scholarly journals Downscaling of ECMWF Ensemble Forecasts for Cases of Severe Weather: Ensemble Statistics and Cluster Analysis

2008 ◽  
Vol 136 (9) ◽  
pp. 3323-3342 ◽  
Author(s):  
Čedo Branković ◽  
Blaženka Matjačić ◽  
Stjepan Ivatek-Šahdan ◽  
Roberto Buizza
Keyword(s):  
Clustering Algorithm ◽  
Dynamical Downscaling ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Orographic Forcing ◽  
And Cluster Analysis

Abstract Dynamical downscaling has been applied to global ensemble forecasts to assess its impact for four cases of severe weather (precipitation and wind) over various parts of Croatia. It was performed with the Croatian 12.2-km version of the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, nested in the ECMWF TL255 (approximately 80 km) global ensemble prediction system (EPS). The 3-hourly EPS output was used to force the ALADIN model over the central European/northern Mediterranean domain. Results indicate that the identical clustering algorithm may yield differing results when applied to either global or to downscaled ensembles. It is argued that this is linked to the fact that a downscaled, higher-resolution ensemble resolves more explicitly small-scale features, in particular those strongly influenced by orographic forcing. This result has important implications in limited-area ensemble prediction, since it implies that downscaling may affect the interpretation or relevance of the global ensemble forecasts; that is, it may not always be feasible to make a selection (or a subset) of global lower-resolution ensemble members that might be representative of all possible higher-resolution evolution scenarios.

scholarly journals A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

2015 ◽  
Vol 30 (5) ◽  
pp. 1158-1181 ◽  
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Morris L. Weisman ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Wrf Model ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending

2014 ◽  
Vol 142 (5) ◽  
pp. 2043-2059 ◽  
Author(s):  
Yong Wang ◽  
Martin Bellus ◽  
Jean-Francois Geleyn ◽  
Xulin Ma ◽  
Weihong Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamical Downscaling ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Lateral Boundary Condition ◽  
Limited Area ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Blending Method

Abstract A blending method for generating initial condition (IC) perturbations in a regional ensemble prediction system is proposed. The blending is to combine the large-scale IC perturbations from a global ensemble prediction system (EPS) with the small-scale IC perturbations from a regional EPS by using a digital filter and the spectral analysis technique. The IC perturbations generated by blending can well represent both large-scale and small-scale uncertainties in the analysis, and are more consistent with the lateral boundary condition (LBC) perturbations provided by global EPS. The blending method is implemented in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF), in which the large-scale IC perturbations are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS), and the small-scale IC perturbations are generated by breeding in ALADIN-LAEF. Blending is compared with dynamical downscaling and breeding over a 2-month period in summer 2007. The comparison clearly shows impact on the growth of forecast spread if the regional IC perturbations are not consistent with the perturbations coming through LBC provided by the global EPS. Blending can cure the problem largely, and it performs better than dynamical downscaling and breeding.

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 The COSMO-LEPS mesoscale ensemble system: validation of the methodology and verification

2005 ◽  
Vol 12 (4) ◽  
pp. 527-536 ◽  
Author(s):  
C. Marsigli ◽  
F. Boccanera ◽  
A. Montani ◽  
T. Paccagnella
Keyword(s):  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Limited Area Model ◽  
Area Model ◽  
Extreme Precipitation Events ◽  
High Precipitation ◽  
A Minor ◽  
The Impact

Abstract. The limited-area ensemble prediction system COSMO-LEPS has been running every day at ECMWF since November 2002. A number of runs of the non-hydrostatic limited-area model Lokal Modell (LM) are available every day, nested on members of the ECMWF global ensemble. The limited-area ensemble forecasts range up to 120h and LM-based probabilistic products are disseminated to several national and regional weather services. Some changes of the operational suite have recently been made, on the basis of the results of a statistical analysis of the methodology. The analysis is presented in this paper, showing the benefit of increasing the number of ensemble members. The system has been designed to have a probabilistic support at the mesoscale, focusing the attention on extreme precipitation events. In this paper, the performance of COSMO-LEPS in forecasting precipitation is presented. An objective verification in terms of probabilistic indices is made, using a dense network of observations covering a part of the COSMO domain. The system is compared with ECMWF EPS, showing an improvement of the limited-area high-resolution system with respect to the global ensemble system in the forecast of high precipitation values. The impact of the use of different schemes for the parametrisation of the convection in the limited-area model is also assessed, showing that this have a minor impact with respect to run the model with different initial and boundary condition.

scholarly journals An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

2011 ◽  
Vol 15 (7) ◽  
pp. 2327-2347 ◽  
Author(s):  
N. Addor ◽  
S. Jaun ◽  
F. Fundel ◽  
M. Zappa
Keyword(s):  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Probability Forecasts ◽  
The City ◽  
Model Chain

