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precipitation events (the >10 mm 12 h −1 results were shown here). These system improvements suggest that with the use of this algorithm, the National Blend probabilistic precipitation forecasts will be of sufficient skill and reliability that they can and should be disseminated more widely. Currently, National Blend guidance does not include fully probabilistic quantitative precipitation forecast guidance, only the probability of nonzero precipitation (POP). The authors of this article intend to work with
precipitation events (the >10 mm 12 h −1 results were shown here). These system improvements suggest that with the use of this algorithm, the National Blend probabilistic precipitation forecasts will be of sufficient skill and reliability that they can and should be disseminated more widely. Currently, National Blend guidance does not include fully probabilistic quantitative precipitation forecast guidance, only the probability of nonzero precipitation (POP). The authors of this article intend to work with
et al. 2016 ; Yang et al. 2016 ). Ensemble members were further classified based on specific typhoon features to improve the QPF performance of typhoons ( Hong et al. 2015 ). The probabilistic quantitative precipitation forecast (PQPF) has been used as an objective parameter to assess and understand the overall performance of ensemble systems and also to develop postprocess bias corrections. For example, Ruiz et al. (2009) calculated PQPF over South America using several ensemble forecast
et al. 2016 ; Yang et al. 2016 ). Ensemble members were further classified based on specific typhoon features to improve the QPF performance of typhoons ( Hong et al. 2015 ). The probabilistic quantitative precipitation forecast (PQPF) has been used as an objective parameter to assess and understand the overall performance of ensemble systems and also to develop postprocess bias corrections. For example, Ruiz et al. (2009) calculated PQPF over South America using several ensemble forecast
EMOS model for probabilistic quantitative precipitation forecasting . Environmetrics , 27 , 280 – 292 , https://doi.org/10.1002/env.2391 . 10.1002/env.2391 Baran , S. , and Á. Baran , 2021 : Calibration of wind speed ensemble forecasts for power generation . Idojaras , 125 , 609 – 624 , https://doi.org/10.28974/idojaras.2021.4.4 . Baran , S. , and S. Lerch , 2018 : Combining predictive distributions for statistical post-processing of ensemble forecasts . Int. J. Forecast
EMOS model for probabilistic quantitative precipitation forecasting . Environmetrics , 27 , 280 – 292 , https://doi.org/10.1002/env.2391 . 10.1002/env.2391 Baran , S. , and Á. Baran , 2021 : Calibration of wind speed ensemble forecasts for power generation . Idojaras , 125 , 609 – 624 , https://doi.org/10.28974/idojaras.2021.4.4 . Baran , S. , and S. Lerch , 2018 : Combining predictive distributions for statistical post-processing of ensemble forecasts . Int. J. Forecast
precipitation products are available at the China Meteorological Data Service Centre ( http://data .cma.cn ). REFERENCES Baran , S. , and D. Nemoda , 2016 : Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting . Environmetrics , 27 , 280 – 292 , https://doi.org/10.1002/env.2391 . Barnston , A. G. , S. J. Mason , L. Goddard , D. G. DeWitt , and S. E. Zebiak , 2003 : Multimodel ensembling in seasonal climate
precipitation products are available at the China Meteorological Data Service Centre ( http://data .cma.cn ). REFERENCES Baran , S. , and D. Nemoda , 2016 : Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting . Environmetrics , 27 , 280 – 292 , https://doi.org/10.1002/env.2391 . Barnston , A. G. , S. J. Mason , L. Goddard , D. G. DeWitt , and S. E. Zebiak , 2003 : Multimodel ensembling in seasonal climate
flash flood monitoring, detection, and decision making. However, the operational FLASH products neither provide probabilistic information to communicate the uncertainty associated with the forecast, nor do they provide significantly longer flash flood forecast lead time. Therefore, to extend the hydrometeorological forecast lead time beyond the watershed response time, it is prudent to explore the use of short-term quantitative precipitation forecasts (QPFs) from NWP models as a forcing to the
flash flood monitoring, detection, and decision making. However, the operational FLASH products neither provide probabilistic information to communicate the uncertainty associated with the forecast, nor do they provide significantly longer flash flood forecast lead time. Therefore, to extend the hydrometeorological forecast lead time beyond the watershed response time, it is prudent to explore the use of short-term quantitative precipitation forecasts (QPFs) from NWP models as a forcing to the
