Regridding daily temperatures: Sanity check

Hi all,

I need to regrid daily temperature files to a courser resolution. Would bilinear interpolation be the appropriate method to use? I typically work with rainfall data, so a sanity check as to the best method to use to regrid temperature would be greatly appreciated. :slight_smile:


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I’m curious, what interpolation method do you typically use for rainfall?

As temperature is intensive I’d say any method that does smoothing, rather than conservation of “flux”, should be fine, but if you’re interested in derivatives patch recovery interpolation may be better.

These descriptions from an old version of the the ESMF documentation seem quite useful to me:

  • bilinear - the algorithm used by this application to generate the bilinear weights is the standard one found in many textbooks. Each destination point is mapped to a location in the source mesh, the position of the destination point relative to the source points surrounding it is used to calculate the interpolation weights.
  • patch - this method is the ESMF version of a technique called “patch recovery” commonly used in finite element modeling. It typically results in better approximations to values and derivatives when compared to bilinear interpolation.
  • conserve - this method will typically have a larger interpolation error than the previous two methods, but will do a much better job of preserving the value of the integral of data between the source and destination grid.
  • neareststod and nearestdtos The nearest neighbor methods work by associating a point in one set with the closest point in another set. If two points are equally close then the point with the smallest index is arbitrarily used (i.e. the point with that would have the smallest index in the weight matrix). There are two versions of this type of interpolation available in the regrid weight generation application. One of these is the nearest source to destination method (“neareststod”). In this method each destination point is mapped to the closest source point. The other of these is the nearest destination to source method (“nearestdtos”). In this method each source point is mapped to the closest destination point. Note, that with this method the unmapped destination point detection doesn’t work, so no error will be returned even if there destination points which don’t map to any source point.
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P.S. That Regridding Overview | Climate Data Guide is a really nice overview too. I like the point that temporal scales matter a lot too: regridding at one temporal scale may make sense, but at another with time averaged data not so much.

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Thank you so much, @Aidan !

I normally use first-order conservative interpolation for rainfall, but didn’t think it was appropriate for temperature, as you also pointed out.

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