This page is devoted to instruction in ncremap. It describes steps necessary to create grids, and to regrid datasets between different grids with ncremap. Some of the simpler regridding options supported by ncclimo are also described at Generate, Regrid, and Split Climatologies (climo files) with ncclimo. This page describes those features in more detail, and other, more boutique features often useful for custom regridding solutions.
The Zen of Regridding
Most modern climate/weather-related research requires a regridding step in its workflow. The plethora of geometric and spectral grids on which model and observational data are stored ensures that regridding is usually necessary to scientific insight, especially the focused and variable resolution studies that E3SM models conduct. Why does such a common procedure seem so complex? Because a mind-boggling number of options are required to support advanced regridding features that many users never need. To defer that complexity, this HOWTO begins with solutions to the prototypical regridding problem, without mentioning any other options. It demonstrates how to solve that problem simply, including the minimal software installation required. Once the basic regridding vocabulary has been introduced, we solve the prototype problem when one or more inputs are "missing", or need to be created. The HOWTO ends with descriptions of different regridding modes and workflows that use features customized to particular models, observational datasets, and formats. The overall organization, including TBD sections (suggest others, or vote for prioritizing, below), is:
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These sliding scale examples specify that valid values must cover at least 25% and 50% of the destination gridcell to meet the threshold for a non-missing destination value. With actual valid destination areas of 25% or 50%, this approach algorithm would produce destination values greater than the conservative algorithm by factors of four and two, respectively. Careful readers may already have observed that the mean-preserving approach (--preserve=mean) is exactly equivalent to the sliding scale approach with a renormalization threshold weight rnr_thr = 0.0. The latter is just a more numerical way of expressing the former. There is no numerical threshold weight equivalent to the integral-preserving approach. However, for convenience setting the threshold weight to the string "none" (--rnr_thr=none) is exactly equivalent to specifying the integral-preserving approach.
In practice, it may make sense to use the default conservative integral-preserving treatment with conservative regriddingweights, and the renormalized mean-preserving treatment with other (non-conservative) regridding algorithms weights such as those produced by bilinear interpolation or nearest-neighbor. Another consideration is whether the fields being regridded to regrid are fluxes or state variables. For example, temperature (unlike heat) and concentrations (amount per unit volume) are not physically conserved quantities under areal-regridding so it often makes sense to interpolate them in a non-conservative fashion, to preserve their fine-scale structure. Few researchers can digest the unphysical values of temperature that the conservative integral-preserving treatment produces in regions rife with missing values. On the other hand, mass and energy fluxes should be physically conserved under areal-regridding. One should consider both the type of field and its conservation properties when choosing a missing value treatment.
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