Development of Gridded Innovations and Observations Data for Reanalysis Diagnostics
Abstract
Reanalysis users presently have no clear way to determine which observations have been assimilated at specific points in space and time. Furthermore, for those grid points where observations have been assimilated, users lack efficient quality control metrics to use in their interpretation of the reanalyses. In addition, observation-space data formats such as BUFR have not been deemed the most convenient or efficient to use by reanalysis users because of the complexity and availability of their libraries. To aid reanalysis users and their research, we propose an accompanying gridded data set based on the observations assimilated during the Modern Era Retrospective-analysis for Research and Applications Version 2 (MERRA-2) project. This new dataset is referred to as the MERRA-2 Gridded Innovations and Observations (GIO) and provides the assimilated observations along with the key statistics produced during the observational data assimilation, including (a) the mean forecast departure, (b) the standard deviation of the forecast departure, (c) the number of data counts as well as (d) the bias corrections for satellites radiances. To create GIO data, the observations and innovations are binned to a grid similar to that of MERRA-2 and saved in a convenient NetCDF file. This dataset provides a resource for teaching data assimilation and enables systematic evaluation of observing system impacts in reanalyses, as well as offering training data for machine learning and artificial intelligence applications.