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already been explored in the context of precipitation downscaling and data assimilation ( Ebtehaj et al. 2012 ; Ebtehaj and Foufoula-Georgiou 2013 ) and climate forecasting [see the recent studies of DelSole and Banerjee (2017) and He et al. (2020) ], but suffers from ignoring the spatiotemporal dependencies among predictors. To respect the embedded space–time structure of the climate system and enforce sparsity, we use a “graph total variation” (GTV) regularizer (i.e., constraint) that promotes
already been explored in the context of precipitation downscaling and data assimilation ( Ebtehaj et al. 2012 ; Ebtehaj and Foufoula-Georgiou 2013 ) and climate forecasting [see the recent studies of DelSole and Banerjee (2017) and He et al. (2020) ], but suffers from ignoring the spatiotemporal dependencies among predictors. To respect the embedded space–time structure of the climate system and enforce sparsity, we use a “graph total variation” (GTV) regularizer (i.e., constraint) that promotes
Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluationwaters, coastlines, and sea ice edge. These classes come from a cluster analysis, purely empirical self-grouping of emissivity characteristics ( Prigent et al. 2006 ). The TPW and T2m parameters are obtained from the Global Atmospheric Analysis (GANAL; JMA 2000 ) and the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) reanalysis datasets for the operational and the climatological GPROF outputs, respectively. For this study, the 1C-R-GMI product (TBs) and the climatological 2A
waters, coastlines, and sea ice edge. These classes come from a cluster analysis, purely empirical self-grouping of emissivity characteristics ( Prigent et al. 2006 ). The TPW and T2m parameters are obtained from the Global Atmospheric Analysis (GANAL; JMA 2000 ) and the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) reanalysis datasets for the operational and the climatological GPROF outputs, respectively. For this study, the 1C-R-GMI product (TBs) and the climatological 2A
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
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
locations of the rain gauges of the FCDMC network, with colors indicating the corresponding record length. Urbanized areas are also shown. (d) Distribution of the intergauge distance. We use records of the Automated Local Evaluation in Real Time (ALERT) rain gauge network of the Flood Control District of the Maricopa County (FCDMC), installed to monitor in real time regional and localized storms, and support flood and flash-flood forecasting. The gauges have been gradually deployed since the beginning
locations of the rain gauges of the FCDMC network, with colors indicating the corresponding record length. Urbanized areas are also shown. (d) Distribution of the intergauge distance. We use records of the Automated Local Evaluation in Real Time (ALERT) rain gauge network of the Flood Control District of the Maricopa County (FCDMC), installed to monitor in real time regional and localized storms, and support flood and flash-flood forecasting. The gauges have been gradually deployed since the beginning
produced by the model’s dynamical equations and parameterizations, which are constrained through the assimilation of satellite radiances ( Benjamin et al. 2019 ). Hence, we refer to this as the “physics-based” approach. A number of datasets, particularly reanalyses such as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017 ) from NASA and ERA5 ( Hersbach et al. 2018 ) from the European Centre for Medium-Range Weather Forecasts assimilate PMW TBs
produced by the model’s dynamical equations and parameterizations, which are constrained through the assimilation of satellite radiances ( Benjamin et al. 2019 ). Hence, we refer to this as the “physics-based” approach. A number of datasets, particularly reanalyses such as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017 ) from NASA and ERA5 ( Hersbach et al. 2018 ) from the European Centre for Medium-Range Weather Forecasts assimilate PMW TBs
modeling ( Maggioni et al. 2011 ; Nikolopoulos et al. 2010 ; White and Singham 2012 ). Though ensemble prediction models have been developed for some applications, flood forecasting in particular ( Cloke and Pappenberger 2009 ), assembling such ensembles can be nontrivial, while for complex physics-based models, the requisite multiple simulations may not be computationally feasible for real-time applications. An alternative approach is to directly ingest precipitation distributions generated by SMPP
