Regridding in space and time

["Chis Konig" <konigc@xxxxxxxxxxxx>]

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



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