reboxing, or 3D interpolation?
Package gstat provides 3D interpolation: library(gstat) demo(gstat3D) in this case, if you'd leave out the variogram model, inverse distance is used for interpolation. An typical issue is, independent whether the third dimension represents z (vertical space) or time, that variability, or spatial correlation, in x/y differs from that in z for the same distance unit (where "same" is only meaningful when z is vertical space). You can then rescale z, or use anisotropic variogram models.
On 11/01/2012 07:44 AM, Tom Roche wrote:
Having done "regridding" (aka 2D interpolation), I'd like to know how to do "reboxing" (aka 3D interpolation) in R. Can anyone point me to docs explaining how to do this, or example code that does this? What I mean, why I ask: I'm a student attempting to incorporate N2O emissions inventories (EIs), initial conditions (ICs), and boundary conditions (BCs) from relatively coarse-scaled global inventories (with horizontal grids on the order of degrees lon-lat) into a relatively fine-scaled regional atmospheric model (12-km LCC horizontal). The emissions are all from surface, hence effectively 2D, and I now know how to "regrid," or interpolate from one 2D grid to another. However the model and the N2O IC/BCs are 3D: the cells are boxes, not just grids. And of course the model (on the one hand) and the N2O IC/BCs have different vertical layering (i.e., numbers of layers, and top height, or maximum vertical extent), as well as different horizontal grids. I'd like to know: is there R code available that will "rebox," i.e., interpolate vertically as well as horizontally? thanks in advance, and feel free to forward, Tom Roche <Tom_Roche at pobox.com>
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