An error occurred while reading CAPPI file:ValueError: negative dimensions are not allowed

[hanzhe910]

I'm very sorry, I have a very simple question that I hope someone can help me with, I'm trying to open a CAPPI file with pyart, but my code is reporting an error as follows:

ValueError Traceback (most recent call last)
Cell In[11], line 9
3 #pyart.map.grid
5 radar_file_path = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/CAP_V_030_256_20180916110009.CAPSPTC'
----> 9 radar = pyart.io.read(radar_file_path)
12 display = pyart.graph.RadarDisplay(radar)
15 plt.figure(figsize=(10, 6))

File ~/anaconda3/envs/pyart/lib/python3.10/site-packages/pyart/io/auto_read.py:125, in read(filename, use_rsl, **kwargs)
123 return read_rsl(filename, **kwargs)
124 else:
--> 125 return read_sigmet(filename, **kwargs)
126 if filetype == "UF":
127 if use_rsl:

File ~/anaconda3/envs/pyart/lib/python3.10/site-packages/pyart/io/sigmet.py:147, in read_sigmet(filename, field_names, additional_metadata, file_field_names, exclude_fields, include_fields, time_ordered, full_xhdr, noaa_hh_hdr, debug, ignore_xhdr, ignore_sweep_start_ms, **kwargs)
144 full_xhdr = False
146 # read data
--> 147 sigmet_data, sigmet_metadata = sigmetfile.read_data(full_xhdr=full_xhdr)
148 first_data_type = sigmetfile.data_type_names[0]
149 if first_data_type == "XHDR": # don't use XHDR as the first data type

File ~/anaconda3/envs/pyart/lib/python3.10/site-packages/pyart/io/_sigmetfile.pyx:133, in pyart.io._sigmetfile.SigmetFile.read_data()

File ~/anaconda3/envs/pyart/lib/python3.10/site-packages/numpy/ma/core.py:8442, in _convert2ma.call(self, *args, **params)
8440 _extras[p] = params.pop(p)
8441 # Get the result
-> 8442 result = self._func.call(*args, **params).view(MaskedArray)
8443 if "fill_value" in common_params:
8444 result.fill_value = _extras.get("fill_value", None)

ValueError: negative dimensions are not allowed

========================================================================================
Meanwhile, my code is:

import matplotlib.pyplot as plt
import pyart
radar_file_path = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/CAP_V_030_256_20180916110009.CAPSPTC'
radar = pyart.io.read(radar_file_path)
display = pyart.graph.RadarDisplay(radar)
plt.figure(figsize=(10, 6))
sweep = 0
display.plot('reflectivity', sweep=sweep, title='Radar Reflectivity', colorbar_label='dBZ')
plt.show()

I don't know if this is because my file is from an official organization and they identify it as cappi, or maybe it is because I don't have the TRMM Radar Software Library(RSL) installed. But I do not know how to install RSL, this does not seem to be a python package, could someone please help me, thank you very much！！！！

[zssherman]

@hanzhe910 Hello! Currently there is no CAPPI functionality in Py-ART. However If the file is gridded, you could try pyart.io.read_grid instead for the file and see if that works? If that doesn't we would have to see where the negative dimensions are coming from?

[hanzhe910]

see

Thank you very much for your reply. I am thrilled to receive a response from the developer of pyart. My CAPPI file is not in netCDF4 format, so it doesn't have a grid, but that's okay. I have communicated with the relevant personnel and am preparing to process the RAW file. Pyart is the best radar data processing package I have ever used. Now, I would like to convert radar data into gridded data. Could you please let me know what formula is used in pyart to convert upper-level winds to surface winds?

[hanzhe910]

@zssherman
Hello,

I have another question that I would greatly appreciate your help with. Due to the elevation angle coverage of my radar, three files are generated:

PPIVOL_A: 0.1 deg
PPIVOL_B: 0.9, 1.8, 2.7, 3.6, 5.4 deg
PPIVOL_C: 10, 15, 22, 34 deg
I would like to obtain the wind speed at an altitude of 3 km. Therefore, my idea is to combine these three files together. My proposed steps are as follows:

Read the three raw files.
Correct the wind speeds in the three files.
Grid the three files to the 3 km altitude.
Merge the three gridded files together.
Could you please let me know if this approach is feasible? I have noticed that all these steps can be accomplished using pyart. However, I have another question: when I use pyart.map.grid_from_radars() to grid the three files, does pyart take into account the tilt angles of the wind speeds?

Thank you very much for your assistance!!!!!!

[zssherman]

@hanzhe910 In terms of converting upper level winds to surface winds, I'm not entirely sure. Someone else might have an idea on that. For joining radars, we do have a pyart.utils.join_radar that will allow you to join radars together. You could join them by the order of the sweeps so ppi_volA with B then those two with C i think would work. You could also use the pyart.correct.region_delias to correct velocities. The pyart horizontal wind profile class could help possibly as well if i understand the problem correctly. Or could use the vad_browning pyart.retrieve function and specify the height you want to obtain a VAD ?

