[ncl-talk] superobbing

[Andrew Kren - NOAA Affiliate]

Thanks Dennis!

On Wed, Jun 27, 2018 at 10:18 AM, Dennis Shea <shea at ucar.edu> wrote:

> Observations: T(lev,time) and Q(lev,time), LAT(time), LON(time)
>
> Is the target grid rectilinear?
>
> If, say, the grid box is bounded by lat_low, lat_high, lon_low, lon_high
>
>   itime = *ind*(LAT.ge.lat_low .and. LAT.lt.lat_high .and. \
>                     LON.ge.lon_low .and. LON.lt.lon_high)
>
>   T_super = *dim_avg_n_Wrap*(T(:,itime), 1)
>   T_super at long_name = "Super Ob: " + T at long_name   ; cancatenate
>   printVarSummary(T_super)
>
> Same for Q
>
> ===
> *http://www.ncl.ucar.edu/Document/Functions/Built-in/ind.shtml
> <http://www.ncl.ucar.edu/Document/Functions/Built-in/ind.shtml>*
> ===
> For grids the lat_low, lat_high, lon_low, lon_high would have to be
> indexed.
>
> HTH
>
> On Tue, Jun 26, 2018 at 12:44 PM, Andrew Kren - NOAA Affiliate <
> andrew.kren at noaa.gov> wrote:
>
>> Dear ncl-talk,
>>
>> I am thinning some data prior to assimilating it in the HWRF model. The
>> data is temperature and moisture vertical profiles that is one-dimensional,
>> a function of time. The lat/lon points of the data is also a function of
>> time, so there is a spatial element to it. Basically, I want to superob the
>> data to create a mean observation within some grid box size to thin it. I
>> would need to regrid the data and search how many profiles within the
>> specified grid size. My question is what would be the best way to achieve
>> this via NCL?
>>
>> Thanks,
>>
>> --
>> Andrew Kren
>> Assistant Scientist
>> CIMAS - NOAA/AOML
>> 314-322-0867
>>
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>>
>


-- 
Andrew Kren
Assistant Scientist
CIMAS - NOAA/AOML
314-322-0867
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