scholarly journals Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

2015 ◽  
Vol 54 (7) ◽  
pp. 1569-1578 ◽  
Author(s):  
S. Abhilash ◽  
A. K. Sahai ◽  
N. Borah ◽  
S. Joseph ◽  
R. Chattopadhyay ◽  
...  
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Probabilistic Prediction ◽  
Forecast System ◽  
Model Framework ◽  
Ensemble Prediction System ◽  
Physical Mechanisms ◽  
Uncertainty Estimates ◽  
Model Ensembles ◽  
Environmental Prediction

AbstractThis study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.

scholarly journals The Soverato flood in Southern Italy: performance of global and limited-area ensemble forecasts

2003 ◽  
Vol 10 (3) ◽  
pp. 261-274 ◽  
Author(s):  
A. Montani ◽  
C. Marsigli ◽  
F. Nerozzi ◽  
T. Paccagnella ◽  
S. Tibaldi ◽  
...  
Keyword(s):  
High Resolution ◽  
Southern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Probabilistic Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Limited Area Model ◽  
Area Model

Abstract. The predictability of the flood event affecting Soverato (Southern Italy) in September 2000 is investigated by considering three different configurations of ECMWF ensemble: the operational Ensemble Prediction System (EPS), the targeted EPS and a high-resolution version of EPS. For each configuration, three successive runs of ECMWF ensemble with the same verification time are grouped together so as to generate a highly-populated "super-ensemble". Then, five members are selected from the super-ensemble and used to provide initial and boundary conditions for the integrations with a limited-area model, whose runs generate a Limited-area Ensemble Prediction System (LEPS). The relative impact of targeting the initial perturbations against increasing the horizontal resolution is assessed for the global ensembles as well as for the properties transferred to LEPS integrations, the attention being focussed on the probabilistic prediction of rainfall over a localised area. At the 108, 84 and 60- hour forecast ranges, the overall performance of the global ensembles is not particularly accurate and the best results are obtained by the high-resolution version of EPS. The LEPS performance is very satisfactory in all configurations and the rainfall maps show probability peaks in the correct regions. LEPS products would have been of great assistance to issue flood risk alerts on the basis of limited-area ensemble forecasts. For the 60-hour forecast range, the sensitivity of the results to the LEPS ensemble size is discussed by comparing a 5-member against a 51-member LEPS, where the limited-area model is nested on all EPS members. Little sensitivity is found as concerns the detection of the regions most likely affected by heavy precipitation, the probability peaks being approximately the same in both configurations.

scholarly journals On the Impact of the Choice of Global Ensemble in Forcing a Regional Ensemble System

2016 ◽  
Vol 31 (2) ◽  
pp. 515-530 ◽  
Author(s):  
Florian Weidle ◽  
Yong Wang ◽  
Geert Smet
Keyword(s):  
Large Scale ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Lateral Boundary Condition ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Driving Model ◽  
Environmental Prediction ◽  
The Impact

Abstract It is quite common that in a regional ensemble system the large-scale initial condition (IC) perturbations and the lateral boundary condition (LBC) perturbations are taken from a global ensemble prediction system (EPS). The choice of global EPS as a driving model can have a significant impact on the performance of the regional EPS. This study investigates the impact of large-scale IC/LBC perturbations obtained from different global EPSs on the forecast quality of a regional EPS. For this purpose several experiments are conducted where the Aire Limitée Adaption dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) regional ensemble is forced by two of the world’s leading global ensembles, the European Centre for Medium-Range Weather Forecasts’ Ensemble Prediction System (ECMWF-EPS) and the Global Ensemble Forecasting System (GEFS) from the National Centers for Environmental Prediction (NCEP), which provide the IC and LBC perturbations. The investigation is carried out for a 51-day period during summer 2010 over central Europe. The results indicate that forcing of the regional ensemble with GEFS performs better for surface parameters, whereas at upper levels forcing with ECMWF-EPS is superior. Using perturbations from GEFS lead to a considerably higher spread in ALADIN-LAEF, which is beneficial near the surface where regional EPSs are usually underdispersive. At upper levels, forcing with GEFS leads to an overdispersion of ALADIN-LAEF as a result of the large spread of some parameters, where forcing ALADIN-LAEF with ECMWF-EPS provides statistically more reliable forecasts. The results indicate that the best global EPS might not always provide the best ICs and LBCs for a regional ensemble.

scholarly journals Analysis of the Relative Operating Characteristic and Economic Value Using the LAPS Ensemble Prediction System in Taiwan

2015 ◽  
Vol 143 (5) ◽  
pp. 1833-1848 ◽  
Author(s):  
Hui-Ling Chang ◽  
Shu-Chih Yang ◽  
Huiling Yuan ◽  
Pay-Liam Lin ◽  
Yu-Chieng Liou
Keyword(s):  
Decision Making ◽  
Operating Characteristic ◽  
Economic Value ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast System ◽  
Relative Operating Characteristic ◽  
Ensemble Prediction System ◽  
Discrimination Ability ◽  
Probabilistic Forecasts

