scholarly journals The Impact of Horizontal Resolution and Ensemble Size on Probabilistic Forecasts of Precipitation by the ECMWF Ensemble Prediction System

2002 ◽  
Vol 17 (2) ◽  
pp. 173-191 ◽  
Author(s):  
Steven L. Mullen ◽  
Roberto Buizza
Keyword(s):  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
The Impact

Fog Prediction from a Multimodel Mesoscale Ensemble Prediction System

2010 ◽  
Vol 25 (1) ◽  
pp. 303-322 ◽  
Author(s):  
Binbin Zhou ◽  
Jun Du
Keyword(s):  
Eastern China ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Ensemble ◽  
Ensemble Size ◽  
Detection Scheme ◽  
Ensemble Prediction System ◽  
Fog Forecasting ◽  
Probabilistic Forecasts

Abstract A new multivariable-based diagnostic fog-forecasting method has been developed at NCEP. The selection of these variables, their thresholds, and the influences on fog forecasting are discussed. With the inclusion of the algorithm in the model postprocessor, the fog forecast can now be provided centrally as direct NWP model guidance. The method can be easily adapted to other NWP models. Currently, knowledge of how well fog forecasts based on operational NWP models perform is lacking. To verify the new method and assess fog forecast skill, as well as to account for forecast uncertainty, this fog-forecasting algorithm is applied to a multimodel-based Mesoscale Ensemble Prediction System (MEPS). MEPS consists of 10 members using two regional models [the NCEP Nonhydrostatic Mesoscale Model (NMM) version of the Weather Research and Forecasting (WRF) model and the NCAR Advanced Research version of WRF (ARW)] with 15-km horizontal resolution. Each model has five members (one control and four perturbed members) using the breeding technique to perturb the initial conditions and was run once per day out to 36 h over eastern China for seven months (February–September 2008). Both deterministic and probabilistic forecasts were produced based on individual members, a one-model ensemble, and two-model ensembles. A case study and statistical verification, using both deterministic and probabilistic measuring scores, were performed against fog observations from 13 cities in eastern China. The verification was focused on the 12- and 36-h forecasts. By applying the various approaches, including the new fog detection scheme, ensemble technique, multimodel approach, and the increase in ensemble size, the fog forecast accuracy was steadily and dramatically improved in each of the approaches: from basically no skill at all [equitable threat score (ETS) = 0.063] to a skill level equivalent to that of warm-season precipitation forecasts of the current NWP models (0.334). Specifically, 1) the multivariable-based fog diagnostic method has a much higher detection capability than the liquid water content (LWC)-only based approach. Reasons why the multivariable approach works better than the LWC-only method were also illustrated. 2) The ensemble-based forecasts are, in general, superior to a single control forecast measured both deterministically and probabilistically. The case study also demonstrates that the ensemble approach could provide more societal value than a single forecast to end users, especially for low-probability significant events like fog. Deterministically, a forecast close to the ensemble median is particularly helpful. 3) The reliability of probabilistic forecasts can be effectively improved by using a multimodel ensemble instead of a single-model ensemble. For a small ensemble such as the one in this study, the increase in ensemble size is also important in improving probabilistic forecasts, although this effect is expected to decrease with the increase in ensemble size.

scholarly journals Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

2019 ◽  
Vol 32 (3) ◽  
pp. 957-972 ◽  
Author(s):  
Takeshi Doi ◽  
Swadhin K. Behera ◽  
Toshio Yamagata
Keyword(s):  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Economic Losses ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Operational Systems ◽  
The Indian Ocean ◽  
Climate Events

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

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 Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

2011 ◽  
Vol 11 (11) ◽  
pp. 30457-30485 ◽  
Author(s):  
P. Groenemeijer ◽  
G. C. Craig
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Ensemble Forecasting ◽  
Total Variance ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

scholarly journals Increasing the horizontal resolution of ensemble forecasts at CMC

