The proposed research includes improving RE-calibrated persiann-CCS at high latitudes as well as improving the algorithm for better estimation of warm rainfall. Validation of the improved warm rain…

This federal contract award to the University of California, Irvine (UC Irvine) is focused on improving the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System (PERSIANN-CCS) for better estimation of warm rainfall and over high latitudes. The $120,000.00 grant from the National Aeronautics and Space Administration (NASA) will integrate the developed algorithm into the Integrated Multi-satellite Retrieval for Global Precipitation Measurement (IMERG) system to enhance the monitoring of global precipitation. The contract does not have a set-aside designation, as UC Irvine is competing based on its research capabilities as a leading public research university. This award is part of NASA's broader efforts to improve precipitation measurement and modeling, which support applications such as flood forecasting, severe weather studies, and water resource management.

The proposed research includes improving RE-calibrated persiann-CCS at high latitudes as well as improving the algorithm for better estimation of warm rainfall. Validation of the improved warm rain estimation will include places where the warmer persiann-CCS threshold is especially significant, such as the subtropical high in the southeastern and northeastern pacific, the comparable area in the southeastern atlantic, and some examples over mid-latitude land (the U.S. For example). The developed algorithm will be integrated into the integrated multi-satellite retrieval for GPM (imerg) for the estimation of global precipitation. The proposed study will enhance our capability to monitor global precipitation in near real-time, at spatial and temporal scales relevant for a large number of applications, including flood forecasting, severe weather studies, and water resource systems operations.