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Calculates gridded binned averages and counts on a rectilinear grid using randomly spaced data.


	function bin_avg (
		zlon [*] : numeric ,  
		zlat [*] : numeric ,  
		z    [*] : numeric ,  
		glon [*] : numeric ,  
		glat [*] : numeric ,  
		opt  [1] : logical    



Longitudes of the observations (z).


Latitudes of the observations (z).


Observation values.


Longitudes of the output grid. These must be equally spaced.


Latitudes of the output grid. These must be equally spaced but may have different spacing than used for glon.


Option. Currently not used. Set to False.

Return value

A rectilinear grid of the size defined by glon and glat. The leftmost dimension will be size two (2). (See Example 1). The grid spacing must be equally spaced: eg: 1x1, 2x4, etc. The output array will be of type double if any of the input is double, and of type float otherwise.


This function averages values z contained within the rectilinear array defined by the input grid glon, glat. This takes advantage of the fact the latitude and longitude grid spacing is required to be constant. As noted in the argument description, the spacing in the latitude and longitude directions need not be equal. The appropriate grid point subscripts are determined via a simple algorithm.

Note: There was a bug in this routine in NCL versions 6.2.1 and earlier where the binning was done incorrectly if the observation locations were outside the region spanned by the user provided grid. This function wasn't explicitly checking for this situation.

See Also

bin_sum, triple2grid, triple2grid2d, poisson_grid_fill


Example 1

Assume zlon, zlat and zVal are one-dimensional (1D) arrays. Let lon and lat be 1D arrays of length M and N which specify grid locations. Then:

   grid = bin_avg(zlon,zlat,zVal, lon,lat, False)   ; (2,N,M)
will return a 3-dimensional array of size 2xNxM. The leftmost dimension is of size 2. The 0-th index is the binned variable; the 1-th contains the count.

Sometimes there are gaps in the returned array. If none are desired, then one approach would be to use poisson_grid_fill.