Hi Madeleine,
The short answer is I don't know. I suspect that you will first have to
determine the dominant category (the one with the highest percentage cov-
erage) in your source grid, and then use cdo remaplaf to interpolate to
your destination grid. Step 1 should be easy using NCL or similar tools,
since all you need to is determine which vegetation type has the high-
est percentage coverage. In the case of two or more categories having
equal coverage within a given grid cell, you'll have to determine what
makes the most sense for your data, perhaps by looking to nearby grid
cells to help make that choice.
I hope this helps.
Best regards,
Daran
-- -----Original Message----- From: Madeleine Patterson [mailto:madeleine.patterson77@gmail.com] Sent: Mon 7/9/2012 1:40 AM To: Daran Rife Cc: ncl-talk@ucar.edu Subject: Re: Regridding from low to high resolution ? Hi Daran, Do you know whether this CDO remaplaf function can deal with 3D data? i.e. percent vegetation type data for 10 veg types? (for every lat lon gridpoint) Thanks MP be a bit slow. As an alternative, Climate Data Operators has a built-in function called remaplaf for regridding categorical data. Provided your input data are in one of the supported data formats (netCDF, HDF, GRIB 1/2), CDO might be a good option. https://code.zmaw.de/embedded/cdo/1.5.4/cdo.html#x1-4880002.12.1 https://code.zmaw.de/boards/1/topics/925
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Received on Mon Jul 9 10:20:12 2012
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