Assimilation of DMSP and TOVS Satellite Soundings in a Mesoscale Model

Abstract

A modeling investigation explores the impacts of the assimilation of satellite-retrieved soundings on forecast error in the Fifth-Generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model (MM5). Simulations of the period of the U.S. Air Force?s Contrail Experiment (18?29 September 1995) vary the initialization method and datasets assimilated, the performance of first-guess reanalysis, the imposition of quality control (QC) on the satellite data, and the frequency of the model update cycle. MM5 experiments employing four-dimensional data assimilation (FDDA) are compared with a control experiment without FDDA. In the former, combinations of conventional surface and radiosonde observations and retrieved temperature and moisture soundings from the Defense Meteorological Satellite Program (DMSP) and Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) satellite instruments are assimilated. Forecast error statistics for the experiments are computed and analyzed. It is found that for retrieved temperatures the DMSP and TOVS sounding datasets used have similar, reasonable accuracy, but for retrieved dewpoints they display significant, and more differing, errors. Overall, the TOVS retrievals obtained are of poorer quality than are the DMSP retrievals. Sensitivity tests reveal that imposing a QC filter on the satellite data prior to assimilation does improve the resultant MM5 simulations. With such QC, it is found that assimilating DMSP and TOVS soundings with the methods used can significantly improve the forecasts of both temperature and moisture variables in the MM5. Model performance, however, can still reflect the relative quality of the satellite retrievals assimilated, with the lower-error DMSP data yielding better simulations than do the TOVS data. Tests exploring the reanalysis of first-guess fields obtained from FDDA show that it does benefit the short-term (0?12 h) forecast but that significant gains diminish thereafter.