[hanzhe910]

@zssherman
Thank you very, very much for your detailed and helpful response. I am extremely grateful for your assistance and the valuable insights you provided. Your suggestions have been incredibly beneficial, and I believe I can use the pyart.retrieve.vad_michelson function to perform the VAD analysis and address my issue.

However, I have one more question. When reading the radar wind data, I understand that it refers to the radial wind, which forms an angle with the ground and is not the horizontal wind. How can I obtain the horizontal wind from this data? Does Py-ART provide functionality to achieve this, or can the projection feature in pyart.map.map_to_grid be used for this purpose?

Thank you so much once again for your invaluable help and prompt response.

[hanzhe910]

@zssherman

I have successfully merged radar data and applied the dealias_region_based function, followed by gridding using pyart.map.grid_from_radars. Here is my code:

==============================================================================
import os
import matplotlib.pyplot as plt
import pyart
import numpy as np
import xarray as xr

radar_file_path1 = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/radar1'
radar_file_path2 = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/radar2'
radar_file_path3 = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/radar3'

radar1 = pyart.io.read(radar_file_path1)
radar2 = pyart.io.read(radar_file_path2)
radar3 = pyart.io.read(radar_file_path3)

radarcomb = pyart.util.join_radar(radar1, radar2)
radarcombfinal = pyart.util.join_radar(radarcomb, radar3)

output_dir = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/radar_plots'
os.makedirs(output_dir, exist_ok=True)

def plot_radar_sweeps(radar, radar_name, output_dir):
display = pyart.graph.RadarDisplay(radar)
num_sweeps = radar.nsweeps
for sweep in range(num_sweeps):
plt.figure(figsize=(10, 6))
display.plot('velocity',vmin=-20,vmax=60, sweep=sweep, title=f'{radar_name} Sweep {sweep + 1}', colorbar_label='m/s')
plt.savefig(os.path.join(output_dir, f'{radar_name}sweep{sweep + 1}.png'))
plt.close()

plot_radar_sweeps(radarcombfinal, 'Radar', output_dir)
print(f'All plots are saved in {output_dir}')

dealias_output_dir = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/radar_plots_dealias'
os.makedirs(dealias_output_dir, exist_ok=True)

dealiased_velocity = pyart.correct.dealias_region_based(radarcombfinal)

radarcombfinal.add_field('velocity', dealiased_velocity, replace_existing=True)

plot_radar_sweeps(radarcombfinal, 'Radar_Dealiased', dealias_output_dir)
print(f'All dealiased plots are saved in {dealias_output_dir}')

z_grid_limits = (3000,3150)
y_grid_limits = (-150000.,150000.)
x_grid_limits = (-150000.,150000.)

grid_resolution = 150

def compute_number_of_points(extent, resolution):
return int((extent[1] - extent[0])/resolution)

z_grid_points = compute_number_of_points(z_grid_limits, grid_resolution)
x_grid_points = compute_number_of_points(x_grid_limits, grid_resolution)
y_grid_points = compute_number_of_points(y_grid_limits, grid_resolution)

print(z_grid_points,
y_grid_points,
x_grid_points)

grid = pyart.map.grid_from_radars(radarcombfinal,
grid_shape=(z_grid_points,
y_grid_points,
x_grid_points),
grid_limits=(z_grid_limits,
y_grid_limits,
x_grid_limits))
#weighting_function='Barnes2',
#gridding_algo='map_to_grid')

display = pyart.graph.GridMapDisplay(grid)
display.plot_grid('velocity',
level=0,
vmin=-20,
vmax=60,
cmap='pyart_HomeyerRainbow')

gridxarray = grid.to_xarray()

xarray_output_dir = '/Users/cuihanzhe/Desktop/ai_downscaling/radardata/radar_plots_xarray'
os.makedirs(xarray_output_dir, exist_ok=True)

plt.figure(figsize=(10, 6))

gridxarray.isel(z=0).velocity.plot(cmap='pyart_HomeyerRainbow',
vmin=-20,
vmax=60);

plt.title(f'Wind Speed at 3 km')
plt.savefig(os.path.join(xarray_output_dir, f'wind_speed_3km.png'))
plt.close()

======================================================================================
However, I encountered a significant issue with my gridding results. In the resulting image after gridding, there are concentric circles at the center where the data is missing, showing as white concentric circles.

I have verified that my radar data does not have this issue because the radar images processed with dealias_region_based are perfectly normal without any concentric circles.

Could you please help me understand why this might be happening? I would greatly appreciate any assistance you can provide.

Thank you very much for your help!

[zssherman]

@hanzhe910 Hmm I would maybe try tweaking the ROI (but might not be ideal) of the gridding etc. But if I had to take a guess it might be something along the lines of this or the link could be helpful in pinpointing why that's the case:
https://groups.google.com/g/pyart-users/c/9G5T9Q9wrPA/m/VIVpIqL1AgAJ