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Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

they need the integration with other systems for applications requiring high quantitative precisions, or spatial scales of about 1 km or less, or measurement updated timely and more frequently than 5 min. These are, for instance, desirable temporal and spatial resolutions for nowcasting purposes in hydrology ( WMO 2017 ). This scenario suggests that new measurement techniques and new data merging strategies are needed to improve the rainfall estimation at local scales. Nonconventional techniques

Open access
Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

progress in GEO sensor technologies along with the advancements in machine learning (ML) techniques, such as support vector machines, random forests, artificial neural network (ANN), deep learning, the new generation of precipitation retrieval algorithms must outperform the current operational products ( Meyer et al. 2016 ; Kuligowski et al. 2016 ; Sadeghi et al. 2019 ; Upadhyaya et al. 2020 ). In recent years, many studies have been conducted to utilize the generation sensor information to improve

Open access
Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed

Free access
Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the

Free access