Improving Satellite Quantitative Precipitation Estimates By Using Cloud Optical Depth

Geostationary satellite quantitative precipitation estimates (QPEs) provide continuous spatial coverage over the continental United States. As there are significant gaps in ground based radar coverage and rain gauge networks in the U.S., these geostationary satellite QPEs can be used to fill in both the spatial and temporal gaps of ground-based measurements. With the launch of GOES-R, the temporal resolution of geostationary satellite QPEs may be comparable to that of WSR-88D volume scans as GOES images will be available every five minutes. However, while these satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations particularly during convective events.

Deep Convective Systems (DCSs) have large cloud shields with similar cloud top temperatures for nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between cloud top temperature and precipitation rates often suffer from large errors because anvil regions (little/no precipitation) cannot be distinguished from rain cores (heavy precipitation) using only brightness temperatures. However, a combination of brightness temperatures and optical depth has been found to reduce overestimates of precipitation in anvil regions (Stenz et al 2014). A new rain mask algorithm incorporating both optical depth and brightness temperatures has been developed, and its application to the existing SCaMPR (Kuligowski 2002) algorithm was evaluated. To quantify the improvements made by the new rain mask in specific DCS components, the Feng classification algorithm (Feng et al. 2011) was used. SCaMPR estimates with the new rain mask applied benefited from significantly reduced overestimates of precipitation in anvil regions.