Your Turn! - SUNY Oswego SOURCE
Data from DOW6 have been processed for SOURCE and are available.
Below, a sample file has been staged for your use.
# File Location
dow_file = 'data/cfrad.20240404_060356.104_DOW6_v170_s17_el1.92_SUR.nc'
Question 1 - Easy
Similar to the previous notebook, create a PPI display of the DOW radar data
Solution - Read in the DOW file and use the plotting cell from previous notebook
# first read in pyart and associated libraries
import warnings
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import pyart
from metpy.plots import USCOUNTIES
import cartopy.crs as ccrs
import cartopy.feature as cfeature
warnings.filterwarnings('ignore')
## 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
# Read in the DOW file
radar = pyart.io.read(dow_file)
# Determine what parameters are within the radar object; similiar to previous notebook
# Note - variable names have changed and new fields added with DOW processing.
radar.fields.keys()
dict_keys(['NCP', 'TRIP_FLA', 'SNRHC', 'SNRVC', 'DBMHC', 'DBMVC', 'DBZHC', 'DBZHC_F', 'DBZVC', 'DBZVC_F', 'VEL', 'VEL_F', 'VS', 'VS_F', 'VL', 'VL_F', 'WIDTH', 'ZDRM', 'RHOHV', 'PHIDP', 'KDP'])
# inspection the radar object if you need more information
radar?
# Determine what type of file we are dealing with
# is it PPI scan, RHI scan, one elevation scan, multiple?
print('number of sweeps', radar.nsweeps)
print('number of gates', radar.ngates)
print('number of rays', radar.nrays)
number of sweeps 1
number of gates 989
number of rays 720
# Determine the range of Azimuth angles
print(np.min(radar.azimuth['data']))
print(np.max(radar.azimuth['data']))
0.3
359.8
radar.instrument_parameters.keys()
dict_keys(['frequency', 'follow_mode', 'pulse_width', 'prt_mode', 'prt', 'prt_ratio', 'polarization_mode', 'nyquist_velocity', 'unambiguous_range', 'n_samples', 'radar_antenna_gain_h', 'radar_antenna_gain_v', 'radar_beam_width_h', 'radar_beam_width_v', 'radar_rx_bandwidth', 'measured_transmit_power_v', 'measured_transmit_power_h'])
# helpful to know
radar.instrument_parameters['nyquist_velocity']
{'long_name': 'unambiguous_doppler_velocity',
'units': 'meters per second',
'_FillValue': np.float32(-9999.0),
'meta_group': 'instrument_parameters',
'data': masked_array(data=[28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533,
28.703533, 28.703533, 28.703533, 28.703533, 28.703533],
mask=False,
fill_value=np.float64(1e+20),
dtype=float32)}
# Check metadata within file for additional information on scan strategies
radar.metadata
{'Conventions': 'CF-1.7',
'Sub_conventions': 'CF-Radial instrument_parameters radar_parameters radar_calibration',
'version': 'CF-Radial-1.4',
'title': '',
'institution': '',
'references': '',
'source': '',
'history': '',
'comment': 'Written by DoradeRadxFile object',
'original_format': 'CFRADIAL',
'driver': '',
'created': '',
'start_datetime': '2024-04-04T06:03:56Z',
'time_coverage_start': '2024-04-04T06:03:56Z',
'start_time': '2024-04-04 06:03:56.104',
'end_datetime': '2024-04-04T06:04:07Z',
'time_coverage_end': '2024-04-04T06:04:07Z',
'end_time': '2024-04-04 06:04:07.977',
'instrument_name': 'DOW6low',
'site_name': 'DOW6low',
'scan_name': '',
'scan_id': np.int32(0),
'platform_is_mobile': 'false',
'n_gates_vary': 'false',
'ray_times_increase': 'true',
'volume_number': 170,
'platform_type': 'fixed',
'instrument_type': 'radar',
'primary_axis': 'axis_z'}
#-------------------
# Plot the Radar PPI
#-------------------
bounds = [43.0, 45.0, -77.5, -74.5]
fig = plt.figure(figsize=[12, 12])
ax1 = fig.add_subplot(111, projection=ccrs.PlateCarree())
#ax1 = fig.add_subplot(111)
ax1.add_feature(cfeature.STATES, linewidth=3)
ax1.add_feature(USCOUNTIES, alpha=0.4)
# Create the Radar Display Object
display = pyart.graph.RadarMapDisplay(radar)
# Plot the reflectivty
# Note - I did not add the sweep parameter since there is only one sweep per file!
display.plot_ppi_map('DBZVC_F',
ax=ax1,
vmin=-20,
vmax=45,
min_lat=bounds[0],
max_lat=bounds[1],
min_lon=bounds[2],
max_lon=bounds[3],
embellish=False,
norm=None,
cmap="pyart_ChaseSpectral")
Question 2 - Intermediate
What type of precipitation occured (i.e. hydrometeor classification) can rely upon a a variety of radar parameters. Create a display of a radar field that can used to identify specific hydrometeors.
