The regridding operation (g3Regrid) has been implemented for services which require datasets with the same resolution. This operation will be especially useful for data intercomparison services (such as Correlation Maps, Lat-Lon map of time averaged differences, etc.) where the data are provided by different instruments. The regridding step gives the user sufficient flexibility to run intercomparison services for data products with different spatial resolutions. To reduce the computational burden, the regridding operation first checks if the datasets have the same spatial resolution. If the datasets do share the same resolution, the data is retained at that resolution and the service proceeds to the next operation. If the datasets do not have the same resolution, the regridding operation regrids the data to the coarsest spatial resolution of the datasets invoked by the service.
The regridding operation initiates by obtaining the dataset information provided by previousoperations of the requested service. As stated before, it checks all of the dataset spatial resolutions, and if the spatial resolution of all the datasets is the same, regridding is not required and the computation proceeds to the next operation. If the spatial resolutions differ, then the spatial resolution of each dataset is regridded to a common spatial resolution, which will be the coarsest spatial resolution among all the datasets. The dataset possessing the coarsest spatial resolution does not require regridding.
The regridding operation currently implements the Box Averaging algorithm provided by GrADS as the regridding function. The regridding operation itself is implemented such that it will be able to support other regridding functions in the future, such as Vote interpolation, Bi-Linear Interpolation, Bessel Interpolation, etc. One of the future goals for this operatiion s to be capable of regridding to the finest spatial resolution, or to a custom spatial resolution provided by the user.