Abstract Measurement of the usefulness of numerical weather prediction considers not only the forecast quality but also the possible economic value (EV) in the daily decision-making process of users. Discrimination ability of an ensemble prediction system (EPS) can be assessed by the relative operating characteristic (ROC), which is closely related to the EV provided by the same forecast system. Focusing on short-range probabilistic quantitative precipitation forecasts (PQPFs) for typhoons, this study demonstrates the consistent and strongly related characteristics of ROC and EV based on the Local Analysis and Prediction System (LAPS) EPS operated at the Central Weather Bureau in Taiwan. Sensitivity experiments including the effect of terrain, calibration, and forecast uncertainties on ROC and EV show that the potential EV provided by a forecast system is mainly determined by the discrimination ability of the same system. The ROC and maximum EV (EVmax) of an EPS are insensitive to calibration, but the optimal probability threshold to achieve the EVmax becomes more reliable after calibration. In addition, the LAPS ensemble probabilistic forecasts outperform deterministic forecasts in respect to both ROC and EV, and such an advantage grows with increasing precipitation intensity. Also, even without explicitly knowing the cost–loss ratio, one can still optimize decision-making and obtain the EVmax by using ensemble probabilistic forecasts.

scholarly journals Predictability of the Performance of an Ensemble Forecast System: Predictability of the Space of Uncertainties

2010 ◽  
Vol 138 (3) ◽  
pp. 962-981 ◽  
Author(s):  
Elizabeth Satterfield ◽  
Istvan Szunyogh
Keyword(s):  
Linear Space ◽  
Linear Representation ◽  
Ensemble Prediction ◽  
Local Error ◽  
Forecast System ◽  
Ensemble Prediction System ◽  
Ensemble Performance ◽  
Ensemble Transform Kalman Filter ◽  
Environmental Prediction ◽  
Forecast Time

Abstract The performance of an ensemble prediction system is inherently flow dependent. This paper investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model gridpoint location ℓ. A local error covariance matrix 𝗣ℓ is defined for each local region, and the diagnostics are applied to the linear space defined by the range of the ensemble-based estimate of 𝗣ℓ. The particular diagnostics are chosen to help investigate the efficiency of in capturing the space of analysis and forecast uncertainties. Numerical experiments are carried out with an implementation of the local ensemble transform Kalman filter (LETKF) data assimilation system on a reduced-resolution [T62 and 28 vertical levels (T62L28)] version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere in a realistic setting are used in these experiments. It is found that (i) paradoxically, the linear space provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time; (ii) provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth (i.e., for cases of lower predictability); and (iii) the ensemble dimension (E dimension) is a reliable predictor of the performance of in predicting the space of forecast uncertainties.

scholarly journals Multivariate Probabilistic Analysis and Predictability of Medium-Range Ensemble Weather Forecasts

2014 ◽  
Vol 142 (11) ◽  
pp. 4074-4090 ◽  
Author(s):  
Jessica Keune ◽  
Christian Ohlwein ◽  
Andreas Hense
Keyword(s):  
Covariance Matrix ◽  
Full Rank ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Verification Methods ◽  
Uncertainty Estimates ◽  
Medium Range ◽  
Prediction Systems

Abstract Ensemble weather forecasting has been operational for two decades now. However, the related uncertainty analysis in terms of probabilistic postprocessing still focuses on single variables, grid points, or stations. Inevitable dependencies in space and time and between variables are often ignored. To address this problem, two probabilistic postprocessing methods are presented, which are multivariate versions of Gaussian fit and kernel dressing, respectively. The multivariate case requires the estimation of a full rank, invertible covariance matrix. For this purpose, a Graphical Least Absolute Shrinkage and Selection Operators (GLASSO) estimator has been employed that is based on sparse undirected graphical models regularized by an L1 penalty term in order to parameterize the full rank inverse covariance. In all cases, the result is a multidimensional probability density. The forecasts used to test the approach are station forecasts of 2-m temperature and surface pressure from four main global ensemble prediction systems (EPS) with medium-range weather forecasts: the NCEP Global Ensemble Forecast System (GEFS), the Met Office Global and Regional Ensemble Prediction System (MOGREPS), the Canadian Meteorological Centre (CMC) Global Ensemble Prediction System (GEPS), and the ECMWF EPS. To evaluate the multivariate probabilistic postprocessing, especially the uncertainty estimates, common verification methods such as the analysis rank histogram and the continuous ranked probability score (CRPS) are applied. Furthermore, a multivariate extension of the CRPS, the energy score, allows for the verification of a complete medium-range forecast as well as for determining its predictability. It is shown that the predictability is similar for all of the examined ensemble prediction systems, whereas the GLASSO proved to be a useful tool for calibrating the commonly observed underdispersion of ensemble forecasts during the first few lead days by using information from the full covariance matrix.

Study of perturbing method in regional BGM ensemble prediction system

2012 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Xiao Yu-Hua ◽  
He Guang-Bi ◽  
Chen Jing ◽  
Deng Guo
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

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