**Next message:**Louis Wicker: "Fwd: [ncl-talk] Is there a way to do this without for loops? Solution!"**Previous message:**Dennis Shea: "Re: xy2 plot"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

All:

I am looking for some hints on how to solve the following problem

without using 2 for loops in NCL.

The problem is I am trying to interpolate 3D gridded data fld to a

radar conical surface. I simply want the xy grid to remain the same,

but

to interpolate to that 2D plane given by some elevation angle and

distance from the radar.

So I have a scalar 3D field like reflectivity

dbz(z,y,x)

which has xg, yg, and zg grid dimensions (each are 1D).

and I have a 2D field of heights created from knowing where each xy

grid pt is and computing the distance from the radar so that

zhgt(y,x) = distance_from_radar * tan(elevation_angle).

So at each x,y point, I need to search for the correct height point

near zhgt(y,x) and then do a simple linear interpolation.

The dumb way would be (forgive the bad syntax)

for j = 0,ny-1

for i = 0,nx-1

k = (search for vertical grid pt right below zhgt(j,i))

uzinterp = coeff * dbz(k,j,i) + (1-coefff)*dbz(k+1,j,i)

end for

end for

But I will bet for a 200x200 (or larger grid) this will be slow!

I am pretty sure that I can easily create a 2D array of the vertical

index "k"

k_2d = ind(zg .le. zhgt) ????

but how can I select the 2D set of points in the dbz(z,y,x) that are

given by k_2d? is there some fancy notation, or do I have to convert

the

dbz array to 1D, select the points out in some rather nasty fashion,

and then convert back to 2D?

Any help would be appreciated, as I am trying to get a nice figure

done for the meso/radar conference next week..

Cheers!

Lou Wicker

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**Next message:**Louis Wicker: "Fwd: [ncl-talk] Is there a way to do this without for loops? Solution!"**Previous message:**Dennis Shea: "Re: xy2 plot"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

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