Re: [ferret_users] interpolation to make an uniform NaN in data for all time steps

To: ferret_users@xxxxxxxx

Subject: Re: [ferret_users] interpolation to make an uniform NaN in data for all time steps

From: "Ansley C. Manke" <ansley.b.manke@xxxxxxxx>

Date: Thu, 6 Sep 2018 09:20:41 -0700

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also, try

cancel axis/modulo `rain,return=xaxis` so that the filling does not smear off the edges of the region.

On 9/4/2018 6:40 PM, saurabh rathore wrote:

Dear Ferreters,

I am facing a problem that in my data set I dont have uniform NaN locations i.e. location of NaN is changing with time so as the Number of Good Points. So I thought to use interpolation to make uniform NaN location at all time steps. such that Number of Good Points will be same at all time steps.

let rain1=rain[x=@fln:5,y=@fln:5]
list rain1[x=@ngd,y=@ngd], rain[x=@ngd,y=@ngd]
             DATA SET: ./rain_land.nc
             TIME: 16-JAN-1961 00:00 to 16-DEC-2017 00:00 360_DAY
             LONGITUDE: 112E to 154E (XY # valid)
             LATITUDE: 44S to 10S (XY # valid)
Column 1: RAIN1 is RAIN[X=@FLN:5,Y=@FLN:5]
Column 2: RAIN is RAIN2[D=1]*MASK[GXY=ROSEXY]
                     RAIN1    RAIN
01-FEB-1961 /   1: 372084. 251863.
16-APR-1961 /   2: 372084. 252028.
16-JUL-1961 /   3: 372200. 251145.
16-OCT-1961 /   4: 372200. 252175.
16-JAN-1962 /   5: 372200. 252582.
16-APR-1962 /   6: 372200. 252074.
16-JUL-1962 /   7: 372200. 252014.
16-OCT-1962 /   8: 372200. 251934.
16-JAN-1963 /   9: 372200. 252245.
16-APR-1963 / 10: 372200. 252299.
16-JUL-1963 / 11: 372200. 253485.
16-OCT-1963 / 12: 372200. 252965.
16-JAN-1964 / 13: 372200. 253166.
16-APR-1964 / 14: 372200. 252025.
16-JUL-1964 / 15: 372200. 251561.
16-OCT-1964 / 16: 372200. 251443.
16-JAN-1965 / 17: 372200. 252010.
16-APR-1965 / 18: 372200. 252017.
16-JUL-1965 / 19: 372200. 252336.
16-OCT-1965 / 20: 372200. 254165.

16-JAN-1966 / 21: 372200. 254125.
16-APR-1966 / 22: 372200. 254299.
16-JUL-1966 / 23: 372200. 254406.
16-OCT-1966 / 24: 372200. 256191.
16-JAN-1967 / 25: 372200. 256082.
16-APR-1967 / 26: 372200. 256142.
16-JUL-1967 / 27: 372200. 256252.
16-OCT-1967 / 28: 372200. 256194.
16-JAN-1968 / 29: 372200. 256746.
.......

......

.......

16-JUL-2011 / 203: 372200. 250125.
16-OCT-2011 / 204: 372200. 253454.
16-JAN-2012 / 205: 372200. 259980.
16-APR-2012 / 206: 372200. 258723.
16-JUL-2012 / 207: 372056. 244920.
16-OCT-2012 / 208: 372108. 249657.
16-JAN-2013 / 209: 372197. 250057.
16-APR-2013 / 210: 372192. 252042.
16-JUL-2013 / 211: 372109. 248603.
16-OCT-2013 / 212: 372114. 250663.
16-JAN-2014 / 213: 372181. 253777.
16-APR-2014 / 214: 372146. 253824.
16-JUL-2014 / 215: 372151. 250433.
16-OCT-2014 / 216: 372093. 253731.
16-JAN-2015 / 217: 372169. 252591.
16-APR-2015 / 218: 372140. 250404.
16-JUL-2015 / 219: 372107. 250558.
16-OCT-2015 / 220: 372100. 251296.
16-JAN-2016 / 221: 372141. 252942.
16-APR-2016 / 222: 372152. 251985.
16-JUL-2016 / 223: 372151. 253558.
16-OCT-2016 / 224: 372152. 254161.
16-JAN-2017 / 225: 372153. 254016.
16-APR-2017 / 226: 372120. 250580.
16-JUL-2017 / 227: 372121. 249324.
16-OCT-2017 / 228: 372149. 253319.
16-DEC-2017 / 229: 372153. 252808.

SO rain is original variable and rain1 is interpolated variable

I am puzzled in this please help.

regards, saurabh

--

REGARDS

Saurabh Rathore

Research Scholar (PhD.)

Centre For Oceans, Rivers, Atmosphere & Land Science Technology

Indian Institute Of Technology, Kharagpur

contact :- 91- 8345984434