Improving Satellite Quantitative Precipitation Estimates By Using Cloud Optical Depth
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.