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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
1. Introduction Seasonal prediction of regional hydroclimate is typically based on deterministic physical models or statistical techniques, yet both approaches exhibit limited predictive ability ( Wang et al. 2009 ; National Academies of Sciences, Engineering, and Medicine 2016 ). Precipitation predictions based on deterministic physical models (regional climate models) exhibit high uncertainty due to imperfect physical conceptualizations, sensitivity to initial and boundary conditions, and
1. Introduction Seasonal prediction of regional hydroclimate is typically based on deterministic physical models or statistical techniques, yet both approaches exhibit limited predictive ability ( Wang et al. 2009 ; National Academies of Sciences, Engineering, and Medicine 2016 ). Precipitation predictions based on deterministic physical models (regional climate models) exhibit high uncertainty due to imperfect physical conceptualizations, sensitivity to initial and boundary conditions, and
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
Applications of Dynamic Land Surface Information for Passive Microwave Precipitation Retrievalcorrelated to regional climate conditions. Subsequent versions of GPROF addressed this by constraining the TRMM (ocean only) GPROF retrievals by two environmental parameters, namely total precipitable water (TPW) and sea surface temperature (SST) ( Kummerow et al. 2011 ). Moving forward to GPM, these same techniques were adapted to land surfaces, by replacing the SST with the 2 m air temperature commonly available from forecast and reanalysis models. In a series of papers describing and testing the Cloud
correlated to regional climate conditions. Subsequent versions of GPROF addressed this by constraining the TRMM (ocean only) GPROF retrievals by two environmental parameters, namely total precipitable water (TPW) and sea surface temperature (SST) ( Kummerow et al. 2011 ). Moving forward to GPM, these same techniques were adapted to land surfaces, by replacing the SST with the 2 m air temperature commonly available from forecast and reanalysis models. In a series of papers describing and testing the Cloud
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
several locations (e.g., Papalexiou and Koutsoyiannis 2013 ; Blanchet et al. 2016 ). If historical rainfall records are available at multiple sites, regional IDF curves are often generated by (i) spatially interpolating i ( T R , τ ) or parameters of the statistical distributions from local or at-site estimations, or (ii) applying regionalization techniques that merge rain gauges into homogeneous regions to increase robustness in the estimate of the statistical distribution parameters ( Hosking
several locations (e.g., Papalexiou and Koutsoyiannis 2013 ; Blanchet et al. 2016 ). If historical rainfall records are available at multiple sites, regional IDF curves are often generated by (i) spatially interpolating i ( T R , τ ) or parameters of the statistical distributions from local or at-site estimations, or (ii) applying regionalization techniques that merge rain gauges into homogeneous regions to increase robustness in the estimate of the statistical distribution parameters ( Hosking
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
Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class. Combining both direct (gauges) and remote (radar/radiometer) measurement techniques, using ground and in-orbit observations complemented by the state-of-the-art atmosphere simulations, the GPM constellation offers full global coverage of rain and snow every 30 min at a resolution of only 0.1° and a latency of only a few hours. Freely available precipitation products are implemented across a spectrum of decision-making scientific tools, ranging from hydrology to world health. To ensure user demands for
. Combining both direct (gauges) and remote (radar/radiometer) measurement techniques, using ground and in-orbit observations complemented by the state-of-the-art atmosphere simulations, the GPM constellation offers full global coverage of rain and snow every 30 min at a resolution of only 0.1° and a latency of only a few hours. Freely available precipitation products are implemented across a spectrum of decision-making scientific tools, ranging from hydrology to world health. To ensure user demands for
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
