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grid_deltas_from_dataarray() returns all nans

See original GitHub issue

What went wrong?

When attempting to use absolute_vorticity(), I encountered some trouble getting any values at all.

This line: https://github.com/Unidata/MetPy/blob/7af0d4949a6ec8ad02e233f4f4a211744b5c6567/src/metpy/xarray.py#L1420-L1421

Causes dx/dy values to be all NaN because lat_lon_grid_deltas() does not appear to properly handle Xarray DataArray’s.

I’m not sure whether the fix is to modify xarray.py call to lat_lon_grid_deltas() to be something like latitude.data and longitude.data, or change how lat_lon_grid_deltas() works so that if given a DataArray it can get the underlying data out.

Happy to make a contribution with a little guidance. Thanks.

P.S.- still having trouble getting appropriate-looking absolute vorticity values but still working on that.

Operating System

Linux

Version

1.3.1

Python Version

3.9.15

Code to Reproduce

# If I use some sample ERA5 latitude/longitude grid data with this code:
dx, dy = mpcalc.lat_lon_grid_deltas(ds['longitude'],ds['latitude'])
print(dx)
print(dy)

# [[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]] meter
# [[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]] meter

dx, dy = mpcalc.lat_lon_grid_deltas(ds['longitude'].data,ds['latitude'].data)
print(dx)
print(dy)
# [[13899.351016076906 13899.351016076906 13899.351016076906 ...  13899.351016076906 13899.351016076906 # 13899.351016076906] [14004.262721525833 14004.262721525833 14004.262721525833 ...  14004.262721525833 # 14004.262721525833 14004.262721525833] [14108.90780683885 14108.90780683885 14108.90780683885 ...  # 14108.90780683885 14108.90780683885 14108.90780683885] ... [26787.690257756163 26787.690257756163 # 26787.690257756163 ...  26787.690257756163 26787.690257756163 26787.690257756163] [26819.849824192395 # 26819.849824192395 26819.849824192395 ...  26819.849824192395 26819.849824192395 26819.849824192395]# [26851.498778762038 26851.498778762038 26851.498778762038 ...  26851.498778762038 26851.498778762038 # 26851.498778762038]] meter
# [[-27798.718571171168 -27798.718571171168 -27798.718571171168 ...  -27798.718571171168 -27798.718571171168 # -27798.718571171168] [-27798.7185711694 -27798.7185711694 -27798.7185711694 ...  -27798.7185711694 # -27798.7185711694 -27798.7185711694] [-27798.71857117152 -27798.71857117152 -27798.71857117152 ...  # -27798.71857117152 -27798.71857117152 -27798.71857117152] ... [-27798.718571170106 -27798.718571170106 # -27798.718571170106 ...  -27798.718571170106 -27798.718571170106 -27798.718571170106] [-27798.71857117046 # -27798.71857117046 -27798.71857117046 ...  -27798.71857117046 -27798.71857117046 -27798.71857117046] # [-27798.71857117046 -27798.71857117046 -27798.71857117046 ...  -27798.71857117046 -27798.71857117046 # -27798.71857117046]] meter

# The first dx/dy are all NaN but the second dx/dy seem reasonable.