scholarly journals Probabilistic high-resolution forecast of heavy precipitation over Central Europe

2004 ◽  
Vol 4 (2) ◽  
pp. 315-322 ◽  
Author(s):  
C. Marsigli ◽  
A. Montani ◽  
F. Nerozzi ◽  
T. Paccagnella
Keyword(s):  
High Resolution ◽  
Central Europe ◽  
Heavy Precipitation ◽  
Precipitation Event ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Precipitation Structure ◽  
High Precipitation

Abstract. The limited-area ensemble prediction system COSMO-LEPS has been running operationally at ECMWF since November 2002. Five runs of the non-hydrostatic limited-area model Lokal Modell (LM) are available every day, nested on five selected members of three consecutive 12-h lagged ECMWF global ensembles. The limited-area ensemble forecasts range up to 120h and LM-based probabilistic products are disseminated to several national weather services. COSMO-LEPS has been constructed in order to have a probabilistic system with high resolution, focussing the attention on extreme events in regions with complex orography. In this paper, the performance of COSMO-LEPS for a heavy precipitation event that affected Central Europe in August 2002 has been examined. At the 4-day forecast range, the probability maps indicate the possibility of the overcoming of high precipitation thresholds (up to 150mm/24h) over the region actually affected by the flood. Furthermore, one out of the five ensemble members predicts 4 days ahead a precipitation structure very similar to the observed one.

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 The Dynamics of an Extreme Precipitation Event in Northeastern Vietnam in 2015 and Its Predictability in the ECMWF Ensemble Prediction System

2017 ◽  
Vol 32 (3) ◽  
pp. 1041-1056 ◽  
Author(s):  
Roderick van der Linden ◽  
Andreas H. Fink ◽  
Joaquim G. Pinto ◽  
Tan Phan-Van
Keyword(s):  
Large Scale ◽  
Extreme Event ◽  
Coastal Region ◽  
Economic Loss ◽  
Precipitation Event ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Upper Level ◽  
Quang Ninh

Abstract A record-breaking rainfall event occurred in northeastern Vietnam in late July–early August 2015. The coastal region in Quang Ninh Province was hit severely, with station rainfall sums in the range of 1000–1500 mm. The heavy rainfall led to flooding and landslides, which resulted in an estimated economic loss of $108 million (U.S. dollars) and 32 fatalities. Using a multitude of data sources and ECMWF ensemble forecasts, the synoptic–dynamic development and practical predictability of the event is investigated in detail for the 4-day period from 1200 UTC 25 July to 1200 UTC 29 July 2015, during which the major portion of the rainfall was observed. A slowly moving upper-level subtropical trough and the associated surface low in the northern Gulf of Tonkin promoted sustained moisture convergence and convection over northeastern Vietnam. The humidity was advected in a moisture transport band lying across the Indochina Peninsula and emanating from a tropical storm over the Bay of Bengal. Analyses of the ECMWF ensemble forecasts clearly showed a sudden emergence of the predictability of the extreme event at lead times of 3 days that was associated with the correct forecasts of the intensity and location of the subtropical trough in the 51 ensemble members. Thus, the Quang Ninh event is a good example in which the predictability of tropical convection arises from large-scale synoptic forcing; in the present case it was due to a tropical–extratropical interaction that has not been documented before for the region and season.

scholarly journals Hydrometeorological Short-Range Ensemble Forecasts in Complex Terrain. Part II: Economic Evaluation

2008 ◽  
Vol 23 (4) ◽  
pp. 557-574 ◽  
Author(s):  
Doug McCollor ◽  
Roland Stull
Keyword(s):  
High Resolution ◽  
Utility Function ◽  
Short Range ◽  
Deterministic Model ◽  
Ensemble Average ◽  
Ensemble Prediction ◽  
Value Assessment ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydroelectric Reservoir

Abstract Two economic models are employed to perform a value assessment of short-range ensemble forecasts of 24-h precipitation probabilities for hydroelectric reservoir operation. Using a static cost–loss model, the value of the probability information is compared to the values of a deterministic control high-resolution forecast and of an ensemble-average forecast for forecast days 1 and 2. It is found that the probabilistic ensemble forecast provides value to a much wider range of hydroelectric operators than either the deterministic high-resolution forecast or the ensemble-average forecast, although for a small subset of operators the value of the three forecasts is the same. Forecasts for day-1 precipitation provide measurably higher value than forecasts for day-2 precipitation because of the loss of skill in the longer-range forecasts. A decision theory model provides a continuous-variable weighting of a user-specific utility function. The utility function weights are supplied by the ensemble prediction system, and the outcome is compared with weights calculated from a deterministic model, from the ensemble average, and from climatology. It is found that the methods employing the full ensemble and the ensemble average outperform the single deterministic model and climatology for the hydroelectric reservoir scenario studied.

