AUX_MRC_1B
Contents
AUX_MRC_1B¶
Abstract: Access to auxiliary MRC product
%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib
Python implementation: CPython
Python version : 3.9.7
IPython version : 8.0.1
viresclient: 0.11.0
pandas : 1.4.1
xarray : 0.21.1
matplotlib : 3.5.1
from viresclient import AeolusRequest
import datetime as dt
import matplotlib.pyplot as plt
request = AeolusRequest()
Product information¶
Description of product.
Documentation:
request.set_collection('AUX_MRC_1B')
request.set_fields(fields=[
"altitude",
"satellite_range",
"normalised_useful_signal",
"mie_scattering_ratio",
"lat_of_DEM_intersection",
"lon_of_DEM_intersection",
"time_freq_step",
"frequency_offset",
"frequency_valid",
"measurement_response",
"measurement_response_valid",
"measurement_error_mie_response",
"reference_pulse_response",
"reference_pulse_response_valid",
"reference_pulse_error_mie_response",
"num_measurements_usable",
"num_valid_measurements",
"num_reference_pulses_usable",
"num_mie_core_algo_fails_measurements",
"num_ground_echoes_not_detected_measurements",
"measurement_mean_sensitivity",
"measurement_zero_frequency",
"measurement_error_mie_response_std_dev",
"measurement_offset_frequency",
"reference_pulse_mean_sensitivity",
"reference_pulse_zero_frequency",
"reference_pulse_error_mie_response_std_dev",
"reference_pulse_offset_frequency",
"satisfied_min_valid_freq_steps_per_cal",
"freq_offset_data_monotonic",
"num_of_valid_frequency_steps",
"measurement_mean_sensitivity_valid",
"measurement_error_response_std_dev_valid",
"measurement_zero_frequency_response_valid",
"measurement_data_monotonic",
"reference_pulse_mean_sensitivity_valid",
"reference_pulse_error_response_std_dev_valid",
"reference_pulse_zero_frequency_response_valid",
"reference_pulse_data_monotonic",
"mie_core_measurement_FWHM",
"mie_core_measurement_amplitude",
"mie_core_measurement_offset"
])
data = request.get_between(
start_time="2020-02-24T18:15:22Z",
end_time="2020-02-24T18:29:46Z",
filetype="nc",
asynchronous=False
)
ds = data.as_xarray()
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20,30)
pars = [
"lat_of_DEM_intersection",
"lon_of_DEM_intersection",
"frequency_valid",
"measurement_response",
"measurement_error_mie_response",
"reference_pulse_response",
"reference_pulse_response_valid",
"reference_pulse_error_mie_response",
"num_valid_measurements",
]
fig, axs = plt.subplots(len(pars))
for idx, p in enumerate(pars):
print(axs[idx])
axs[idx].plot(
ds.frequency_offset.values,
ds[p].values
)
axs[idx].scatter(
ds.frequency_offset.values,
ds[p].values
)
axs[idx].set_title(p)
AxesSubplot(0.125,0.808774;0.775x0.0712264)
AxesSubplot(0.125,0.723302;0.775x0.0712264)
AxesSubplot(0.125,0.63783;0.775x0.0712264)
AxesSubplot(0.125,0.552358;0.775x0.0712264)
AxesSubplot(0.125,0.466887;0.775x0.0712264)
AxesSubplot(0.125,0.381415;0.775x0.0712264)
AxesSubplot(0.125,0.295943;0.775x0.0712264)
AxesSubplot(0.125,0.210472;0.775x0.0712264)
AxesSubplot(0.125,0.125;0.775x0.0712264)