Abstract. The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment. No definitive conclusion on the model chain capacity to forecast flooding events endangering the city of Zurich could be drawn because of the under-sampling of extreme events. Further research on the form of the reforecasts needed to infer on floods associated to return periods of several decades, centuries, is encouraged.

scholarly journals Evaluating current convection-permitting ensembles for past high-impact weather events in Italy: results from the SPITCAPE Special Project

2020 ◽  
Author(s):  
Valerio Capecchi ◽  
Bernardo Gozzini
Keyword(s):  
Dynamical Downscaling ◽  
Horizontal Resolution ◽  
High Impact ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Area Of Interest ◽  
Weather Events ◽  
Special Project ◽  
High Impact Weather

<p>The main goal of the ECMWF Special Project SPITCAPE is to understand the information content of the current ensemble systems both at global and meso scales in re-forecasting past high-impact weather events. In particular one of the main questions addressed in the project is: what is the added value of running a high-resolution (namely convection-permitting) ensembles for high-impact weather events with respect to global ones?<br>Running operational Ensemble Prediction Systems (EPS) at the convection-permitting (CP) scale is currently on the agenda at a number of European weather forecasting services and research centres: UK Met Office, Météo France and DWD to mention a few. Moreover, in the framework of the activities of the forthcoming ItaliaMeteo agency, it is foreseen the development of a regional EPS at CP scale for the Italian domain.<br>Recently, it has been demonstrated that the baseline approach of dynamical downscaling using CP models nested in a global ensemble with a coarser horizontal resolution (e.g. 20 km) provides valuable information. Since the introduction of the IFS model cycle 41r2 in March 2016, the horizontal resolution of the ECMWF ensemble forecasts (ENS) is about 18 km and it is planned to be further increased up to 10 km in the next future<br>(after the installation of the new supercomputer in the Bologna data center). Thus, these higher-resolution global ENS data allow us to estimate the technical feasibility and value of the simple dynamical downscaling method to initialise limited-area and CP models (the WRF-ARW, MESO-NH and MOLOCH models in the present case) directly nested in the new ECMWF global ensemble.<br>We applied this pragmatic approach in re-forecasting two high-impact weather events occurred in Italy in recent years (the Cinque Terre flooding occurred in October 2011 and the flash flood of Genoa in November 2011) with the ENS global forecasts and the data produced with the WRF-ARW, MESO-NH and MOLOCH models. The skills of the forecasts in the short-range are evaluated in terms of Probability of Precipitation exceeding predefined rainfall thresholds. In the medium-range we report and discuss the forecast uncertainty (i.e. ensemble spread) of ENS at different starting dates. Besides the fact that both global and regional model data under-estimate rainfall maxima in the area of interest, results demonstrate that CP ensemble forecasts provide better predictions regarding the occurrence of extreme precipitations and the area most likely affected.<br>The comparison among results obtained with regional models contribute to the debate regarding the reliability of these models and their strengths and weaknesses with respect to: (I) the accuracy of the results for the two events considered, (II) the integration with ECMWF products, (III) the ease of implementation and (IV) the computational costs in view of a potential use for operational forecasting activities.</p>

scholarly journals Generation of Scenarios from Calibrated Ensemble Forecasts with a Dual-Ensemble Copula-Coupling Approach

2016 ◽  
Vol 144 (12) ◽  
pp. 4737-4750 ◽  
Author(s):  
Zied Ben Bouallègue ◽  
Tobias Heppelmann ◽  
Susanne E. Theis ◽  
Pierre Pinson
Keyword(s):  
Probability Distributions ◽  
Forecast Error ◽  
Computational Cost ◽  
Joint Probability ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Joint Probability Distributions

Abstract Probabilistic forecasts in the form of ensembles of scenarios are required for complex decision-making processes. Ensemble forecasting systems provide such products but the spatiotemporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Nonparametric approaches allow the reconstruction of spatiotemporal joint probability distributions at a small computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach, called d-ECC, is applied to wind forecasts from the high-resolution Consortium for Small-Scale Modeling (COSMO) ensemble prediction system (EPS) run operationally at the German Weather Service (COSMO-DE-EPS). Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and within a product-oriented framework. Verification results over a 3-month period show that the innovative method d-ECC performs as well as or even outperforms ECC in all investigated aspects.