2003 ◽  
Vol 10 (6) ◽  
pp. 463-468 ◽  
Author(s):  
G. Pellerin ◽  
L. Lefaivre ◽  
P. Houtekamer ◽  
C. Girard
Keyword(s):  
Operating Characteristic ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Ensemble Size ◽  
Ensemble Forecasts ◽  
Relative Operating Characteristic ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
Probability Of Precipitation ◽  
Model Approach

Abstract. Ensemble forecasts are run operationally since February 1998 at the Canadian Meteorological Centre, with outputs up to ten days. The ensemble size was increased from eight to sixteen members in August 1999. The method of producing the perturbed analyses consists of running independent assimilation cycles that use perturbed sets of observations and are driven by eight different models, mainly different in their physical parameterizations. Perturbed analyses are doubled by taking opposite pairs. A multi-model approach is then used to obtain the forecasts. The ensemble output has been used to generate several products. In view of increasing computing facilities, the ensemble prediction system horizontal resolution was increased to TL149 in June 2001. Heights at 500 hPa and mean sea-level pressure maps are regularly used. Charts of precipitation with the probability of precipitation being above various thresholds are also produced at each run. The probabilistic forecast of the 24-h accumulated precipitation has shown skill as demonstrated by the relative operating characteristic (ROC). Verifications of the ensemble forecasts will be presented.

scholarly journals Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

2009 ◽  
Vol 137 (4) ◽  
pp. 1480-1492 ◽  
Author(s):  
Frédéric Vitart ◽  
Franco Molteni
Keyword(s):  
Lead Time ◽  
Monsoon Rainfall ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Variable Resolution ◽  
Sensitivity Experiments ◽  
Extended Range ◽  
Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

2019 ◽  
Vol 34 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Jun Du ◽  
Xiefei Zhi ◽  
Jingzhuo Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

scholarly journals Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study

2006 ◽  
Vol 13 (1) ◽  
pp. 53-66 ◽  
Author(s):  
S. Federico ◽  
E. Avolio ◽  
C. Bellecci ◽  
M. Colacino ◽  
R. L. Walko
Keyword(s):  
Case Studies ◽  
Horizontal Resolution ◽  
Probabilistic Forecast ◽  
Computational Time ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Precipitation Forecasting ◽  
Regional Atmospheric Modelling System ◽  
The Impact

Abstract. This paper reports preliminary results for a Limited area model Ensemble Prediction System (LEPS), based on RAMS (Regional Atmospheric Modelling System), for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time in an operational implementation of LEPS, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that form the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12-km horizontal resolution. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecasts, LEPS forecasts are compared to a full Brute Force (BF) ensemble. This ensemble is based on RAMS, has 36 km horizontal resolution and is generated by 51 members, nested in each ECMWF-EPS member. LEPS and BF results are compared subjectively and by objective scores. Subjective analysis is based on precipitation and probability maps of case studies whereas objective analysis is made by deterministic and probabilistic scores. Scores and maps are calculated by comparing ensemble precipitation forecasts against reports from the Calabria regional raingauge network. Results show that LEPS provided better rainfall predictions than BF for all case studies selected. This strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria for these cases. To further explore the impact of local physiographic features on QPF (Quantitative Precipitation Forecasting), LEPS results are also compared with a 6-km horizontal resolution deterministic forecast. Due to local and mesoscale forcing, the high resolution forecast (Hi-Res) has better performance compared to the ensemble mean for rainfall thresholds larger than 10mm but it tends to overestimate precipitation for lower amounts. This yields larger false alarms that have a detrimental effect on objective scores for lower thresholds. To exploit the advantages of a probabilistic forecast compared to a deterministic one, the relation between the ECMWF-EPS 700 hPa geopotential height spread and LEPS performance is analyzed. Results are promising even if additional studies are required.

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.

Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System

1998 ◽  
Vol 124 (550) ◽  
pp. 1935-1960 ◽  
Author(s):  
R. Buizza ◽  
T. Petroliagis ◽  
T. Palmer ◽  
J. Barkmeijer ◽  
M. Hamrud ◽  
...  
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Model Resolution ◽  
Ensemble Size ◽  
Ensemble Prediction System
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