I'm trying to analyze rainfall data from https://imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html, specifically the 'Gridded Rainfall (0.25 x 0.25) NetCDF' Files.
I used netCDF4 to load the data but I get this peculiar result:
from netCDF4 import Dataset
import numpy as np
path = '2013.nc'
f = Dataset(path ,'r')
rain = np.array(f.variables['RAINFALL'][:,:,:])
print(rain[0][0])
output:
[-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999.]
NOTE: There isn't a problem with this specific file, as I get the same result with the data for every year. You can try it out yourself.
Am I loading the data in python wrong is there something up with the data that I downloaded?
The latitude and longitude variables don't give me this problem though
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
# file import
path = Path('../../Downloads/')
file = 'Clim_Pred_LRF_New_RF25_IMD0p252013.nc'
f = Dataset(path / file, 'r')
# select variable "Rainfall"
rain = np.array(f.variables['RAINFALL'][:,:,:])
# assign NaN value
rain[rain==-999.] = np.nan
# plot dataset for selected date (2013-01-18)
plt.pcolormesh(rain[17])
plt.colorbar()
The data looks fine.
But check out the shape of rain:
print(rain.shape)
(365, 129, 135)
# --> (days, latitude, longitude).
With rain[0][0] you're selecting the first time step (Jan-01) and the first latitude (6.5), which will return a 1D array with NaN values only because all pixels are outside of India. Selecting the second and third dimension with 0 will always return arrays with -999 exclusively. You can try for example rain[17][100]!
print(rain[17][100]
array([ nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, 1.2962954 , 0.99620295, 2.705826 ,
9.772656 , 11.187045 , 0. , 3.0871496 , 5.298688 ,
18.108587 , 34.280876 , 35.034836 , 41.41929 , 38.952507 ,
63.49905 , 92.398125 , 92.92495 , 80.57031 , 63.660515 ,
45.413662 , nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan],
dtype=float32)