Expression Evaluation Using "offt" Dimension Override

Motivation
Syntax
A Diagonal Slice
A Time-Varying Diagonal Slice
Applying a Function Over a Time-Varying Diagonal Slice

Motivation

The time axis in the GrADS environment is a simple, linear, one-dimensional object, defined by its start time, a time increment, and its length. The ensemble axis in the GrADS environment is also a linear, one-dimensional object, but it can become complicated when the metadata for individual ensemble members is time axis related. Such is the case when individual ensemble members have a start time and a length that is different from the defined time axis. For these kinds of data sets, the notion of "initial time" takes on a subtlety: clarifying the difference between the first time step in the defined time axis, and the first time step for an individual member.

Syntax

Beginning with version 2.0.a7, GrADS has a new interface for making the clarification between absolute time axis indices and indices that are offsets from an ensemble member's initial time. It has been implemented in the expression syntax as part of a complete variable declaration, which has three components:

abbrev.file#(dimexpr,dimexpr,...)    where:

abbrev   is the abbreviation for the variable as specified in the data descriptor file
file#    is the file number that contains this variable. If file# is absent, GrADS assumes the default file number.
dimexpr  is a dimension expression that locally modifies the current dimension environment.

The new interface allows a dimexpr that specifies an offset from the variable's initial time (which may be different for each ensemble member). The syntax is "offt" followed by "=/+/-" followed by an offset value.

The "offt" dimension override is currently supported only for variables associated with a data file -- it will not work with defined variables or for dimension expressions given as arguments to functions such as ave or sum.

A Diagonal Slice

For the purposes of explaining how to use expressions that employ the "offt" dimension override, we use a lag ensemble data set that contains 10 members. Each member is a 96-hour forecast, with output every 6 hours, and initialized at 12-hour intervals. The figure below illustrates the coverage of all 10 members in the Time-Ensemble domain; the rainbow colored boxes represent indivual time steps in each member.

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