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distributions. In our experiments we show that BMA was calibrated and sharp for the period we considered. This indicates that BMA has the potential to provide both calibrated PoP forecasts, and calibrated and sharp probabilistic quantitative precipitation forecasts (PQPFs). In section 2 we review the BMA technique and describe our extension of it to precipitation. Then in section 3 we give results for daily 48-h forecasts of 24-h accumulated precipitation over the North American Pacific Northwest in
distributions. In our experiments we show that BMA was calibrated and sharp for the period we considered. This indicates that BMA has the potential to provide both calibrated PoP forecasts, and calibrated and sharp probabilistic quantitative precipitation forecasts (PQPFs). In section 2 we review the BMA technique and describe our extension of it to precipitation. Then in section 3 we give results for daily 48-h forecasts of 24-h accumulated precipitation over the North American Pacific Northwest in
on the impact on the forecast quality and analyzed soil moisture. Mon. Wea. Rev. , 135 , 300 – 314 . 10.1175/MWR3309.1 Ebert, E. E. , and McBride J. L. , 2000 : Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol. , 239 , 179 – 202 . 10.1016/S0022-1694(00)00343-7 Ebert, E. E. , Damrath U. , Wergen W. , and Baldwin M. E. , 2003 : The WGNE assessment of short-term quantitative precipitation forecasts. Bull. Amer. Meteor. Soc. , 84
on the impact on the forecast quality and analyzed soil moisture. Mon. Wea. Rev. , 135 , 300 – 314 . 10.1175/MWR3309.1 Ebert, E. E. , and McBride J. L. , 2000 : Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol. , 239 , 179 – 202 . 10.1016/S0022-1694(00)00343-7 Ebert, E. E. , Damrath U. , Wergen W. , and Baldwin M. E. , 2003 : The WGNE assessment of short-term quantitative precipitation forecasts. Bull. Amer. Meteor. Soc. , 84
consistency and statistical properties, which make it appealing for operational use. Operational experience has shown that the most prominent source of uncertainty in the flood forecasting process is due to the quantitative precipitation forecast (QPF). The aim of this paper is to present an application of a Bayesian processor for QPF output (BPO), which is to be used as an IUP for the BFS. The proposed processor quantifies the uncertainty of the QPF and is suited for offline execution. In contrast to the
consistency and statistical properties, which make it appealing for operational use. Operational experience has shown that the most prominent source of uncertainty in the flood forecasting process is due to the quantitative precipitation forecast (QPF). The aim of this paper is to present an application of a Bayesian processor for QPF output (BPO), which is to be used as an IUP for the BFS. The proposed processor quantifies the uncertainty of the QPF and is suited for offline execution. In contrast to the
1. Introduction Probabilistic quantitative precipitation forecasts (PQPFs) from ensemble systems provide quantitative guidance on forecast uncertainty that has the potential to improve forecast quality and utility. In contrast to a deterministic forecast, which predicts only a single outcome for precipitation quantity, an ensemble provides a discrete estimate of probability distributions across a range of precipitation rates. Timely, accurate PQPFs could provide valuable guidance for decision
1. Introduction Probabilistic quantitative precipitation forecasts (PQPFs) from ensemble systems provide quantitative guidance on forecast uncertainty that has the potential to improve forecast quality and utility. In contrast to a deterministic forecast, which predicts only a single outcome for precipitation quantity, an ensemble provides a discrete estimate of probability distributions across a range of precipitation rates. Timely, accurate PQPFs could provide valuable guidance for decision
1. Introduction The assessment of hydrogeological risk in small catchments requires the availability of skillful, high-resolution quantitative precipitation forecasts (QPFs), with a lead time of at least 24–48 h ( Ferraris et al. 2002 ; Siccardi et al. 2005 ), which is usually provided by the output of a limited area circulation model (LAM; Bacchi et al. 2003 ). Within this framework, the verification of the model prediction skill represents an essential step for the development of efficient
1. Introduction The assessment of hydrogeological risk in small catchments requires the availability of skillful, high-resolution quantitative precipitation forecasts (QPFs), with a lead time of at least 24–48 h ( Ferraris et al. 2002 ; Siccardi et al. 2005 ), which is usually provided by the output of a limited area circulation model (LAM; Bacchi et al. 2003 ). Within this framework, the verification of the model prediction skill represents an essential step for the development of efficient