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 Statistical Postprocessing and Multivariate Structuring of High-Resolution Ensemble Precipitation Forecasts

2018 ◽  
Vol 19 (11) ◽  
pp. 1815-1833 ◽  
Author(s):  
Chiem van Straaten ◽  
Kirien Whan ◽  
Maurice Schmeits
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Deep Convection ◽  
Winter Season ◽  
End Users ◽  
Ensemble Prediction ◽  
High Dimensional ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts

A comparison of statistical postprocessing methods is performed for high-resolution precipitation forecasts. We keep hydrological end users in mind and thus require that the systematic errors of probabilistic forecasts are corrected and that they show a realistic high-dimensional spatial structure. The most skillful forecasts of 3-h accumulated precipitation in 3 × 3 km2 grid cells covering the land surface of the Netherlands were made with a nonparametric method [quantile regression forests (QRF)]. A parametric alternative [zero-adjusted gamma distribution (ZAGA)] corrected the precipitation forecasts of the short-range Grand Limited Area Model Ensemble Prediction System (GLAMEPS) up to +60 h less well, particularly at high quantiles, as verified against calibrated precipitation radar observations. For the subsequent multivariate restructuring, three empirical methods, namely, ensemble copula coupling (ECC), the Schaake shuffle (SSh), and a recent minimum-divergence sophistication of the Schaake shuffle (MDSSh), were tested and verified using both the multivariate variogram skill score (VSS) and the continuous ranked probability score (CRPS), the latter after aggregating the forecasts spatially. ECC and MDSSh were more skillful than SSh in terms of the CRPS and the VSS. ECC performed somewhat worse than MDSSh for summer afternoon and evening periods, probably due to the worse representation of deep convection in the hydrostatic GLAMEPS compared to reality. Overall, the high-resolution postprocessing comparison shows that skill for local precipitation amounts improves up to the 98th percentile in both the summer and winter season and that the high-dimensional joint distribution can successfully be restructured. Forecasting products like this enable multiple end users to derive their own desired aggregations.

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.

scholarly journals Probabilistic Flood Forecasting with a Limited-Area Ensemble Prediction System: Selected Case Studies

2007 ◽  
Vol 8 (4) ◽  
pp. 897-909 ◽  
Author(s):  
M. Verbunt ◽  
A. Walser ◽  
J. Gurtz ◽  
A. Montani ◽  
C. Schär
Keyword(s):  
Case Studies ◽  
Heavy Precipitation ◽  
Hydrologic Model ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Rhine River ◽  
Time Step ◽  
Ensemble Prediction System ◽  
Runoff Forecasting

Abstract A high-resolution atmospheric ensemble forecasting system is coupled to a hydrologic model to investigate probabilistic runoff forecasts for the alpine tributaries of the Rhine River basin (34 550 km2). Five-day ensemble forecasts consisting of 51 members, generated with the global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), are downscaled with the limited-area model Lokal Modell (LM). The resulting limited-area ensemble prediction system (LEPS) uses a horizontal grid spacing of 10 km and provides one-hourly output for driving the distributed hydrologic model Precipitation–Runoff–Evapotranspiration–Hydrotope (PREVAH) hydrologic response unit (HRU) with a resolution of 500 × 500 m2 and a time step of 1 h. The hydrologic model component is calibrated for the river catchments considered, which are characterized by highly complex topography, for the period 1997–98 using surface observations, and validated for 1999–2002. This study explores the feasibility of atmospheric ensemble predictions for runoff forecasting, in comparison with deterministic atmospheric forcing. Detailed analysis is presented for two case studies: the spring 1999 flood event affecting central Europe due to a combination of snowmelt and heavy precipitation, and the November 2002 flood in the Alpine Rhine catchment. For both cases, the deterministic simulations yield forecast failures, while the coupled atmospheric–hydrologic EPS provides appropriate probabilistic forecast guidance with early indications for extreme floods. It is further shown that probabilistic runoff forecasts using a subsample of EPS members, selected by a cluster analysis, properly represent the forecasts using all 51 EPS members, while forecasts from randomly chosen subsamples reveal a reduced spread compared to the representative members. Additional analyses show that the representation of horizontal advection of precipitation in the atmospheric model may be crucial for flood forecasts in alpine catchments.

scholarly journals Comparison of Postprocessing Methods for the Calibration of 100-m Wind Ensemble Forecasts at Off- and Onshore Sites

2014 ◽  
Vol 53 (4) ◽  
pp. 950-969 ◽  
Author(s):  
Constantin Junk ◽  
Lueder von Bremen ◽  
Martin Kühn ◽  
Stephan Späth ◽  
Detlev Heinemann
Keyword(s):  
Central Europe ◽  
Forecast Skill ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Wind Vector ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Calibration Methods ◽  
Ensemble Model Output Statistics ◽  
Variance Deficit

AbstractEnsemble forecasts are a valuable addition to deterministic wind forecasts since they allow the quantification of forecast uncertainties. To remove common deficiencies of ensemble forecasts such as biases and ensemble spread deficits, various postprocessing methods for the calibration of wind speed (univariate calibration) and wind vector (bivariate calibration) ensemble forecasts have been developed in recent years. The objective of this paper is to compare the performance of state-of-the-art calibration methods at distinct off- and onshore sites in central Europe. The aim is to identify calibration- and site-dependent improvements in forecast skill over uncalibrated 100-m ensemble forecasts from the ECMWF Ensemble Prediction System. The ensemble forecasts were evaluated at four onshore and three offshore measurement towers in central Europe at 100-m height for lead times up to 5 days. The results show that the recursive and adaptive wind vector calibration (AUV) outperforms calibration methods such as univariate ensemble model output statistics (EMOS), bivariate EMOS, variance deficit calibration, and ensemble copula coupling in terms of the root-mean-square error and continuous ranked probability score at almost all sites. It was found that exponential downweighting of past measurements in AUV contributes to higher forecast skill since similar downweighting approaches in the other calibration methods improved forecast skill. Proposing a bidimensional bias correction in bivariate EMOS similar to the approach taken in AUV yields bivariate EMOS skill at onshore sites that is similar to AUV skill. Deterministic and probabilistic improvements are usually much lower at offshore sites and increase with increasing complexity of the site characteristics since systematic forecast errors and ensemble underdispersion are larger at high-roughness sites.

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