Re: Regridding in space and time

[Jaison Kurian <jaison@xxxxxxxxxxxxxxxxxx>]

Hi Chris,
           You reached there almost...but missed few points. I modified 
your script (see the attachment:scat2grid.jnl, using the data 
sample_data.txt)  to get proper time dimension for the variable based on 
following details...

     1. Your data is repeating for the common X-Y locations for 5 time
          points ( 1990- 3/31, 4/30, 5/21, 6/14 &  6/21). But the lat-lon
          point "80.50  -67.75" exists only for "1990 03 31". If this  
          point is skipped we are left with 107 spacial points and 5 time
          points ==> 107*5 = 535 values (or lines in the data file). 
          Now read the variable in Ferret like...

   define axis/x=1:107:1 xscat        ! spatial points  
   define axis/t=1:5:1   tscat        ! time points
   define grid/x=xscat/t=tscat gscat           
   file/grid=gscat/var=..............

    2. to define a time axis choose only the five values from year, month 
          & day (skip all the repeated values). Get the time values using
          days1900 function, define a time axis..assign to the variable 
          using @ASN regridding.

   3.  Use this new variable in scat2grid function. Now the variable is 
         2D but one of the dimention is time. So you have to give only
         the common lat and lon points as the arguments to the scat2grid.
 
       Define the final destination grid such that it is within the limits
         of the original data. Dont forget to use the /UNITS attribute
         while defining latitude or longitude axis

   4.  Define a climatological monthly time  axis then regrid to this
        usin @MODULO regridding to get the climatology.

 Please go through the modified script..if anything need to be 
explained..let me know.

 As a "bonus" you can see how to use "POLYGON" command to look at the 
original data in a spatial plot without any interpolation/extrapolation.


 Hope this helps

 With Regards

 Jaison 


On Thu, 18 Nov 2004, Chis Konig wrote:

> Hi there,
> 
> I have data that came in 2D-fields at irregular times, i.e. it is 
> unevenly spaced in time (weekly - monthly) and space (i.e. 2D but was 
> not on a lon-lat-grid). I'd like to make a climatology with monthly 
> means etc. from it.
> 
> Unless there is an elegant way of doing that all at once (3D 
> regridding?) I think I have to achieve this in two steps (but correct me 
> if I'm wrong):
> 
> - First regrid it in space onto a lon-lat-grid for each time step.
> - Secondly regrid it over time, i.e. make monthly averages etc.
> 
> I might prefer a two-stepped approach anyway as it gives me more 
> control, e.g. different types of interpolation for space and time.
> 
> The data is in ASCII and in the form:
> 
> yr mo dy lat lon var
> 
> An example file can be found here: 
> http://math.nyu.edu/~konigc/sample_data/sample_data.txt
> 
> The first three variables (time) are non-decreasing but identical for 
> several hundreds to thousands data points (all the data from one field).
> 
> That's how far I got:
> 
> !!!!!!!!!!!!!!!!!!!!!!!!
> 
> define axis/x=1:1000:1 x_temp
> define grid/x=x_temp g_temp
> file/var="yr,mo,dy,lat,lon,var"/grid=g_temp sample_data.txt
> 
> let gregday = days1900(yr,mo,dy)
> 
> define axis/x=-75:-55:0.25 xax
> define axis/y=79:83:0.25 yax
> !Those axes would be different for the total data.
> 
> let var_2=scat2gridgauss_XY(lon,lat,var,x[gx=xax],y[gy=yax],0.5,0.5,2,2)
> !OR
> let var_2=scat2gridlaplace_XY(lon,lat,var,x[gx=xax],y[gy=yax],5,2)
> 
> shade var_2
> go land
> 
> !!!!!!!!!!!!!!!!!!!!!!!!
> 
> My problems with that approach:
> 
> - I lose the time dimension in the regridding process.
> - The data is confined to the ocean. But with scat2gridXXX the 
> information 'spills' onto the land. Is there a possibility to use a 
> simpler interpolation (nearest neighbour, linear...)?
> 
> Thanks for any help!
> 
> Chris
> 
> 
> 
> 

-- 
___________________________________________________

    Jaison Kurian                           
    Centre for Atmospheric and Oceanic Sciences
    Indian Institute of Science
    B A N G A L O R E   560 012
    Ph: +91-80-3942505
        +91-80-3600450
    Fax:+91-80-3600865
___________________________________________________
!  
! delete the line containing data for 1990 03 31 80.50  -67.75  
!   to bring all days to a common scattered points
!
      define axis/x=1:107:1 xscat   ! data points  
      define axis/t=1:5:1   tscat   ! time points

      define grid/x=xscat/t=tscat gscat
 
      file/var="yr,mo,dy,lat,lon,var"/grid=gscat sample_data.txt

      ! let us have a cross check  
      list/y=5 var
 
      !  get all the 5 dates  

      let yr_5  =  yr[i=1] ! values need only for I=1,L=1:5     
      let mo_5  =  mo[i=1] !     "
      let dy_5  =  dy[i=1] !     "
  
      list yr_5, mo_5, dy_5

      let gregday = days1900(yr_5,mo_5,dy_5)
      
      define axis/t/t0="01-JAN-1900:00"/units=days/from_data t5day=gregday
      list t[gt=t5day]

      ! get the variable onto the time grid instead of Y

      let var_t    = var[GT=t5day@ASN]         

      plot/l=5 var_t

message

! now do the scat2grid
!      choose min & max for defining the axes so that those
!      values are within the actual range

      let lat_1D = lat[l=1]  ! we need only the 107 common values 
      let lon_1D = lon[l=1]  ! we need only the 107 common values

      define axis/x=-61.5:-67.5:0.25/units=longitudes xlon  
      define axis/y=80.5:81.75:0.25/units=latitudes ylat

      let var_2=scat2gridgauss_XY(lon_1D,lat_1D,var_t,x[gx=xlon],y[gy=ylat],0.125,0.125,2,2)

      fill/l=1 var_2
      plot /ov/vs/color=7 lon[l=1],lat[l=1]
      go land

!  no you can define a monthly climatological axis (if needed..because now
!     you have one value per month)..and make the climatology using var_t

!      let us see what "polygon" can do

 message

      let xpts = XSEQUENCE(lon[l=1,i=1:107])  
      let ypts = XSEQUENCE(lat[l=1,i=1:107])

      let dxp=0.2 ; let bx=ysequence({`(-1)*dxp`,`dxp`,`dxp`,`(-1)*dxp`})
      let dzp=0.05 ; let bz=ysequence({`dzp`,`dzp`,`(-1)*dzp`,`(-1)*dzp`})

      polygon bx+xpts,bz+ypts,var_t[l=1]
      plot /ov/vs lon[l=1],lat[l=1]
      go land