Comparison and Combination of Regional and Global Ensemble Prediction Systems for Probabilistic Predictions of Hub-Height Wind Speed

2015 ◽  
Vol 30 (5) ◽  
pp. 1234-1253 ◽  
Author(s):  
Constantin Junk ◽  
Stephan Späth ◽  
Lueder von Bremen ◽  
Luca Delle Monache
Keyword(s):  
Wind Speed ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Discrimination Ability ◽  
Adaptive Calibration ◽  
Error Correlation ◽  
Multimodel Ensembles ◽  
Prediction Systems

Abstract The objective of this paper is to compare probabilistic 100-m wind speed forecasts, which are relevant for wind energy applications, from different regional and global ensemble prediction systems (EPSs) at six measurement towers in central Europe and to evaluate the benefits of combining single-model ensembles into multimodel ensembles. The global 51-member EPS from the European Centre for Medium-Range Weather Forecasts (ECMWF EPS) is compared against the Consortium for Small-Scale Modelling’s (COSMO) limited-area 16-member EPS (COSMO-LEPS) and a regional, high-resolution 20-member EPS centered over Germany (COSMO-DE EPS). The ensemble forecasts are calibrated with univariate (wind speed) ensemble model output statistics (EMOS) and bivariate (wind vector) recursive and adaptive calibration (AUV). The multimodel ensembles are constructed by pooling together raw or best-calibrated ensemble forecasts. An additional postprocessing of these multimodel ensembles with both EMOS and AUV is also tested. The best-performing calibration methodology for ECMWF EPS is AUV, while EMOS performs better than AUV for the calibration of COSMO-DE EPS. COSMO-LEPS has similar skill when calibrated with both EMOS and AUV. The AUV ECMWF EPS outperforms the EMOS COSMO-LEPS and COSMO-DE EPS for deterministic and probabilistic wind speed forecast skill. For most thresholds, ECMWF EPS has a comparable reliability and sharpness but higher discrimination ability. Multimodel ensembles, which are constructed by pooling together the best-calibrated EPSs, improve the skill relative to the AUV ECMWF EPS. An analysis of the error correlation among the EPSs indicates that multimodel ensemble skill can be considerably higher when the error correlation is low.

scholarly journals Calibration of Limited-Area Ensemble Precipitation Forecasts for Hydrological Predictions

2014 ◽  
Vol 142 (6) ◽  
pp. 2176-2197 ◽  
Author(s):  
Tommaso Diomede ◽  
Chiara Marsigli ◽  
Andrea Montani ◽  
Fabrizio Nerozzi ◽  
Tiziana Paccagnella
Keyword(s):  
Linear Regression ◽  
Training Dataset ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Quantile Mapping ◽  
Ensemble Prediction System ◽  
Position Errors ◽  
Beneficial Impact ◽  
The Impact

Abstract The main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003–07 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The calibration provided a beneficial impact for the ensemble forecast over Switzerland and Germany; whereas, it resulted as less effective for Emilia-Romagna. The analog-based method seemed to be preferred because of its capability of correct position errors and spread deficiencies. A suitable spatial domain for the analog search can help to handle model spatial errors as systematic errors. However, the performance of the analog-based method may degrade in cases where a limited training dataset is available. The quantile-to-quantile mapping and linear regression methods were less effective, mainly because the forecast–analysis relation was not so strong for the available training dataset. The verification of the calibration process was then performed by coupling ensemble precipitation forecasts with a distributed rainfall–runoff model. This test was carried out for a medium-sized catchment located in Emilia-Romagna, showing a beneficial impact of the analog-based method on the reduction of missed events for discharge predictions.

scholarly journals Perturbing Surface Initial Conditions in a Regional Ensemble Prediction System

2016 ◽  
Vol 144 (9) ◽  
pp. 3377-3390 ◽  
Author(s):  
Martin Bellus ◽  
Yong Wang ◽  
Florian Meier
Keyword(s):  
Initial Conditions ◽  
Lower Troposphere ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Breeding Method ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Surface Data ◽  
Screen Level

Two techniques for perturbing surface initial conditions in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF) are presented and investigated in this paper. The first technique is the noncycling surface breeding (NCSB), which combines short-range surface forecasts driven by perturbed atmospheric forcing and the breeding method for generating the perturbations on surface initial conditions. The second technique, which is currently used in the ALADIN-LAEF operational version, applies an ensemble of surface data assimilations (ESDA) in which the observations are randomly perturbed. Both techniques are evaluated over a two-month period from late spring to summer. The results show that the evaluation is more favorable to ESDA. In general, the ensemble forecasts of the observed near-surface meteorological variables (screen-level variables) of ESDA are more skillful than NCSB, in particular for 2-m temperature they are statistically more consistent and reliable. A slightly better statistical reliability for 2-m relative humidity and 10-m wind has been found as well. This could be attributed to the introduction of surface data assimilation in ESDA, which provides more accurate surface initial conditions. Moreover, the observation perturbation in ESDA helps to better estimate the initial condition uncertainties. For the forecast of precipitation and the upper-air variables in the lower troposphere, both ESDA and NCSB perform very similarly, having neutral impact.

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