 and shape: (3600,)>
	long_name: azimuth_angle_from_true_north
	units: degrees
	standard_name: beam_azimuth_angle
	axis: radial_azimuth_coordinate
	comment: Azimuth of antenna relative to true north
elevation:
	data: <ndarray of type: float64 and shape: (3600,)>
	long_name: elevation_angle_from_horizontal_plane
	units: degrees
	standard_name: beam_elevation_angle
	axis: radial_elevation_coordinate
	comment: Elevation of antenna relative to the horizontal plane
fields:
	DBZ:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: nan
		units: dBZ
		standard_name: equivalent_reflectivity_factor
		Polarization: Horizontal
	VEL:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: nan
		units: m/s
		standard_name: radial_velocity_of_scatterers_away_from_instrument
		Polarization: Horizontal
	WIDTH:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: nan
		units: m/s
		standard_name: doppler_spectrum_width
		Polarization: Horizontal
	ZDR:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: nan
		units: dB
		standard_name: log_differential_reflectivity_hv
		Polarization: Horizontal and Vertical
	PHIDP:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: nan
		units: degrees
		standard_name: differential_phase_hv
		Polarization: Horizontal and Vertical
	RHOHV:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: nan
		units: unitless
		standard_name: cross_correlation_ratio_hv
		Polarization: Horizontal and Vertical
fixed_angle:
	data: <ndarray of type: float32 and shape: (10,)>
	long_name: Target angle for sweep
	units: degrees
	standard_name: target_fixed_angle
instrument_parameters: None
latitude:
	data: <ndarray of type: float64 and shape: (1,)>
	long_name: Latitude
	units: degrees_north
	standard_name: Latitude
longitude:
	data: <ndarray of type: float64 and shape: (1,)>
	long_name: Longitude
	units: degrees_east
	standard_name: Longitude
nsweeps: 10
ngates: 1600
nrays: 3600
radar_calibration: None
range:
	data: <ndarray of type: float64 and shape: (1600,)>
	long_name: range_to_measurement_volume
	units: meters
	standard_name: projection_range_coordinate
	axis: radial_range_coordinate
	spacing_is_constant: true
	comment: Coordinate variable for range. Range to center of each bin.
scan_rate: None
scan_type: ppi
sweep_end_ray_index:
	data: <ndarray of type: int64 and shape: (10,)>
	long_name: Index of last ray in sweep, 0-based
	units: count
sweep_mode:
	data: <ndarray of type: |S1 and shape: (10, 32)>
	long_name: Sweep mode
	units: unitless
	standard_name: sweep_mode
	comment: Options are: "sector", "coplane", "rhi", "vertical_pointing", "idle", "azimuth_surveillance", "elevation_surveillance", "sunscan", "pointing", "manual_ppi", "manual_rhi"
sweep_number:
	data: <ndarray of type: int64 and shape: (10,)>
	long_name: Sweep number
	units: count
	standard_name: sweep_number
sweep_start_ray_index:
	data: <ndarray of type: int64 and shape: (10,)>
	long_name: Index of first ray in sweep, 0-based
	units: count
target_scan_rate: None
time:
	data: <ndarray of type: float64 and shape: (3600,)>
	long_name: time_in_seconds_since_volume_start
	units: seconds since 2021-08-06T00:37:26Z
	standard_name: time
	calendar: gregorian
metadata:
	instrument_name: Cherrapunji S-band Dual-pol DWR
	Created using: pyiwr (Indian Weather Radar Toolkit) Module developed at SIGMA Research Lab, IIT Indore
	version: Version 1.0.0
	title: Cherrapunji S-band Dual-pol DWR data
	institution: ISRO
	references: Py-art_https://arm-doe.github.io/pyart/notebooks/basic_ingest_using_test_radar_object.html
	source: DWR Short Volume Scan File (250km)
	comment: 
	Conventions: CF/Radial
	history: DWR raw (6n.dwr) data file encoded into Py-ART compatible NetCDF file
	field_names: DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV
	volume_number: 0
	platform_type: fixed
	instrument_type: radar
	primary_axis: axis_z

Visualizations

For PPI at specific elevation

pyiwr.visualize.elevation(radar, field_name='DBZ', elevation_index=0, rings=True, grid=True,
range_in_km= True, save_image= False, img_name='img.png')

For PPI of any field at all elevation

pyiwr.visualize.all_elevation(radar, field_name='DBZ', rings=True, grid=True, range_in_km=True,
save_image= False, img_name='img.png')

