Introduction

The -box_ave option does a spatial smoothing by doing a simple box average of the data field. Amount of smoothing can be controlled by the size of the box. The -box_ave option can be used on regional and global fields. To identify global fields, you can use the option -cyclic.

Usage

-box_ave DX DY CRITICAL_WEIGHT
   DX=width of box (in grid points), DX has to be an odd positive integer
   DY=height of box (in grid points), DY has to be an odd positive integer

   The box average is the mean value for a box of DX x DY centered on
   the grid point.

   CRITICAL_WEIGHT
      -1: grid(i,j) = UNDEFINED    if original grid(i,j) is undefined
                    = box average  if original grid(i,j) is defined
       not -1: let wt = number of grid points that are defined in the box
            grid(i,j) = UNDEFINED     if wt <= WT
                      = box average   if wt > WT

The speed of -box_ave is O(NX*NY*DY). The O(NX*NY) method was slower
because of poor cache utilization and false sharing.

Example

I had a high-resolution Gaussian grid and wanted to convert it to a 1x1 degree grid. There were about 81 grid points in a 1 degree cell. The budget interpolation in -new_grid worked but it was slow and worked by taking 25 bilinear interpolations and averaging them to make the budget interpolation. So the pre-existing solution was slow and slighly inaccurate. To interpolate scalars to the 1x1 grid, you can run a box_average with 9x9 grid and then use -new_grid to get the cell average values. This method is, as expected, slightly smoother than the budget interpolation of -new_grid. For vectors, you have to use the budget interpolation of the -new_grid option.

See also: -new_grid,