AbstractAbstract
This study tests the sensitivity of the National Centers for Environmental Prediction Global Forecast System (NCEP-GFS) forecasts to the presence of optically thick ice clouds with the Advanced Technology Microwave Sounder (ATMS) radiance observations. The tests use cloud phase/type (optically thick ice cloud amount) information available from the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud product and include GFS seasonal experiments with a modified thinning criterion to select ATMS observations with minimum optically thick ice cloud content. Results indicate that the data assimilation and forecasting performance of the GFS were sensitive to the thinning criteria and, thus, to the presence of optically thick ice cloud amounts in the ATMS observations. Data assimilation statistics such as the root-mean-square errors (RMSEs) for the observation minus background (
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B
) and observation minus analysis (
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A
) (ATMS brightness temperatures) showed significant changes, while the GFS forecasts of several important variables (geopotential heights, temperature) showed improvements. These findings show that the assimilation of ATMS observations in the GFS is sensitive to the presence of optically thick ice cloud amounts. The minimization of optically thick ice cloud amounts in ATMS observations could have a positive impact on the assimilation and forecasting performance of the GFS. They also present a case for future work focusing on inclusion/testing of supplemental cloud phase/type–specific tests to the existing thinning criteria in the GFS to eliminate these observations at the earliest stage of the data assimilation process.
Significance StatementThis study focuses on demonstrating the importance of removing microwave radiance observations impacted by the presence of optically thick ice clouds in the data assimilation system of an operational weather forecasting model [National Centers for Environmental Prediction Global Forecast System (NCEP-GFS)]. These observations are identified in the Advanced Technology Microwave Sounder (ATMS) dataset by using collocated Visible Infrared Imaging Radiometer Suite (VIIRS) information. Results show significant improvements in the forecasting performance of the GFS. Observations impacted by thick ice clouds can have adverse effects on the data assimilation and weather forecasting performance of NCEP-GFS. Findings from this study present the case to further refine existing microwave quality control to reduce the use of these observations.
