scholarly journals Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts

2020 ◽  
Vol 24 (2) ◽  
pp. 1011-1030
Author(s):  
Hanoi Medina ◽  
Di Tian
Keyword(s):  
Bias Correction ◽  
Bayesian Model Averaging ◽  
Reference Evapotranspiration ◽  
Weather Prediction ◽  
Regional Climate ◽  
Reference Network ◽  
Model Ensemble ◽  
Post Processing ◽  
Single Model ◽  
Numerical Weather

Abstract. Reference evapotranspiration (ET0) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches, including the nonhomogeneous Gaussian regression (NGR), affine kernel dressing (AKD), and Bayesian model averaging (BMA) techniques, for improving daily and weekly ET0 forecasting based on single or multiple numerical weather predictions (NWPs) from the THORPEX Interactive Grand Global Ensemble (TIGGE), which includes the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), and the United Kingdom Meteorological Office (UKMO) forecasts. The approaches were examined for the forecasting of summer ET0 at 101 US Regional Climate Reference Network stations distributed all over the contiguous United States (CONUS). We found that the NGR, AKD, and BMA methods greatly improved the skill and reliability of the ET0 forecasts compared with a linear regression bias correction method, due to the considerable adjustments in the spread of ensemble forecasts. The methods were especially effective when applied over the raw NCEP forecasts, followed by the raw UKMO forecasts, because of their low skill compared with that of the raw ECMWF forecasts. The post-processed weekly forecasts had much lower rRMSE values (between 8 % and 11 %) than the persistence-based weekly forecasts (22 %) and the post-processed daily forecasts (between 13 % and 20 %). Compared with the single-model ensemble, ET0 forecasts based on ECMWF multi-model ensemble ET0 forecasts showed higher skill at shorter lead times (1 or 2 d) and over the southern and western regions of the US. The improvement was higher at a daily timescale than at a weekly timescale. The NGR and AKD methods showed the best performance; however, unlike the AKD method, the NGR method can post-process multi-model forecasts and is easier to interpret than the other methods. In summary, this study demonstrated that the three probabilistic approaches generally outperform conventional procedures based on the simple bias correction of single-model forecasts, with the NGR post-processing of the ECMWF and ECMWF–UKMO forecasts providing the most cost-effective ET0 forecasting.

scholarly journals Comparison of probabilistic post-processing approaches for improving NWP-based daily and weekly reference evapotranspiration forecasts

2019 ◽  
Author(s):  
Hanoi Medina ◽  
Di Tian
Keyword(s):  
Bias Correction ◽  
Bayesian Model Averaging ◽  
Reference Evapotranspiration ◽  
Model Averaging ◽  
The United States ◽  
Correction Method ◽  
Post Processing ◽  
Computationally Efficient ◽  
Single Model ◽  
Ensemble Forecasts

Abstract. Reference evapotranspiration (ETo) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches, including the nonhomogeneous Gaussian regression (NGR), affine kernel dressing (AKD), and Bayesian model averaging (BMA) techniques, for improving daily and weekly ETo forecasting based on single or multiple numerical weather predictions (NWP) from The International Grand Global Ensemble (TIGGE), including the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (UKMO). We found that the NGR, the AKD and the BMA methods greatly improved the skill and reliability of the ETo forecasts compared to a linear regression bias correction method, due to the considerable adjustments on the spread of ensemble forecasts. The methods were especially effective when applied over the weekly NCEP forecasts, followed by UKMO forecasts. The post-processed weekly forecasts had much lower rRMSE (between 8 %–11 %) than the persistence-based weekly forecasts (22 %), and the post-processed daily forecasts (13 %–20 %). Compared with the single model ETo forecasts based on ECMWF, multi-model ensemble ETo forecasts showed higher skill at short lead times (1 or 2 days) and over the southern and western regions of the United States. The improvement was higher at the daily timescale than at the weekly timescale. The NGR and AKD methods performed the best, but the NGR method is more flexible and computationally efficient than the other methods. In summary, the study demonstrated that the three probabilistic approaches generally outperform conventional procedures based on the simple bias correction of single model forecasts, with the NGR post-processing of the ECMWF and ECMWF-UKMO forecasts providing the most efficient ETo forecasting.

scholarly journals Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 300 ◽  
Author(s):  
Aida Jabbari ◽  
Deg-Hyo Bae
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Bias Correction ◽  
Lead Time ◽  
Weather Prediction ◽  
Total Least Squares ◽  
Time Dependent ◽  
Precipitation Forecast ◽  
Post Processing ◽  
The Real

Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under- and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short- to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method—known as dynamic weighting (DW)—to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively.

scholarly journals Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

scholarly journals Data assimilation of GNSS zenith total delays from a Nordic processing centre

2017 ◽  
Vol 17 (22) ◽  
pp. 13983-13998 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

L'évolution opérationnelle du modèle Arpège et de ses paramétrisations physiques

2021 ◽  
pp. 047
Author(s):  
François Bouyssel ◽  
Marta Janisková ◽  
Éric Bazile ◽  
Yves Bouteloup ◽  
Jean-Marcel Piriou
Keyword(s):  
Research And Development ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Post Processing ◽  
Dynamical Core ◽  
Var Analysis ◽  
Numerical Weather ◽  
Physical Parameterizations

Au cours de ses années à la direction du Groupe de modélisation et d'assimilation pour la prévision (Gmap), Jean-François Geleyn dirigea avec enthousiasme, dynamisme, efficacité et une grande expertise la préparation de toutes les évolutions des systèmes opérationnels de prévision numérique du temps Arpège et Aladin. Plusieurs évolutions majeures furent implémentées en opérationnel au cours de cette période. La contribution de Jean-François Geleyn fut notamment remarquable sur le noyau dynamique, le post-traitement des prévisions, leur évaluation et tout particulièrement les paramétrisations physiques des modèles de prévision. Il coordonna en effet tous les travaux de recherche et développement sur les paramétrisations physiques des modèles Arpège et Aladin et sur les paramétrisations physiques linéarisées développées pour l'analyse 4D-Var dans son groupe et chez les partenaires Aladin. Over the years when Jean-François Geleyn was the head of GMAP he led with great enthusiasm, dynamism, efficiency and expertise the preparation of all the evolutions of operational numerical weather prediction (NWP) systems of Arpège and Aladin. Several major changes were implemented operationally during this period. Jean-François Geleyn's contribution was remarkable in several topics such as the dynamical core, the post-processing of weather predictions, their validation, and especially the physical parameterizations of the numerical weather predictions models. In principle, he coordinated all the research and development activities on physical parameterizations for Arpège and Aladin NWP models and on the linearized physical parameterizations developed for the 4D-Var analysis in his group and among the Aladin partners.

scholarly journals Data assimilation of GNSS Zenith Total Delays from a Nordic processing centre

2017 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information obtained from Global Navigation Satellite System (GNSS) observations from ground-based receiver stations of the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art km-scale numerical weather prediction system. Different processing techniques have been implemented to derive the the moisture-related GNSS information in the form of Zenith Total Delays (ZTD) and these are described and compared. In addition full scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the following forecast quality. The sensitivity of results to aspects of the data processing, observation density, bias-correction and data assimilation have been investigated. Results show a benefit on forecast quality of using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

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