Assimilating Cloud Optical Depth for Applications to Extreme Weather Events

Abstract

AbstractThis paper reports the assimilation of cloud optical depth datasets into a variational data assimilation system to improve cloud ice, cloud water, rain, snow, and graupel analysis in extreme weather events for improving forecasts. A cloud optical depth forward operator was developed and implemented in the Space and Time Multiscale Analysis System (STMAS), a multiscale three-dimensional variational analysis system. Using this improved analysis system, the NOAA GOES-15 DCOMP (Daytime Cloud Optical and Microphysical Properties) cloud optical depth products were assimilated to improve the microphysical states. For an eight-day period of extreme weather events in September 2013 in Colorado, the United States, the impact of the cloud optical depth assimilation on the analysis results and forecasts was evaluated. The DCOMP products improved the cloud ice and cloud water predictions significantly in convective and lower levels. The DCOMP products also reduced errors in temperature and relative humidity data at the top (250–150 hPa) and bottom (850–700 hPa) layers. With the cloud ice improvement at higher layers, the DCOMP products provided better forecasts of cloud liquid at low layers (900–700 hPa), temperature and wind at all layers, and relative humidity at middle and bottom layers. Furthermore, for this extreme weather event, both equitable threat score (ETS) and bias were improved throughout the 12 h period, with the most significant improvement observed in the first 3 h. This study will raise the expectation of cloud optical depth product assimilation in operational applications.