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Re: [ferret_users] masking out data but keep default values within

Thanks steve,

I got desired result with @NRST transformation.

(which required simply replacing the corresponding line by: let prm2=precip[d=1,gxy=nmask2@nrst]*nmask2)

However, doing same with @XACT transformation resoulted in "no valid data", probably because there is not a single match between source and destination grid coordinates. Moreover, it seems from the manual that even if it had worked that wouldn't give the desire result.

Anyway, I am glad one of your suggestions worked.

Best regards,

On Thursday, February 13, 2014 8:08 PM, Steve Hankin <steven.c.hankin@xxxxxxxx> wrote:
Hi Jagadish,

By default regridding is by linear interpolation, which results in "smoothing" (sort of).
Check out regridding using the "nearest" (g=@NRST) transformation:

The "exact" (@XACT) regridding transformation is also a possibility to suppress smoothing of any kind at all (leaving missing value voids instead).

    - Steve


On 2/13/2014 10:08 AM, jagadish karmacharya wrote:
Dear all,

I wonder if anyone had a chance to think about the problem below, which I posted sometime back.

Any advice would be greatly appriciated or is it not possible to carry out in ferret?

Thanks in advance,

On Friday, December 6, 2013 1:08 PM, jagadish karmacharya <j_karmacharya@xxxxxxxxx> wrote:
Dear all,

I want to mask a coarse resolution data (upper left plot) outside a region of interest. I can do that (upper right plot) but the probelm is: because of corase data resolution only portion of desired area is covered (and also some undesired area outside the boundary). I can solved the problem of coverage by first interpolating the coarse resolution data to a high resolution grid (lower left plot) but the problem is: data is now smoothened.

What I want is: to keep the original magnitude at each location but also be able to mask out data outside region of interest. Someone suggested this could be done by resampling data but I have no idea how to do that. I would appreciate any suggestions.

Below is my script for generating those plots

sh d
    currently SET data sets:
    1> GPCPv2.2.precip.JJAS.mean.nc
name    title                            I        J        K        L
PRECIP  Average Monthly Rate of Precipi  1:144    1:72      ...      1:19
    2> NPL_adm0_box.nc  (default)
name    title                            I        J        K        L
MASK    IF PT_IN_POLY(TEMP[L=1,D=3],LON  1:455    1:300    1:1      1:1

! originial data
set vi ul
shade/nolab precip[d=1,l=1,x=79:89,y=25:31];go land 1,,1

! mask interpolated to the data grid of 2.5 deg resolution
set vi ur
def grid/x=mask[d=2,gxy=precip[d=1]]/y=mask[d=2,gxy=precip[d=1]] gxy1
let dummy1=0*x[gx=gxy1]*y[gy=gxy1]
let N_NoTime1=reshape(mask[d=2,l=1,gxy=gxy1],dummy1); let Nmask1= if N_NoTime1 then 1
let prm1=precip[d=1]*nmask1
shade/nolab/lev prm1[l=1,x=79:89,y=25:31];go land 1,,1

! mask and data interpolated to a fine grid of 0.1 deg resolution
set vi ll
def axis/x=78E:91E:0.1 xax;  def axis/y=25N:31N:0.1 yax; def grid/x=xax/y=yax gxy2
let dummy2=0*x[gx=gxy2]*y[gy=gxy2]
let N_NoTime2=reshape(mask[d=2,l=1,gxy=gxy2],dummy2); let Nmask2= if N_NoTime2 then 1
let prm2=precip[d=1,gxy=nmask2]*nmask2
shade/nolab/lev prm2[l=1,x=79:89,y=25:31];go land 1,,1

Best regards,


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