wet and ER days for all the summer monsoon months. The GEFSv12 has relatively outperformed the GEFS-SubX for both categorical rainfall events during all the months and it is notably more for ER days. The area under the curve (AUC) is more than 0.6 in both models. Moreover, the AUC value of GEFSv12 is higher than the GEFS-SubX for both wet and ER days during all the months ( Fig. 3 ). Fig . 2. The reliability diagram for Taiwan probabilistic quantitative precipitation forecast (PQPF) from 2000
wet and ER days for all the summer monsoon months. The GEFSv12 has relatively outperformed the GEFS-SubX for both categorical rainfall events during all the months and it is notably more for ER days. The area under the curve (AUC) is more than 0.6 in both models. Moreover, the AUC value of GEFSv12 is higher than the GEFS-SubX for both wet and ER days during all the months ( Fig. 3 ). Fig . 2. The reliability diagram for Taiwan probabilistic quantitative precipitation forecast (PQPF) from 2000
1. Introduction Skillful and reliable probabilistic quantitative precipitation forecasts (PQPF) are necessary for a variety of applications. Forecasters at the National Oceanic Atmospheric Administration (NOAA) use PQPFs to provide impact-based decision support services to water resource managers and emergency personnel, especially for characterizing the uncertainty leading up to a possible extreme-precipitation event ( Dahl and Xue 2016 ). The ensemble precipitation data used to generate
1. Introduction Skillful and reliable probabilistic quantitative precipitation forecasts (PQPF) are necessary for a variety of applications. Forecasters at the National Oceanic Atmospheric Administration (NOAA) use PQPFs to provide impact-based decision support services to water resource managers and emergency personnel, especially for characterizing the uncertainty leading up to a possible extreme-precipitation event ( Dahl and Xue 2016 ). The ensemble precipitation data used to generate
: Improvements and application to a French large river basin . Atmos. Res. , 169 , 147 – 159 , doi: 10.1016/j.atmosres.2015.09.015 . 10.1016/j.atmosres.2015.09.015 Bontron , G. , 2004 : Prévision quantitative des précipitations: Adaptation probabiliste par recherche d’analogues. Utilisation des réanalyses NCEP/NCAR et application aux précipitations du sud-est de la France (Quantitative precipitation forecasts: Probabilistic adaptation by analogues sorting. Use of the NCEP/NCAR reanalyses and application
: Improvements and application to a French large river basin . Atmos. Res. , 169 , 147 – 159 , doi: 10.1016/j.atmosres.2015.09.015 . 10.1016/j.atmosres.2015.09.015 Bontron , G. , 2004 : Prévision quantitative des précipitations: Adaptation probabiliste par recherche d’analogues. Utilisation des réanalyses NCEP/NCAR et application aux précipitations du sud-est de la France (Quantitative precipitation forecasts: Probabilistic adaptation by analogues sorting. Use of the NCEP/NCAR reanalyses and application
al. 2022 ), we propose to use Unet, a type of denoising autoencoder, to generate high-resolution, accurate, and reliable probabilistic quantitative precipitation forecast (PQPF) in this work. Both input and output of Unet are high-resolution maps that cover the entire study domain which avoids training and running separate models at each grid point. We aim to address three important questions in this work: How can we adopt an Unet architecture for generating high-resolution precipitation forecasts
al. 2022 ), we propose to use Unet, a type of denoising autoencoder, to generate high-resolution, accurate, and reliable probabilistic quantitative precipitation forecast (PQPF) in this work. Both input and output of Unet are high-resolution maps that cover the entire study domain which avoids training and running separate models at each grid point. We aim to address three important questions in this work: How can we adopt an Unet architecture for generating high-resolution precipitation forecasts
Prediction , San Antonio, TX, Amer. Meteor. Soc., 10.1 , http://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47241.htm . Gagne , D. J. , II , A. McGovern , and M. Xue , 2014 : Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts . Wea. Forecasting , 29 , 1024 – 1043 , https://doi.org/10.1175/WAF-D-13-00108.1 . 10.1175/WAF-D-13-00108.1 Gagne , D. J. , II , S. E. Haupt , D. W. Nychka , and G. Thompson , 2019 : Interpretable deep
Prediction , San Antonio, TX, Amer. Meteor. Soc., 10.1 , http://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47241.htm . Gagne , D. J. , II , A. McGovern , and M. Xue , 2014 : Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts . Wea. Forecasting , 29 , 1024 – 1043 , https://doi.org/10.1175/WAF-D-13-00108.1 . 10.1175/WAF-D-13-00108.1 Gagne , D. J. , II , S. E. Haupt , D. W. Nychka , and G. Thompson , 2019 : Interpretable deep
