- There is a relatively direct relevancy of the EWA algorithm to the reprojection of geographically gridded data.
- The application of EWA in this case appears valid from a reprojection perspective.
- For geographically gridded data we have an array of data, with correlated latitude and longitude values. It is easy to see how the algorithm would be applied.
- When you look at a geographically gridded data array versus a swath data array, you can see that the geographically gridded data is generally wider than the swath, but there is no elliptical stretching of the grid cells in their geographic spacing. The ellipse of influence for the source data is circular.
- There remains, however, the non-circular ellipses of influence due to the reprojection to a non-geographic, projected grid. This is properly handled by the EWA implementation.
- EWA should be a relatively efficient implementation relative to reverse projection techniques.
- The source-cell to target-cells mapping does not have the elliptical spread of the sample-spots to consider - only the elliptical spread of the projection mapping. There should be no more, and likely fewer cells per source cell, and thus greater efficiency per source cell count than for a swath dataset.
- The algorithm has a gaussian filter built-in to mitigate down-sampling aliasing effects, where relevant in the output projected grid.
- In this case, the preprocessing step of EWA - the Latitude-Longitude-to-Row-Column calculation (ll2rc) can be handled in one of two ways
- The latitude/longitude arrays of values can be precomputed (if not directly available) and then fed into the ll2rc method
- Alternatively, the ll2rc method can be modified into a method that directly uses the 1D dimension-scales (dimension-variables) providing latitude and longitude values. I would call such a method llDim2rc.
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