Re: Regridding from low to high resolution ?

From: Daran Rife <Daran.Rife_at_nyahnyahspammersnyahnyah>
Date: Mon Jul 09 2012 - 10:15:45 MDT

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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