Wherever the mask values are less than zero, the values in expr are set to the missing data value.

Works with gridded or station data. Where mask values are positive, the expr values are not modified. Thus the result of maskout is data with a possibly increased number of missing data values. The maskout function, in spite of its apparent simplicity, is extremely useful.

Usage Notes


  1. See the Examples for the const function for a description of using maskout to calculate the percentage of the globe covered by precipitation.

  2. The maskout function can be used to cause part of the data to be ignored while doing another calculation. For example, if we have a land-sea mask, where sea values are negative, and we want to take some areal average of a quantity only over land:

    d aave(maskout(p,mask.2),lon=0,lon=360,lat=0,lat=90)

  3. People frequently have trouble using a mask grid, because it is often put into a seperate file, and given some arbitrary date/time and level. Thus, it is often necessary to locally override the dimension environment while using the mask grid:

    d aave(maskout(p,mask.2(t=1)),lon=0,lon=360,lat=0,lat=90)

    would probably be how Example 2 would have to be expressed in order to work, with the local override of t=1 specified on the mask data. See the documentation on how GrADS evaluates expressions within the dimension environment for more information.