UC Irvine

Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having highŠresolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. StateŠofŠtheŠart deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of highŠresolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.088 and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satelliteŠbased product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANNŠSDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANNŠCNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANNŠCNN outperforms PERSIANNŠCCS (and PERSIANNŠSDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the rootŠmean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANNŠCNN was lower than that of PERSIANNŠCCS (PERSIANNŠSDAE) by 37% (14%), showing the estimation accuracy of the proposed model.