Search Results

You are looking at 1 - 10 of 110 items for :

  • Probabilistic Quantitative Precipitation Forecasting (PQPF) x
  • All content x
Clear All
Thomas M. Hamill and Michael Scheuerer

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

Full access
Hsu-Feng Teng, James M. Done, Cheng-Shang Lee, and Ying-Hwa Kuo

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

Restricted access
Nusrat Yussouf, Katie A. Wilson, Steven M. Martinaitis, Humberto Vergara, Pamela L. Heinselman, and Jonathan J. Gourley

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

Free access
Joseph Bellier, Isabella Zin, and Guillaume Bontron

: 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

Full access
Eric D. Loken, Adam J. Clark, Amy McGovern, Montgomery Flora, and Kent Knopfmeier

Prediction , San Antonio, TX, Amer. Meteor. Soc., 10.1 , . 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 , . 10.1175/WAF-D-13-00108.1 Gagne , D. J. , II , S. E. Haupt , D. W. Nychka , and G. Thompson , 2019 : Interpretable deep

Full access
Thomas M. Gowan, W. James Steenburgh, and Craig S. Schwartz

1. Introduction Recent increases in computational capabilities have allowed for the development of ensemble numerical weather prediction (NWP) modeling systems with horizontal grid spacings ≤ 4 km, such that cumulus parameterizations can be omitted ( Kain et al. 2008 ). Commonly referred to as “convection permitting” ensembles (CPEs), these modeling systems offer significant promise for improving quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) over the western United

Full access
Malik Rizwan Asghar, Tomoki Ushiyama, Muhammad Riaz, and Mamoru Miyamoto

more realistic topography and atmospheric circulations ( Davolio et al. 2008 ; Roberts et al. 2009 ; Yan and Gallus 2016 ; Yu et al. 2016 ). As computational power and observational data quality and quantity improve, the quantitative precipitation forecasting (QPF) by NWP has substantially increased in their capability. Bartholmes and Todini (2005) conducted research employing the combinations of precipitation forecasts by mesoscale atmospheric models of different resolutions with a

Full access
Wentao Li, Qingyun Duan, and Quan J. Wang

postprocessing using quantile mapping and rank-weighted best-member dressing . Mon. Wea. Rev. , 146 , 4079 – 4098 , . 10.1175/MWR-D-18-0147.1 Hamill , T. M. , and J. S. Whitaker , 2006 : Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application . Mon. Wea. Rev. , 134 , 3209 – 3229 , . 10.1175/MWR3237.1 Klein , W. H. , B. M. Lewis , and I. Enger , 1959 : Objective prediction

Full access
Michael Scheuerer and Thomas M. Hamill

all of the raw ensemble forecasts are zero. A new diagnostic tool, the fraction of threshold exceedance (FTE) histogram, has been proposed and was used to demonstrate that the MDSS-SDA and ECC-mQ-SNP ensembles represent the fractional areal coverage of precipitation exceeding a predefined threshold better than the StSS ensembles for all thresholds and better than the MDSS-RO and ECC-Q ensembles at the lowest threshold. These conclusions were confirmed by quantitative verification of probabilistic

Full access
Eric D. Loken, Adam J. Clark, Ming Xue, and Fanyou Kong

forecasts are further evaluated using AUC (e.g., Marzban 2004 ), which measures a forecast system’s ability to discriminate between events and nonevents (e.g., Mason and Graham 2002 ). AUC values greater than or equal to 0.70 are considered useful in an ensemble framework ( Buizza et al. 1999 ). The same five precipitation thresholds used in the FSS analysis are used in the AUC computations to convert the quantitative precipitation forecasts (QPF) into binary forecasts. In each ensemble member, grid

Full access