LIDAR ppi converation in gridded format. #1720
Comments
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@priya1809 Hello, are there values in RWS when you have it in the radar object and when you plot? I see you have #success so double checking? If so, you might need to add a gatefilter and masked the invalid before gridding. |
data is there, after gridding I am unable to get that data I'm not sure how to apply gatefilter here if I have one field "rws" it. Is there an alternative approach or any guidance on applying Gatefilter in this specific case? |
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A gatefilter within the gridding call? |
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I apologize for the confusion in my previous response. Upon re-examining my process, I realized that I didn't apply the Gatefilter in this case. I re-edited my reply. please check. |
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Try using: |
It still same: grid = pyart.map.grid_from_radars([radar], grid_limits=grid_limits, grid_shape=grid_shape, gatefilters=gatefilter) radar.fields['rws'] np.nanmin(ds['rws']) np.nanmax(ds['rws']) |
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What does the grid.fields['rws']['data'] look like? Trying to pinpoint if its the xarray dataset or the grid object. |
mask=[[[ True, True, True, ..., True, True, True], fill_value=1e+20, Please download the file from here. https://drive.google.com/drive/u/0/folders/1-N323zsaSfNy7C60kqE780j1kf2m-UPQ |
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@priya1809 This could also be a radius of influence issue as well. I'm wondering if when gridding there isn't enough data points when its searching around to interpolate. What does upping the ROI do for you? |
grid.fields['ROI']['data'] ds['ROI'] ... Coordinates:
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@zssherman I've shared the data with you. Could you kindly try processing it? I've provided the exact data that I'm working with now. Your assistance would be greatly appreciated. |
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@priya1809 Talked to some folks, so lidar is outside my expertise, but can I suggest opening a forum here as its more science related: There might be some folks who can guide the right parameters for gridding lidar data. I still think this is a ROI issue, but in terms of the right values I'm not 100% sure. |
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@zssherman Thank you so much for your suggestion. |
I have LiDAR data that I'd like to convert to radar format (because I want to use pyart package) and then grid it. However, after gridding, I noticed that the ds.rsw values are NaN. Could you please advise on how to retrieve valid values for ds.rsw?
import pyart
import numpy as np
from datetime import datetime
from netCDF4 import Dataset
import warnings
warnings.filterwarnings('ignore')
Load data lidar data
file_path = "D:/lidar/ncfiles/WCS000248_2023-09-23_09-50-36_ppi_40_100m.nc"
data = Dataset(file_path)
sweep_group = data.groups["Sweep_860123"]
time = sweep_group.variables["time"][:]
latitude = data.variables["latitude"][:]
longitude = data.variables["longitude"][:]
altitude = data.variables["altitude"][:]
azimuth = sweep_group.variables["azimuth"][:]
elevation = sweep_group.variables["elevation"][:]
range_ = sweep_group.variables["range"][:]
radial_wind_speed = sweep_group.variables["radial_wind_speed"][:]
rsw = np.array(radial_wind_speed)
Create radar object using pyart
radar = pyart.testing.make_empty_ppi_radar(rsw.shape[1], len(azimuth), 1)
radar.latitude['data'] = np.array([latitude])
radar.longitude['data'] = np.array([longitude])
radar.altitude['data'] = np.array([altitude])
#radar.time['data'] = np.array([(t - time_converted[0]).total_seconds() for t in time_converted])
radar.time = {
'standard_name': 'time',
'long_name': 'time in seconds since volume start',
'calendar': 'gregorian',
'units': 'seconds since 2023-09-23T04:35:05Z',
'comment': 'times are relative to the volume start_time',
'data': np.array([(t - time_converted[0]).total_seconds() for t in time_converted]),
'FillValue':1e+20
}
radar.azimuth['data'] = azimuth
radar.elevation['data'] = elevation
radar.range['data'] = range
radial_wind_speed_dict = {
'long_name': 'radial_wind_speed',
'standard_name': 'radial_wind_speed_of_scatterers_away_from_instrument',
'units': 'm/s',
'sampling_ratio': 1.0,
'_FillValue': -9999 ,
'grid_mapping': 'grid_mapping',
'coordinates': 'time range',
'data': np.ma.masked_invalid(rsw) # Mask invalid data
}
radar.fields = { 'rws': radial_wind_speed_dict}
#success plot this:
import matplotlib.pyplot as plt
from pyart.graph import RadarDisplay
display = RadarDisplay(radar)
fig, ax = plt.subplots(figsize=(10, 8))
display.plot_ppi("rws", sweep=0, ax=ax, cmap="coolwarm")
plt.show()
Now grid
grid_limits = ((10., 4000.), (-4500., 4500.), (-4500., 4500.))
grid_shape = (20, 50, 50)
grid = pyart.map.grid_from_radars([radar], grid_limits=grid_limits, grid_shape=grid_shape)
ds = grid_dv.to_xarray()
print(ds)
print(ds)
<xarray.Dataset> Size: 641kB
Dimensions: (time: 1, z: 20, y: 50, x: 50, nradar: 1)
Coordinates: (12/16)
lat (y, x) float64 20kB 45.02 45.02 ... 45.1 45.1
lon (y, x) float64 20kB 7.603 7.605 ... 7.715 7.717
...
origin_altitude (time) float64 8B nan
radar_altitude (nradar) float64 8B nan
radar_latitude (nradar) float64 8B 45.06
radar_longitude (nradar) float64 8B 7.66
radar_time (nradar) int64 8B 0
radar_name (nradar) <U10 40B 'fake_radar'
Dimensions without coordinates: nradar
Data variables:
rws (time, z, y, x) float64 400kB nan nan ... nan
ROI (time, z, y, x) float32 200kB 500.0 ... 500.0
Attributes:
radar_name: fake_radar
nradar: 1
instrument_name: fake_radar
np.nanmax(ds.rws)
Out[18]: nan
np.nanmin(ds.rws)
Out[19]: nan
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