era5POS preprocessor

[tommylees112]

the era5POS data when converted to monthly doesn't necessarily make sense to just upsample from hourly to monthly

    @staticmethod
    def resample_time(ds: xr.Dataset,
                      resample_length: str = 'M',
                      upsampling: bool = False) -> xr.Dataset:

        # TODO: would be nice to programmatically get upsampling / not
        ds = ds.sortby('time')

        resampler = ds.resample(time=resample_length)

        if not upsampling:
            return resampler.mean()
        else:
            return resampler.nearest()

Because it's then Mean HOURLY precipitation over a month - when really we should be summing over the month.

...
        if not upsampling:
            return resampler.mean()
        elif data == 'era5POS':
             return resampler.sum()
        else:
            return resampler.nearest()

Otherwise we get extremely different spatial patterns:

CHIRPS (mm/month)

ERA5POS (mm/hour mean for each month)

[gabrieltseng]

Isn't this okay if we normalize (which we do)?

[tommylees112]

hmmm yeah you would think so actually. but the spatial patterns would remain the same and they're so vastly different i'm just a little concerned that we haven't done the preprocessing correctly!

[gabrieltseng]

Are they so different? They have extremely different resolutions (this is only resolved at the engineering step), but the peaks in precipitation look like they are in roughly the same spot, as well as the troughs.