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specific hydrologic processes. For example, the Flash Flood Guidance (FFG) system used worldwide estimates runoff generation ( Sweeney 1992 ), However, FFG only addresses parts of the flood’s characteristics and does not focus on water propagation overland or along streams. It misses any occurrence of flooding downstream of the rainfall, especially the delay, magnitude, and duration of the flood. Because a flood forecasting system needs to describe these characteristics ahead of time, modern instances
specific hydrologic processes. For example, the Flash Flood Guidance (FFG) system used worldwide estimates runoff generation ( Sweeney 1992 ), However, FFG only addresses parts of the flood’s characteristics and does not focus on water propagation overland or along streams. It misses any occurrence of flooding downstream of the rainfall, especially the delay, magnitude, and duration of the flood. Because a flood forecasting system needs to describe these characteristics ahead of time, modern instances
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
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
algorithm (GPROF; Kummerow et al. 2001 , 2015 ), the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) family of products ( Ashouri et al. 2015 ; Hsu et al. 1997 ), and “cloud morphing”-based techniques such as the CPC morphing technique (CMORPH; Joyce et al. 2004 ; Xie et al. 2017 ), JAXA’s Global Satellite Mapping of Precipitation (GsMAP; Kubota et al. 2007 ), and NASA’s Integrated Multisatellite Retrievals for GPM (IMERG; Huffman et al. 2018
algorithm (GPROF; Kummerow et al. 2001 , 2015 ), the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) family of products ( Ashouri et al. 2015 ; Hsu et al. 1997 ), and “cloud morphing”-based techniques such as the CPC morphing technique (CMORPH; Joyce et al. 2004 ; Xie et al. 2017 ), JAXA’s Global Satellite Mapping of Precipitation (GsMAP; Kubota et al. 2007 ), and NASA’s Integrated Multisatellite Retrievals for GPM (IMERG; Huffman et al. 2018
1. Introduction For many hydrological, climate, and weather forecasting applications, an important quantity is the amount of precipitation that falls on Earth’s surface over a given time interval, i.e., the surface precipitation rate. A fully global satellite-based precipitation estimate that can transition across changing Earth surface conditions and complex land–water boundaries is an important capability for proper evaluation of the precipitation produced or diagnosed in weather and climate
1. Introduction For many hydrological, climate, and weather forecasting applications, an important quantity is the amount of precipitation that falls on Earth’s surface over a given time interval, i.e., the surface precipitation rate. A fully global satellite-based precipitation estimate that can transition across changing Earth surface conditions and complex land–water boundaries is an important capability for proper evaluation of the precipitation produced or diagnosed in weather and climate
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
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
the contribution of lateral terrestrial water flow on regionally hydrological cycle. Coupled with the height above nearest drainage (HAND) technique, the National Water Model (NWM) system with its core component as WRF-Hydro offers an operational framework for real-time and forecast flood guidance across the contiguous United States ( Johnson et al. 2019 ). As noted above, the WRF-Hydro system has been implemented for a wide range of research and operational prediction problems over the world
the contribution of lateral terrestrial water flow on regionally hydrological cycle. Coupled with the height above nearest drainage (HAND) technique, the National Water Model (NWM) system with its core component as WRF-Hydro offers an operational framework for real-time and forecast flood guidance across the contiguous United States ( Johnson et al. 2019 ). As noted above, the WRF-Hydro system has been implemented for a wide range of research and operational prediction problems over the world
products [e.g., Climate Prediction Center (CPC) unified precipitation estimates, Global Precipitation Climatology Center (GPCC) precipitation dataset, NCEP–NCAR reanalysis data] have become available at various spatial and temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, reanalysis) and data merging techniques. While such datasets are useful to investigate the spatial and temporal behavior in global precipitation ( Fischer and Knutti 2014 ; Ghosh 2012
products [e.g., Climate Prediction Center (CPC) unified precipitation estimates, Global Precipitation Climatology Center (GPCC) precipitation dataset, NCEP–NCAR reanalysis data] have become available at various spatial and temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, reanalysis) and data merging techniques. While such datasets are useful to investigate the spatial and temporal behavior in global precipitation ( Fischer and Knutti 2014 ; Ghosh 2012
half-hourly gauge–radar QPE from the Ground Validation Multi-Radar Multi-Sensor (GV-MRMS; Petersen et al. 2020 ) suite of products is used in this study as a high-quality reference to evaluate the satellite QPEs. GV-MRMS builds on the MRMS QPE that is derived from 176 WSR-88D radars and more than 18 000 automatic hourly rain gauges over the contiguous United States and Canada ( Zhang et al. 2016 ). Advanced data integration techniques are used to create 3D reflectivity mosaic grids and
half-hourly gauge–radar QPE from the Ground Validation Multi-Radar Multi-Sensor (GV-MRMS; Petersen et al. 2020 ) suite of products is used in this study as a high-quality reference to evaluate the satellite QPEs. GV-MRMS builds on the MRMS QPE that is derived from 176 WSR-88D radars and more than 18 000 automatic hourly rain gauges over the contiguous United States and Canada ( Zhang et al. 2016 ). Advanced data integration techniques are used to create 3D reflectivity mosaic grids and
distance from the coastline. c. GV-MRMS The evaluation of SPPs requires deriving high-quality reference rainfall datasets at the satellite product pixel spatial and temporal resolution. In this study as a reference dataset the high-resolution, ground-based, radar–rain gauge corrected precipitation dataset GV-MRMS ( Kirstetter et al. 2012 , 2018 ) is used. GV-MRMS builds on MRMS that uses advanced data integration techniques to create high-resolution 3D reflectivity mosaic grids and quantitative
distance from the coastline. c. GV-MRMS The evaluation of SPPs requires deriving high-quality reference rainfall datasets at the satellite product pixel spatial and temporal resolution. In this study as a reference dataset the high-resolution, ground-based, radar–rain gauge corrected precipitation dataset GV-MRMS ( Kirstetter et al. 2012 , 2018 ) is used. GV-MRMS builds on MRMS that uses advanced data integration techniques to create high-resolution 3D reflectivity mosaic grids and quantitative
smooth interpolation techniques which effect is similar to that of low-pass filtering. The issue of nonorthogonal errors across products is particularly critical when using techniques such as total least squares or triple collocation for QPEs evaluation (e.g., Alemohammad et al. 2015 ; Li et al. 2018 ; Lu et al. 2021 ), and more generally when ranking QPEs based on their consistency with supposedly “independent” data. For example, a QPE that would correctly reproduce the fine-scale variability of
smooth interpolation techniques which effect is similar to that of low-pass filtering. The issue of nonorthogonal errors across products is particularly critical when using techniques such as total least squares or triple collocation for QPEs evaluation (e.g., Alemohammad et al. 2015 ; Li et al. 2018 ; Lu et al. 2021 ), and more generally when ranking QPEs based on their consistency with supposedly “independent” data. For example, a QPE that would correctly reproduce the fine-scale variability of
