The output allows many statistical calculations to be made. Here's an example of opening up a global model file and looking at the 1000 mb relative humidity, statistically,

ga-> set gxout stat
ga-> d rh
Data Type = grid
Dimensions = 0 1
I Dimension = 1 to 145
J Dimension = 1 to 73
Sizes = 145 73 10585
Undef value = 1e+20
Undef count = 0 Valid count = 10585
Min, Max = 0.0610352 100.061
Stats(sum,sumsqr,n): 787381 6.35439e+07 10585
Stats(sum,sumsqr)/n: 74.3865 6003.2
Stats(sum,sumsqr)/(n-1): 74.3935 6003.77
Stats(sigma,var)(n): 21.6761 469.854
Stats(sigma,var)(n-1): 21.6771 469.898
Cmin, cmax, cint = 10 100 10

Let's break it down:

Data Type = grid ----- you have a grid

Dimensions = 0 1 ----- the dimension type for the variable


0 - lon
1 - lat
2 - lev
3 - time

1 - not varying

I Dimension = 1 to 145 ------ obvious

J Dimension = 1 to 73

Sizes = 145 73 10585 ------- 10585 is 145*73 or total number of points

Undef value = 1e+20 ------- undefined value

Undef count = 0 Valid count = 10585 ----- # of defined and undefined points in the grid. Remember that if GrADS can't find any data it returns undefs. This is useful for checking if you have any data, Valid count = 0 means no...

Min, Max = 0.0610352 100.061 ---- UHHH OHHHH! we have slight supersaturation..

Stats(sum,sumsqr,n): 787381 6.35439e+07 10585 - This should be fairly obvious, sum = the simple sum of all defined points.

sumsqr = sum of, in this case, rh*rh and 10585 is n.

Stats(sum,sumsqr)/n: 74.3865 6003.2 - Divide by n for convenience, the first number is the "biased" mean...

Stats(sum,sumsqr)/(n-1): 74.3935 6003.77 - the so called unbiased mean (remove 1 degree of freedom), etc.

Stats(sigma,var)(n): 21.6761 469.854 - the standard deviation and variance "biased" (n)

Stats(sigma,var)(n-1): 21.6771 469.898 - the standard deviation and variance "unbiased" (n-1)

Cmin, cmax, cint = 10 100 10 - What GrADS will use when contouring.