1. Introduction Even though remarkable improvements have been made over the past decades in the deterministic predictions of temperature, humidity, winds, and other forecast variables using numerical weather prediction models, the improvements in quantitative precipitation forecasts (QPFs) are still relatively slow ( Sanders 1986 ; Applequist et al. 2002 ). The considerable difficulties surrounding the production of accurate QPFs beyond a few hours, in tandem with the large societal impacts of
1. Introduction Even though remarkable improvements have been made over the past decades in the deterministic predictions of temperature, humidity, winds, and other forecast variables using numerical weather prediction models, the improvements in quantitative precipitation forecasts (QPFs) are still relatively slow ( Sanders 1986 ; Applequist et al. 2002 ). The considerable difficulties surrounding the production of accurate QPFs beyond a few hours, in tandem with the large societal impacts of
1. Introduction Short-range quantitative precipitation forecasts (QPFs) are critical in providing flash flood warnings, information for transportation management (including aviation and highway decision making), and fire weather forecasting. Flash flood warnings require 30-min precipitation input, while river flood prediction requires 6-h QPFs. Due to a lack of good precipitation estimates at high temporal frequencies (such as hourly and less than hourly), this paper focuses on discussing 6-h
1. Introduction Short-range quantitative precipitation forecasts (QPFs) are critical in providing flash flood warnings, information for transportation management (including aviation and highway decision making), and fire weather forecasting. Flash flood warnings require 30-min precipitation input, while river flood prediction requires 6-h QPFs. Due to a lack of good precipitation estimates at high temporal frequencies (such as hourly and less than hourly), this paper focuses on discussing 6-h
1. Introduction A multimodel short-range ensemble forecasting (SREF) system from the summer of 2004 implemented as part of the National Oceanic and Atmospheric Administration’s New England High Resolution Temperature Program (NEHRTP; Stensrud et al. 2006 ) was used to investigate new methods for developing reliable probabilistic quantitative precipitation forecasts (PQPFs). The outcome was a simple binning technique that is very skillful and reliable in providing probabilistic quantitative
1. Introduction A multimodel short-range ensemble forecasting (SREF) system from the summer of 2004 implemented as part of the National Oceanic and Atmospheric Administration’s New England High Resolution Temperature Program (NEHRTP; Stensrud et al. 2006 ) was used to investigate new methods for developing reliable probabilistic quantitative precipitation forecasts (PQPFs). The outcome was a simple binning technique that is very skillful and reliable in providing probabilistic quantitative
routinely used to monitor and compare general forecast quality at operational prediction centers (e.g., Simmons and Hollingsworth 2002 ). The quality of quantitative precipitation forecasts (QPF) is typically measured in terms of categorical verification scores ( Jolliffe and Stephenson 2003 ), a process that requires the specification of a precipitation threshold. Examples for this category of QPF verification studies can be found, for instance, in Damrath et al. (2000) for Germany and Ebert et al
routinely used to monitor and compare general forecast quality at operational prediction centers (e.g., Simmons and Hollingsworth 2002 ). The quality of quantitative precipitation forecasts (QPF) is typically measured in terms of categorical verification scores ( Jolliffe and Stephenson 2003 ), a process that requires the specification of a precipitation threshold. Examples for this category of QPF verification studies can be found, for instance, in Damrath et al. (2000) for Germany and Ebert et al
quantitative precipitation forecasts (PQPFs) for the same set of forecasts documented by Yuan et al. (2005a) . Besides being of greater operational relevance, analysis of 6-h accumulation should help elucidate the differences at each 6-h forecast period between the skill of the 0000 and 1200 UTC forecasts and possibly reveal the reasons for the discrepancies. Section 2 of this paper briefly describes the ensemble configuration and verification datasets. Section 3 describes the model performance of the
quantitative precipitation forecasts (PQPFs) for the same set of forecasts documented by Yuan et al. (2005a) . Besides being of greater operational relevance, analysis of 6-h accumulation should help elucidate the differences at each 6-h forecast period between the skill of the 0000 and 1200 UTC forecasts and possibly reveal the reasons for the discrepancies. Section 2 of this paper briefly describes the ensemble configuration and verification datasets. Section 3 describes the model performance of the
