Lat/lon gridded data does not have monotonically increasing latitudes

[jbusecke]

First of all THANK YOU so much for this effort! Having ERA5 data available in an ARCO format is truly a game changer!

I noticed a small issue: The latitudes of the lat/lon gridded data 1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2/ seems to have decreasing latitude values

import xarray as xr

ar_full_37_1h = xr.open_zarr(
    'gs:https://gcp-public-data-arco-era5/ar/1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2/',
).isel(time=0)
ar_full_37_1h

which makes selecting a region in xarray slightly counterintuitive:

ar_full_37_1h.sel(latitude=slice(-50, 50))

returns no latitude indicies

while

ar_full_37_1h.sel(latitude=slice(50, -50))

gives (the desired)

If you end up reprocessing the data at some point, I wonder if something like xarrays ds.sortby('latitude') or equivalent could be added to the pipeline.

[tom-andersson]

@jbusecke this is standard for reanalysis data (such as ERA5), although I agree it is counterintuitive.

For example, try loading sample NCEP reanalysis data with xarray:

>>> import xarray as xr
>>> print(xr.tutorial.open_dataset("air_temperature"))
<xarray.Dataset>
Dimensions:  (lat: 25, time: 2920, lon: 53)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Data variables:
    air      (time, lat, lon) float32 ...
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http:https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

The latitude values are also in decreasing order.

[jbusecke]

Oh interesting, I did not know that. Thanks @tom-andersson.

I personally would make the argument that this is something that 'should' be changed to make the data more analysis ready, but I guess this is somewhat personal preference and it would be good if there is more general guidance on this that ARCO-producers could refer to.
In fact I wonder if this is something that would fall under a 'tidy array' concept (see this talk from scipy this year). @dcherian, where would be a good place to discuss this sort of stuff?

[dcherian]

I agree that this is not ideal but there are very many datasets like this ;) particularly in the raster imaging space.

See pydata/xarray#1613 for a discussion on a nicer API that ignores order of the coordinate variable.