For PPI of all fields

pyiwr.visualize.fields_elevation(radar, elevation_index=0, range_in_km=True, rings=True, grid=True,
save_image= False, img_name='img.png')

For open source nc files needing correction or restructuring

File location

filename = '/media/sigma/Backup Plus/Nitig/radar/Nitig_data/NC FILES/sweep10/RSCHR_02APR2022_133109_L2B_STD.nc'

#For mosdac nc files
radar = pyiwr.format_transform.correctednc (filename, save_file = False)

radar.info()
radar.info()

Processing file:  RSCHR_02APR2022_133109_L2B_STD.nc
File RSCHR_02APR2022_133109_L2B_STD.nc corrected and restructured successfully
altitude:
	data: <ndarray of type: float64 and shape: (1,)>
	_FillValue: nan
	units: meters
altitude_agl: None
antenna_transition: None
azimuth:
	data: <ndarray of type: float32 and shape: (3600,)>
	_FillValue: nan
	standard_name: ray_azimuth_angle
	units: degrees
	long_name: azimuth_angle_from_true_north
	axis: radial_azimuth_coordinate
elevation:
	data: <ndarray of type: float32 and shape: (3600,)>
	_FillValue: nan
	standard_name: ray_elevation_angle
	units: degrees
	long_name: elevation_angle_from_horizontal_plane
	axis: radial_azimuth_coordinate
fields:
	DBZ:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: 0.0
		units: dBZ
		standard_name: equivalent_reflectivity_factor
		Polarization: Horizontal
		coordinates: time range
	VEL:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: 0.0
		units: m/s
		standard_name: radial_velocity_of_scatterers_ away_from_instrument
		Polarization: Horizontal
		coordinates: time range
	WIDTH:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: 0.0
		units: m/s
		standard_name: doppler_spectrum_width
		Polarization: Horizontal
		coordinates: time range
	ZDR:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: 0.0
		units: dB
		standard_name: log_differential_reflectivity_hv
		Polarization: Horizontal and Vertical
		coordinates: time range
	PHIDP:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: 0.0
		units: degrees
		standard_name: differential_phase_hv
		Polarization: Horizontal and Vertical
		coordinates: time range
	RHOHV:
		data: <ndarray of type: float32 and shape: (3600, 1600)>
		_FillValue: 0.0
		units: 
		standard_name: cross_correlation_ratio_hv
		Polarization: Horizontal and Vertical
		coordinates: time range
fixed_angle:
	data: <ndarray of type: float32 and shape: (10,)>
	_FillValue: nan
	units: degrees
instrument_parameters: None
latitude:
	data: <ndarray of type: float64 and shape: (1,)>
	_FillValue: nan
	units: degrees_north
longitude:
	data: <ndarray of type: float64 and shape: (1,)>
	_FillValue: nan
	units: degrees_east
nsweeps: 10
ngates: 1600
nrays: 3600
radar_calibration: None
range:
	data: <ndarray of type: float64 and shape: (1600,)>
	_FillValue: nan
	standard_name: projection_range_coordinate
	long_name: range_to_measurement_volume
	units: meters
	spacing_is_constant: true
	meters_to_center_of_first_gate: true
	axis: radial_range_coordinate
scan_rate: None
scan_type: ppi
sweep_end_ray_index:
	data: <ndarray of type: int64 and shape: (10,)>
sweep_mode:
	data: <ndarray of type: |S1 and shape: (10, 32)>
sweep_number:
	data: <ndarray of type: int32 and shape: (10,)>
sweep_start_ray_index:
	data: <ndarray of type: int64 and shape: (10,)>
target_scan_rate: None
time:
	data: <ndarray of type: float64 and shape: (3600,)>
	_FillValue: nan
	standard_name: time
	long_name: time_in_seconds_since_volume_start
	units: seconds since 2022-04-02T13:31:09Z
	calendar: gregorian
metadata:
	instrument_name: Cherrapunji S-band Dual-pol DWR
	Created using: pyiwr (Python Indian Weather Radar Toolkit) Module developed by Researchers at SIGMA Research Lab, IIT Indore
	version: Version 1.0.0
	title: Cherrapunji S-band Dual-pol DWR data
	institution: ISRO
	references: Py-art_https://arm-doe.github.io/pyart/notebooks/basic_ingest_using_test_radar_object.html
	source: DWR volume scan data
	comment: 
	Conventions: CF/Radial
	field_names: DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV
	history: DWR mosdac files (.nc) data encoded into Py-ART compatible NetCDF file
	volume_number: 0
	platform_type: fixed
	instrument_type: radar
	primary_axis: axis_z

