claire claire - 5 months ago 24
Python Question

understanding pyresample to regrid irregular grid data to a regular grid

I need to regrid data on a irregular grid (lambert conical) to a regular grid. I think pyresample is my best bet. Infact my original lat,lon are not 1D (which seems to be needed to use basemap.interp or scipy.interpolate.griddata).

I found this SO's answer helpful. However I get empty interpolated data. I think it has to do with the choice of my radius of influence and with the fact that my data are wrapped (??).

This is my code:

import numpy as np
from matplotlib import pyplot as plt
import netCDF4
%matplotlib inline
url = ""
SRHtemp = netCDF4.Dataset(url).variables['hlcy'][0,::]
Y_n = netCDF4.Dataset(url).variables['y'][:]
X_n = netCDF4.Dataset(url).variables['x'][:]
T_n = netCDF4.Dataset(url).variables['time'][:]

lat_n = netCDF4.Dataset(url).variables['lat'][:]
lon_n = netCDF4.Dataset(url).variables['lon'][:]

lat_n and lon_n are irregular and the latitude and longitude corresponding to the projected coordinates x,y.

Because of the way lon_n is, I added:

lon_n[lon_n<0] = lon_n[lon_n<0]+360

so that now if I plot them they look nice and ok:

enter image description here

Then I create my new set of regular coordinates:

XI = np.arange(148,360)
YI = np.arange(0,87)
XI, YI = np.meshgrid(XI,YI)

Following the answer above I wrote the following code:

from pyresample.geometry import SwathDefinition
from pyresample.kd_tree import resample_nearest

def_a = SwathDefinition(lons=XI, lats=YI)
def_b = SwathDefinition(lons=lon_n, lats=lat_n)
interp_dat = resample_nearest(def_b,SRHtemp,def_a,radius_of_influence = 70000,fill_value = -9.96921e+36)

the resolution of the data is about 30km, so I put 70km, the fill_value I put is the one from the data, but of course I can just put zero or nan.

however I get an empty array.

What do I do wrong? also - if there is another way of doing it, I am interested in knowing it. Pyresample documentation is a bit thin, and I need a bit more help.

I did find this answer suggesting to use another griddata function:

import matplotlib.mlab as ml
resampled_data = ml.griddata(lon_n.ravel(), lat_n.ravel(),SRHtemp.ravel(),XI,YI,interp = "linear")

and it seems to be ok:

enter image description here

But I would like to understand more about pyresample, since it seems so powerful.


The problem is that XI and XI are integers, not floats. You can fix this by simply doing

XI = np.arange(148,360.)
YI = np.arange(0,87.)
XI, YI = np.meshgrid(XI,YI)

The inability to handle integer datatypes is an undocumented, unintuitive, and possibly buggy behavior from pyresample.

A few more notes on your coding style:

  • It's not necessary to overwrite the XI and YI variables, you don't gain much by this
  • You should just load the netCDF dataset once and the access the variables via that object