issue_comments: 1102107532

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https://github.com/pydata/xarray/issues/6493#issuecomment-1102107532 https://api.github.com/repos/pydata/xarray/issues/6493 1102107532 IC_kwDOAMm_X85BsNOM 9312831 2022-04-19T05:43:05Z 2022-04-19T05:43:05Z NONE

Thanks to you guys here @Illviljan @TomNicholas @dcherian. I've been a user of xgcm for quite a time. So you can see my proposal just follows the style of xgcm.

I am working on my xinvert package, in which I may need some partial differential calculations. This can be done by xgcm quite well, but I am still worried about the metrics concept introduced by xgcm. I think this should be discussed over xgcm's repo.

For most of the cases, lat/lon-type grids are uniform and on the Arakawa A grid. So xarray's differentiate() is good enough with pad() (although it is experimental) for BCs, as suggested by @dcherian. We don't need stagged grid point and metrics, as in xgcm, but centered difference (a[i+1]-a[i-1]) will be good enough for A grid. This is simpler and do not make heavy dependence of the third-party package like xgcm.

I'll give a try with differentiate() and pad() to implement grad/div/vor... But some designs in xgcm also inspire me to make things much natural.

 
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