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pythongeospatialnetcdfpython-xarray

Merging xarray datasets with same extent but different spatial resolution

I have one dataset of satellite based solar induced fluorescence (SIF) and one of modeled precipitation. I want to compare precipitation to SIF on a per pixel basis in my study area. My two datasets are of the same area but at slightly different spatial resolutions. I can successfully plot these values across time and compare against each other when I take the mean for the whole area, but I'm struggling to create a scatter plot of this on a per pixel basis.

Honestly I'm not sure if this is the best way to compare these two values when looking for the impact of precip on SIF so I'm open to ideas of different approaches. As for merging the data currently I'm using xr.combine_by_coords but it is giving me an error I have described below. I could also do this by converting the netcdfs into geotiffs and then using rasterio to warp them, but that seems like an inefficient way to do this comparison. Here is what I have thus far:

import netCDF4
import numpy as np
import dask
import xarray as xr

rainy_bbox = np.array([
    [-69.29519955115512,-13.861261028444734],
    [-69.29519955115512,-12.384786628185896],
    [-71.19583431678012,-12.384786628185896],
    [-71.19583431678012,-13.861261028444734]])
max_lon_lat = np.max(rainy_bbox, axis=0)
min_lon_lat = np.min(rainy_bbox, axis=0)

# this dataset is available here: ftp://fluo.gps.caltech.edu/data/tropomi/gridded/
sif = xr.open_dataset('../data/TROPO_SIF_03-2018.nc')

# the dataset is global so subset to my study area in the Amazon
rainy_sif_xds = sif.sel(lon=slice(min_lon_lat[0], max_lon_lat[0]), lat=slice(min_lon_lat[1], max_lon_lat[1]))  

# this data can all be downloaded from NASA Goddard here either manually or with wget but you'll need an account on https://disc.gsfc.nasa.gov/: https://pastebin.com/viZckVdn
imerg_xds = xr.open_mfdataset('../data/3B-DAY.MS.MRG.3IMERG.201803*.nc4')

# spatial subset
rainy_imerg_xds = imerg_xds.sel(lon=slice(min_lon_lat[0], max_lon_lat[0]), lat=slice(min_lon_lat[1], max_lon_lat[1]))  

# I'm not sure the best way to combine these datasets but am trying this
combo_xds = xr.combine_by_coords([rainy_imerg_xds, rainy_xds])

Currently I'm getting a seemingly unhelpful RecursionError: maximum recursion depth exceeded in comparison on that final line. When I add the argument join='left' then the data from the rainy_imerg_xds dataset is in combo_xds and when I do join='right' the rainy_xds data is present, and if I do join='inner' no data is present. I assumed there was some internal interpolation with this function but it appears not.

Solution

  • This documentation from xarray outlines quite simply the solution to this problem. xarray allows you to interpolate in multiple dimensions and specify another Dataset's x and y dimensions as the output dimensions. So in this case it is done with

    # interpolation based on http://xarray.pydata.org/en/stable/interpolation.html
# interpolation can't be done across the chunked dimension so we have to load it all into memory
rainy_sif_xds.load()

#interpolate into the higher resolution grid from IMERG
interp_rainy_sif_xds = rainy_sif_xds.interp(lat=rainy_imerg_xds["lat"], lon=rainy_imerg_xds["lon"])

# visualize the output
rainy_sif_xds.dcSIF.mean(dim='time').hvplot.quadmesh('lon', 'lat', cmap='jet', geo=True, rasterize=True, dynamic=False, width=450).relabel('Initial') +\
interp_rainy_sif_xds.dcSIF.mean(dim='time').hvplot.quadmesh('lon', 'lat', cmap='jet', geo=True, rasterize=True, dynamic=False, width=450).relabel('Interpolated')

    enter image description here

    # now that our coordinates match, in order to actually merge we need to convert the default CFTimeIndex to datetime to merge dataset with SIF data because the IMERG rainfall dataset was CFTime and the SIF was datetime
rainy_imerg_xds['time'] = rainy_imerg_xds.indexes['time'].to_datetimeindex()

# now the merge can easily be done with
merged_xds = xr.combine_by_coords([rainy_imerg_xds, interp_rainy_sif_xds], coords=['lat', 'lon', 'time'], join="inner")

# now visualize the two datasets together // multiply SIF by 30 because values are so ow
merged_xds.HQprecipitation.rolling(time=7, center=True).sum().mean(dim=('lat', 'lon')).hvplot().relabel('Precip') * \
(merged_xds.dcSIF.mean(dim=('lat', 'lon'))*30).hvplot().relabel('SIF')

    enter image description here