modeling ( Maggioni et al. 2011 ; Nikolopoulos et al. 2010 ; White and Singham 2012 ). Though ensemble prediction models have been developed for some applications, flood forecasting in particular ( Cloke and Pappenberger 2009 ), assembling such ensembles can be nontrivial, while for complex physics-based models, the requisite multiple simulations may not be computationally feasible for real-time applications. An alternative approach is to directly ingest precipitation distributions generated by SMPP
) forecasting (the gap between weather forecasts and seasonal climate predictions) using models and observations and assessment of uncertainty propagation to impact studies such as floods, droughts and ecological changes. IPC12 also aimed to provide a forum to explore new data analytic and machine learning (ML) methodologies, taking advantage of the unprecedented explosion of Earth observations from space and climate model outputs, for improved estimation and prediction. It also brought together scientists
) forecasting (the gap between weather forecasts and seasonal climate predictions) using models and observations and assessment of uncertainty propagation to impact studies such as floods, droughts and ecological changes. IPC12 also aimed to provide a forum to explore new data analytic and machine learning (ML) methodologies, taking advantage of the unprecedented explosion of Earth observations from space and climate model outputs, for improved estimation and prediction. It also brought together scientists
the daily and subdaily scales are more important from the standpoint of operational watershed hydrology and water resources management for applications such as flood forecasting. Furthermore, since PDIR-Now is an IR-based precipitation dataset, it is intended to be particularly advantageous in providing timely and adequate precipitation estimates when other datasets based on PMW and multisensor fusion are not available. With these considerations in mind, analysis of PDIR-Now at the daily and
the daily and subdaily scales are more important from the standpoint of operational watershed hydrology and water resources management for applications such as flood forecasting. Furthermore, since PDIR-Now is an IR-based precipitation dataset, it is intended to be particularly advantageous in providing timely and adequate precipitation estimates when other datasets based on PMW and multisensor fusion are not available. With these considerations in mind, analysis of PDIR-Now at the daily and
Enhancing PMW Satellite Precipitation Estimation: Detecting Convective ClassMicrowave Sounder API Application programming interface BB Bright band DNN Deep neural network DPR Dual-frequency precipitation radar ECMWF European Centre for Medium-Range Weather Forecasts FOV Field of view GANAL Global analysis GLM Geostationary Lightning Mapper GMI GPM Microwave Imager GPM Global Precipitation Measurement GPROF Goddard profiling algorithm GV-MRMS Ground Validation–Multi Radar/Multi Sensor HSS Heidke skill score IR Infrared JMA Japan Meteorological Agency MHS Microwave Humidity
Microwave Sounder API Application programming interface BB Bright band DNN Deep neural network DPR Dual-frequency precipitation radar ECMWF European Centre for Medium-Range Weather Forecasts FOV Field of view GANAL Global analysis GLM Geostationary Lightning Mapper GMI GPM Microwave Imager GPM Global Precipitation Measurement GPROF Goddard profiling algorithm GV-MRMS Ground Validation–Multi Radar/Multi Sensor HSS Heidke skill score IR Infrared JMA Japan Meteorological Agency MHS Microwave Humidity
Forecast System Reanalysis (CFSR) v2, and 5) Water and Global Change (WATCH) Forcing Data–ERA-Interim (WFDEI) version 14 August 2018. The PERSN-CDR, MSWEP, and WFDEI datasets combine information from observations, satellites, and reanalysis. The CPC uses only observations and the CFSR is purely a reanalysis product. PERSN-CDR is derived from the satellite data (Gridsat-B1), adjusted using the precipitation data from Global Precipitation Climatology Project ( Ashouri et al. 2015 ; Nguyen et al. 2018
Forecast System Reanalysis (CFSR) v2, and 5) Water and Global Change (WATCH) Forcing Data–ERA-Interim (WFDEI) version 14 August 2018. The PERSN-CDR, MSWEP, and WFDEI datasets combine information from observations, satellites, and reanalysis. The CPC uses only observations and the CFSR is purely a reanalysis product. PERSN-CDR is derived from the satellite data (Gridsat-B1), adjusted using the precipitation data from Global Precipitation Climatology Project ( Ashouri et al. 2015 ; Nguyen et al. 2018