Visualizations

For PPI at specific elevation

pyiwr.visualize.elevation(radar, field_name='DBZ', elevation_index=0, rings=True, grid=True,
range_in_km= True, save_image= False, img_name='img.png')

For PPI of any field at all elevation

pyiwr.visualize.all_elevation(radar, field_name='DBZ', rings=True, grid=True, range_in_km=True,
save_image= False, img_name='img.png')

For PPI of all fields

pyiwr.visualize.fields_elevation(radar, elevation_index=0, range_in_km=True, rings=True, grid=True,
save_image= False, img_name='img.png')

For gridded objects NC file from a radar object NetCDF files

File location

xarray_grids = pyiwr.format_transform.sweeps2gridnc (filename, grid_shape =(81 , 501 , 501) , height =20
, length =250 , save_file = False)

Processing file:  RSCHR_02APR2022_133109_L2B_STD.nc
Xarray gridding of volumetric sweeps of radar PPI scan file: RSCHR_02APR2022_133109_L2B_STD.nc done successfully

xarray_grids.info()

xarray.Dataset {
dimensions:
	time = 1 ;
	z = 81 ;
	y = 501 ;
	x = 501 ;

variables:
	object time(time) ;
	float64 z(z) ;
		z:standard_name = projection_z_coordinate ;
		z:long_name = Z distance on the projection plane from the origin ;
		z:axis = Z ;
		z:units = m ;
		z:positive = up ;
	float64 lat(y, x) ;
		lat:long_name = latitude of grid cell center ;
		lat:units = degree_N ;
		lat:standard_name = Latitude ;
	float64 lon(y, x) ;
		lon:long_name = longitude of grid cell center ;
		lon:units = degree_E ;
		lon:standard_name = Longitude ;
	float64 y(y) ;
		y:standard_name = projection_y_coordinate ;
		y:long_name = Y distance on the projection plane from the origin ;
		y:axis = Y ;
		y:units = m ;
	float64 x(x) ;
		x:standard_name = projection_x_coordinate ;
		x:long_name = X distance on the projection plane from the origin ;
		x:axis = X ;
		x:units = m ;
	float32 DBZ(time, z, y, x) ;
		DBZ:_FillValue = 0.0 ;
		DBZ:units = dBZ ;
		DBZ:standard_name = equivalent_reflectivity_factor ;
		DBZ:Polarization = Horizontal ;
		DBZ:coordinates = time range ;
	float32 VEL(time, z, y, x) ;
		VEL:_FillValue = 0.0 ;
		VEL:units = m/s ;
		VEL:standard_name = radial_velocity_of_scatterers_ away_from_instrument ;
		VEL:Polarization = Horizontal ;
		VEL:coordinates = time range ;
	float32 WIDTH(time, z, y, x) ;
		WIDTH:_FillValue = 0.0 ;
		WIDTH:units = m/s ;
		WIDTH:standard_name = doppler_spectrum_width ;
		WIDTH:Polarization = Horizontal ;
		WIDTH:coordinates = time range ;
	float32 ZDR(time, z, y, x) ;
		ZDR:_FillValue = 0.0 ;
		ZDR:units = dB ;
		ZDR:standard_name = log_differential_reflectivity_hv ;
		ZDR:Polarization = Horizontal and Vertical ;
		ZDR:coordinates = time range ;
	float32 PHIDP(time, z, y, x) ;
		PHIDP:_FillValue = 0.0 ;
		PHIDP:units = degrees ;
		PHIDP:standard_name = differential_phase_hv ;
		PHIDP:Polarization = Horizontal and Vertical ;
		PHIDP:coordinates = time range ;
	float32 RHOHV(time, z, y, x) ;
		RHOHV:_FillValue = 0.0 ;
		RHOHV:units =  ;
		RHOHV:standard_name = cross_correlation_ratio_hv ;
		RHOHV:Polarization = Horizontal and Vertical ;
		RHOHV:coordinates = time range ;
	float32 ROI(time, z, y, x) ;
		ROI:standard_name = radius_of_influence ;
		ROI:long_name = Radius of influence for mapping ;
		ROI:units = m ;
		ROI:least_significant_digit = 1 ;
		ROI:_FillValue = -9999.0 ;

