Re: ncl-talk Digest, Vol 86, Issue 41

From: Chad Herman <chad.herman.us_at_nyahnyahspammersnyahnyah>
Date: Wed Jan 26 2011 - 19:13:36 MST

Thanks for your suggestion Gus.

That would partially solve the extra ocean data issue, but this data set
contains some interpolation which causes land temperature data to extend
out to 250 km over the ocean. Masking out the ocean would remove this
data. Then there's still the issue of how to remove interpolated data
from deep land regions where there was no data to begin with. I've
decided to deal with the issue by writing my own interpolation scheme so
I can have finer control over the results.

-Chad

On 01/25/2011 06:32 AM, ncl-talk-request@ucar.edu wrote:
> Message: 5
> Date: Mon, 24 Jan 2011 18:06:34 -0500
> From: Gus Correa<gus@ldeo.columbia.edu>
> Subject: Re: Preventing extrapolation
> To: NCL-talk<ncl-talk@ucar.edu>
> Message-ID:<4D3E05FA.9000304@ldeo.columbia.edu>
> Content-Type: text/plain; charset=us-ascii; format=flowed
>
> Chad Herman wrote:
>> > Hi everyone,
>> >
>> > I've spent all of yesterday investigating this issue and haven't found
>> > any satisfactory solution. I have a file containing land surface
>> > temperature anomalies. The data is represented on an equal-area grid
>> > with 8,000 grid boxes, so I have three arrays: one for longitude,
>> > latitude and anomaly.
>> >
>> > My task is to interpolate it into 5x5 resolution. I can't use any
>> > interpolation functions that require the data present on a regular 2D
>> > grid. I've used CSSGRID and NATGRID and they interpolate fine, but they
>> > interpolate too much. It's completely infilling the oceans as well as
>> > large land surfaces which must be missing. I've looked into the two
>> > aforementioned interpolation routines and can't seem to find a way to
>> > completely stop them from interpolating into data absent regions.
>> >
>> > For NATGRID, I used nnsetp("ext", 0) and nnsetp("nul",
>> > anomaly@_FillValue) which at least stopped excessive interpolation in
>> > the Arctic. I tried to use bin_avg to find grid boxes where there is no
>> > data whatsoever and use the results to mask out the interpolated grid.
>> > While this solves the ocean data problem, it creates a new problem by
>> > masking out too many grid boxes on land. If there are a couple of grid
>> > boxes with non-missing data surrounding a grid box or two with no data,
>> > it seems quite reasonable to interpolate into the missing region. But if
>> > there is no data there, the interpolated values get masked.
>> >
>> > Any suggestions?
>> >
>> > Thanks,
>> > Chad
> Chad
>
> Suggestion: You could build your mask from a separate topography
> dataset, say, ETOPO or Smith and Sandwell, at the same resolution/grid
> of your interpolated data.
>
> http://iridl.ldeo.columbia.edu/SOURCES/.Sandwell/.seafloor/.height/
> http://iridl.ldeo.columbia.edu/SOURCES/.Sandwell/.seafloor/.height/figviewer.html?map.url=X+Y+fig-+colors+-fig
> http://www.ngdc.noaa.gov/mgg/global/global.html
>
> Would this help?
>
> Gus Correa
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Received on Wed Jan 26 19:14:05 2011

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