Solution - Compare Equivalent Radar Reflectivity Factor, Differential Reflectivity, and Coorelation Coefficient
# To compare 3 simultaneous fields, we leverage matplotlib and make a multi-panel plot(s)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=[8, 16], subplot_kw={"projection": ccrs.PlateCarree()})
# adjust space between subplots
fig.subplots_adjust(hspace=0.3)
# Reflectivity
# Note - I assigned the display to axis 1 using the 'ax' feature
display.plot_ppi_map('DBZVC_F',
ax=ax1,
vmin=-20,
vmax=45,
min_lat=bounds[0],
max_lat=bounds[1],
min_lon=bounds[2],
max_lon=bounds[3],
embellish=False,
norm=None,
cmap="pyart_ChaseSpectral")
ax1.add_feature(cfeature.STATES, linewidth=3)
ax1.add_feature(USCOUNTIES, alpha=0.4)
# Differential Reflectivity
# Reflectivity
# Note - I assigned the display to axis 1 using the 'ax' feature
display.plot_ppi_map('ZDRM',
ax=ax2,
vmin=0,
vmax=1,
min_lat=bounds[0],
max_lat=bounds[1],
min_lon=bounds[2],
max_lon=bounds[3],
embellish=False,
norm=None,
cmap="pyart_ChaseSpectral")
ax2.add_feature(cfeature.STATES, linewidth=3)
ax2.add_feature(USCOUNTIES, alpha=0.4)
# Correlation Coefficient
# Note - I assigned the display to axis 1 using the 'ax' feature
display.plot_ppi_map('RHOHV',
ax=ax3,
vmin=0.5,
vmax=1,
min_lat=bounds[0],
max_lat=bounds[1],
min_lon=bounds[2],
max_lon=bounds[3],
embellish=False,
norm=None)
ax3.add_feature(cfeature.STATES, linewidth=3)
ax3.add_feature(USCOUNTIES, alpha=0.4)
# Velocity
# Note - I assigned the display to axis 1 using the 'ax' feature
display.plot_ppi_map('VEL',
ax=ax4,
min_lat=bounds[0],
max_lat=bounds[1],
min_lon=bounds[2],
max_lon=bounds[3],
embellish=False,
norm=None)
ax4.add_feature(cfeature.STATES, linewidth=3)
ax4.add_feature(USCOUNTIES, alpha=0.4)
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fc91bc79520>
Question 3 - Hard
Equivalent Radar Reflectivity factor can be used to determine precipitation rates. Create a function to calculate snowfall from this field and display snowfall rates using the following equation
Z-S Relationships
\(Z = A*S ^ {B}\)
Where:
Z = Reflectivity in dBZ
A = Coefficient applied to Z-S Relationship (not in the exponent)
S = Liquid snowfall rate
B = Coefficient applied to Z-S Relationship (in the exponent)
We also need to apply a snow water equivalent ratio (swe) to convert from liquid to snow (ex. 8 inches of snow –> 1 inch of rain would be 8.0).
This equation now becomes:
\(Z = swe*A*S ^ {B}\)
Solving for S, we get:
\(S = swe * (\frac{z}{a})^{1/B}\)
Where z is reflectivity in units of dB (\(z =10^{Z/10}\))
Solution - Calculate new field from above equation and reflectivity, add it back into the radar object, and display
def snow_rate(radar, swe_ratio, A, B):
"""
Snow rate applied to a pyart.Radar object
Takes a given Snow Water Equivilent ratio (swe_ratio), A and B value
for the Z-S relationship and creates a radar field similar to DBZ
showing the radar estimated snowfall rate in mm/hr. Then the given
SWE_ratio, A and B are stored in the radar metadata for later
reference.
"""
# Setting up for Z-S relationship:
snow_z = radar.fields['DBZHC_F']['data'].copy()
# Convert it from dB to linear units
z_lin = 10.0**(radar.fields['DBZHC_F']['data']/10.)
# Apply the Z-S relation.
snow_z = swe_ratio * (z_lin/A)**(1./B)
# Add the field back to the radar. Use reflectivity as a template
radar.add_field_like('DBZHC_F', 'snow_z', snow_z,
replace_existing=True)
# Update units and metadata
radar.fields['snow_z']['units'] = 'mm/h'
radar.fields['snow_z']['standard_name'] = 'snowfall_rate'
radar.fields['snow_z']['long_name'] = 'snowfall_rate_from_z'
radar.fields['snow_z']['valid_min'] = 0
radar.fields['snow_z']['valid_max'] = 500
radar.fields['snow_z']['swe_ratio'] = swe_ratio
radar.fields['snow_z']['A'] = A
radar.fields['snow_z']['B'] = B
return radar
Call the above function to calculate snowfall rates with your chosen relationship
Reference |
A |
B |
Band |
|---|---|---|---|
Matrosov et al.(2009) Braham(1990) |
67 |
1.28 |
X |
radar = snow_rate(radar, 8.5, 67, 1.28)
# Check the new field, snow_z, is within the radar object
radar.fields.keys()
dict_keys(['NCP', 'TRIP_FLA', 'SNRHC', 'SNRVC', 'DBMHC', 'DBMVC', 'DBZHC', 'DBZHC_F', 'DBZVC', 'DBZVC_F', 'VEL', 'VEL_F', 'VS', 'VS_F', 'VL', 'VL_F', 'WIDTH', 'ZDRM', 'RHOHV', 'PHIDP', 'KDP', 'snow_z'])
# Now display the snowfall rates
#-------------------
# Plot the Radar PPI
#-------------------
bounds = [43.0, 45.0, -77.5, -74.5]
fig = plt.figure(figsize=[12, 12])
ax1 = fig.add_subplot(111, projection=ccrs.PlateCarree())
#ax1 = fig.add_subplot(111)
ax1.add_feature(cfeature.STATES, linewidth=3)
ax1.add_feature(USCOUNTIES, alpha=0.4)
# Create the Radar Display Object
display = pyart.graph.RadarMapDisplay(radar)
# Plot the reflectivty
# Note - I did not add the sweep parameter since there is only one sweep per file!
display.plot_ppi_map('snow_z',
ax=ax1,
vmin=0,
vmax=15,
min_lat=bounds[0],
max_lat=bounds[1],
min_lon=bounds[2],
max_lon=bounds[3],
embellish=False,
norm=None,
cmap='Blues')