// global attributes:
	:instrument_name = Cherrapunji S-band Dual-pol DWR ;
	:Created using = pyiwr (Python Indian Weather Radar Toolkit) Module developed by Researchers at SIGMA Research Lab, IIT Indore ;
	:version = Version 1.0.0 ;
	:title = Cherrapunji S-band Dual-pol DWR data ;
	:institution = ISRO ;
	:references = Py-art_https://arm-doe.github.io/pyart/notebooks/basic_ingest_using_test_radar_object.html ;
	:source = DWR volume scan data ;
	:comment =  ;
	:Conventions = CF/Radial ;
	:field_names = DBZ, VEL, WIDTH, ZDR, PHIDP, RHOHV ;
	:history = DWR mosdac files (.nc) data encoded into Py-ART compatible NetCDF file ;
	:volume_number = 0 ;
	:platform_type = fixed ;
	:instrument_type = radar ;
	:primary_axis = axis_z ;
}

For CAPPI in cartesian plane.

pyiwr.visualize.cappi(xarray_grids, altitude_level =3, field_name ='DBZ', radar_location ='CHERRAPUNJI',
grid=True, rings =False, ticks_in_km =True, save_image =False, img_name ='img.png')

For MAX CAPPI in cartesian plane

pyiwr.visualize.cappi_max(xarray_grids, field_name ='DBZ', radar_location ='CHERRAPUNJI',
grid=True, rings =False, ticks_in_km =True, save_image =False, img_name ='img.png')

For MAX CAPPI (with marginal cross-sections) in cartesian plane

pyiwr.visualize.marginal_max(xarray_grids, field_name ='DBZ', radar_location ='CHERRAPUNJI', rings
=True, grid =True, cross_sections =True, save_image =False, img_name ='img.png')

For MAX CAPPI (with marginal cross-sections) with map

pyiwr.visualize.marginal_max_map(xarray_grids, radar_location = 'CHERRAPUNJI', field_name ='DBZ',
background ='terrain-background', cross_sections = False, save_image = False, img_name ='img.png')

Example Usage

Multiple output for single cell

Importing Essential Libraries

Dual - Polarized DWR FILES

Defining File Path

TERLS RADAR

Reading content of file and making radar object

Visualization in Cartesian Plane

PPI Plot of any Radar Field at any given elevation angle

PPI Plots at all elevation angle of any Radar Field

PPI Plots of all Radar Fields at any given elevation angle

Reading content of file and making grid object

Visualization in Cartesian Plane

CAPPI@3km

MAXZ CAPPI

Marginal Distribution of MAXZ CAPPI

Marginal Distribution of MAXZ CAPPI with background map

Reading content of file and making radar object

SOHRA RADAR

Visualization in Cartesian Plane

PPI Plot of any Radar Field at any given elevation angle

PPI Plots at all elevation angle of any Radar Field

PPI Plots of all Radar Fields at any given elevation angle

Reading content of file and making grid object

Visualization in Cartesian Plane

CAPPI@3km

MAXZ CAPPI

Marginal Distribution of MAXZ CAPPI

Marginal Distribution of MAXZ CAPPI with background map

SHAR RADAR

Reading content of file and making radar object

Visualization in Cartesian Plane

PPI Plot of any Radar Field at any given elevation angle

PPI Plots at all elevation angle of any Radar Field

PPI Plots of all Radar Fields at any given elevation angle

Reading content of file and making grid object

Visualization in Cartesian Plane

CAPPI@3km

MAXZ CAPPI

Marginal Distribution of MAXZ CAPPI

Marginal Distribution of MAXZ CAPPI with background map

THANK YOU

For any problem Please contact the developers

PYIWR

Importing the pyiwr Library

Radar object formating for dual-pol raw dwr file

File location

Visualizations

For PPI at specific elevation

For PPI of any field at all elevation

For PPI of all fields

For open source nc files needing correction or restructuring

File location

Visualizations

For PPI at specific elevation

For PPI of any field at all elevation

For PPI of all fields

For gridded objects NC file from a radar object NetCDF files

File location

For CAPPI in cartesian plane.

For MAX CAPPI in cartesian plane

For MAX CAPPI (with marginal cross-sections) in cartesian plane

For MAX CAPPI (with marginal cross-sections) with map

Thank You