scholarly journals Development and Evaluation of the High Resolution Limited Area Ensemble Prediction System in the Korea Meteorological Administration

Atmosphere ◽  
2015 ◽  
Vol 25 (1) ◽  
pp. 67-83 ◽  
Author(s):  
SeHyun Kim ◽  
Hyun Mee Kim ◽  
Jun Kyung Kay ◽  
Seung-Woo Lee
Keyword(s):  
High Resolution ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Korea Meteorological Administration

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 A flood episode in northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions

2013 ◽  
Vol 17 (6) ◽  
pp. 2107-2120 ◽  
Author(s):  
S. Davolio ◽  
M. M. Miglietta ◽  
T. Diomede ◽  
C. Marsigli ◽  
A. Montani
Keyword(s):  
Horizontal Resolution ◽  
Northern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Model Ensemble ◽  
Single Model ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Meteorological Models

Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (northern Italy), for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the involved meteorological models. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members forced by the same large-scale conditions indicates that the systems are governed mainly by the boundary conditions, although the different limited area models' characteristics may still have a non-negligible impact.

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 A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations

2008 ◽  
Vol 136 (2) ◽  
pp. 443-462 ◽  
Author(s):  
Xiaoli Li ◽  
Martin Charron ◽  
Lubos Spacek ◽  
Guillem Candille
Keyword(s):  
Deep Convection ◽  
Horizontal Resolution ◽  
Forecast Skill ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Singular Vectors

Abstract A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.

Application and Verification of the ECMWF Precipitation Type Forecast Product (PTYPE) in China

2020 ◽  
Author(s):  
Quan Dong ◽  
Feng Zhang ◽  
Ning Hu ◽  
Zhiping Zong
Keyword(s):  
High Resolution ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Probability Forecast ◽  
Freezing Rain ◽  
Ensemble Prediction System ◽  
Optimal Probability ◽  
Probability Threshold ◽  
Precipitation Type

<p>The ECMWF (European Centre for Medium-Range Weather Forecasts) precipitation type forecast products—PTYPE are verified using the weather observations of more than 2000 stations in China of the past three winter half years (October to next March). The products include the deterministic forecast from High-resolution model (HRE) and the probability forecast from ensemble prediction system (EPS). Based on the verification results, optimal probability thresholds approaches under criteria of TS maximization (TSmax), frequency match (Bias1) and HSS maximization (HSSmax) are used to improve the deterministic precipitation type forecast skill. The researched precipitation types include rain, sleet, snow and freezing rain.</p><p>The verification results show that the proportion correct of deterministic forecast of ECMWF high-resolution model is mostly larger than 90% and the TSs of rain and snow are high, next is freezing rain, and the TS of sleet is small indicating that the forecast skill of sleet is limited. The rain and snow separating line of deterministic forecasts show errors of a little south in short-range and more and more significant north following elongating lead times in medium-range. The area of sleet forecasts is smaller than observations and the freezing rain is bigger for the high-resolution deterministic forecast. The ensemble prediction system offsets these errors partly by probability forecast. The probability forecast of rain from the ensemble prediction system is smaller than the observation frequency and the probability forecast of snow is larger in short-range and smaller in medium-range than the observation frequency. However, there are some forecast skills for all of these probability forecasts. There are advantages of ensemble prediction system compared to the high-resolution deterministic model. For rain and snow, for some special cost/loss ratio events the EPS is better than the HRD. For sleet and freezing rain, the EPS is better than the HRD significantly, especially for the freezing rain.</p><p>The optimal thresholds of snow and freezing rain are largest which are about 50%~90%, decreasing with elongating lead times. The thresholds of rain are small which are about 10%~20%, increasing with elongating lead times. The thresholds of sleet are the smallest which are under 10%. The verifications show that the approach of optimal probability threshold based on EPS can improve the forecast skill of precipitation type. The proportion correct of HRD is about 92%. Bias1 and TSmax improve it and the improvement of HSSmax is the most significant which is about 94%. The HSS of HRD is about 0.77~0.65. Bias1 increases 0.02 and TSmax increases more. The improvement of HSSmax is the biggest which is about 0.81~0.68 and the increasing rate is around 4%. From the verifications of every kinds of precipitation types, it is demonstrated that the approach of optimal probability threshold improves the performance of rain and snow forecasts significantly compared to the HRD and decreases the forecast area and missing of freezing rain and sleet which are forecasted more areas and false alarms by the HRD.</p><p><strong>Key words: </strong>ECMWF; ensemble prediction system；precipitation type forecast; approach of optimal probability threshold; verification</p>

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 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.

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