scholarly journals DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)

2004 ◽  
Vol 85 (6) ◽  
pp. 853-872 ◽  
Author(s):  
T. N. Palmer ◽  
A. Alessandri ◽  
U. Andersen ◽  
P. Cantelaube ◽  
M. Davey ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Economic Value ◽  
Ensemble Prediction ◽  
Model Ensemble ◽  
Single Model ◽  
Multimodel Ensemble ◽  
Coupled Models ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Climate Simulations

A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere–ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole.

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 An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

2009 ◽  
Vol 6 (4) ◽  
pp. 4891-4917
Author(s):  
J. A. Velázquez ◽  
T. Petit ◽  
A. Lavoie ◽  
M.-A. Boucher ◽  
R. Turcotte ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast Horizon ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydrological Forecasting ◽  
Updating Procedure ◽  
First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

scholarly journals An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

2009 ◽  
Vol 13 (11) ◽  
pp. 2221-2231 ◽  
Author(s):  
J. A. Velázquez ◽  
T. Petit ◽  
A. Lavoie ◽  
M.-A. Boucher ◽  
R. Turcotte ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast Horizon ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydrological Forecasting ◽  
Updating Procedure ◽  
First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

scholarly journals Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels?

2009 ◽  
Vol 137 (4) ◽  
pp. 1460-1479 ◽  
Author(s):  
Andreas P. Weigel ◽  
Mark A. Liniger ◽  
Christof Appenzeller
Keyword(s):  
Ensemble Prediction ◽  
Model Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
Probabilistic Forecasts ◽  
The Difference

Abstract Multimodel ensemble combination (MMEC) has become an accepted technique to improve probabilistic forecasts from short- to long-range time scales. MMEC techniques typically widen ensemble spread, thus improving the dispersion characteristics and the reliability of the forecasts. This raises the question as to whether the same effect could be achieved in a potentially cheaper way by rescaling single model ensemble forecasts a posteriori such that they become reliable. In this study a climate conserving recalibration (CCR) technique is derived and compared with MMEC. With a simple stochastic toy model it is shown that both CCR and MMEC successfully improve forecast reliability. The difference between these two methods is that CCR conserves resolution but inevitably dilutes the potentially predictable signal while MMEC is in the ideal case able to fully retain the predictable signal and to improve resolution. Therefore, MMEC is conceptually to be preferred, particularly since the effect of CCR depends on the length of the data record and on distributional assumptions. In reality, however, multimodels consist only of a finite number of participating single models, and the model errors are often correlated. Under such conditions, and depending on the skill metric applied, CCR-corrected single models can on average have comparable skill as multimodel ensembles, particularly when the potential model predictability is low. Using seasonal near-surface temperature and precipitation forecasts of three models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset, it is shown that the conclusions drawn from the toy-model experiments hold equally in a real multimodel ensemble prediction system. All in all, it is not possible to make a general statement on whether CCR or MMEC is the better method. Rather it seems that optimum forecasts can be obtained by a combination of both methods, but only if first MMEC and then CCR is applied. The opposite order—first CCR, then MMEC—is shown to be of only little effect, at least in the context of seasonal forecasts.

scholarly journals Multiconvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies

2005 ◽  
Vol 18 (15) ◽  
pp. 2963-2978 ◽  
Author(s):  
T. E. LaRow ◽  
S. D. Cocke ◽  
D. W. Shin
Keyword(s):  
Initial Conditions ◽  
Coupled Model ◽  
Skill Score ◽  
Model Ensemble ◽  
Single Model ◽  
Multimodel Ensemble ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Climate Studies ◽  
Start Dates

Abstract A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.

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.

scholarly journals Comparing different meteorological ensemble approaches: hydrological predictions for a flood episode in Northern Italy

2012 ◽  
Vol 8 (1) ◽  
pp. 33-37 ◽  
Author(s):  
S. Davolio ◽  
T. Diomede ◽  
C. Marsigli ◽  
M. M. Miglietta ◽  
A. Montani ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Single Case ◽  
Northern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Model Ensemble ◽  
Single Model ◽  
Ensemble Prediction System ◽  
Model Approach

Abstract. Within the framework of coupled meteorological-hydrological predictions, this study aims at comparing two high-resolution meteorological ensembles, covering short and medium range. The two modelling systems have similar characteristics, as almost the same number of members, the model resolution (about 7 km), the driving ECMWF global ensemble prediction system, but are obtained through different methodologies: the former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter follows a single-model approach, based on COSMO-LEPS (Limited-area Ensemble Prediction System), the operational ensemble forecasting system developed within the COSMO consortium. Precipitation forecasts are evaluated in terms of hydrological response, after coupling the meteorological models with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno river (Northern Italy), for a severe weather episode. Although a single case study does not allow for robust and definite conclusions, the comparison among different predictions points out a remarkably better performance of mesoscale model ensemble forecasts compared to global ones. Moreover, the multi-model ensemble outperforms the single model approach.

scholarly journals Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble Forecasts

2012 ◽  
Vol 27 (4) ◽  
pp. 972-987 ◽  
Author(s):  
Yong Wang ◽  
Simona Tascu ◽  
Florian Weidle ◽  
Karin Schmeisser
Keyword(s):  
Horizontal Resolution ◽  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Initial Condition ◽  
Weather Variables ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Model Based ◽  
Surface Weather

Abstract The regional single-model-based Aire Limitée Adaptation Dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble prediction system (EPS) is evaluated and compared with the global ECMWF-EPS to investigate the added value of regional to global EPS models. ALADIN-LAEF consists of 16 perturbed members at 18-km horizontal resolution, while ECMWF-EPS includes 50 perturbed members at 50-km horizontal resolution. In ALADIN-LAEF, the atmospheric initial condition uncertainty is quantified by using blending, which combines large-scale uncertainty generated by the ECMWF-EPS singular-vector approach with small-scale perturbations resolved by the ALADIN breeding technique. The surface initial condition perturbations are generated by use of the noncycling surface breeding (NCSB) technique, and different physics schemes are employed for different forecast members to account for model uncertainties. The verification and comparison have been carried out for a 2-month period during summer 2007 over central Europe. The results show a quite favorable level of performance for ALADIN-LAEF compared to ECMWF-EPS for surface weather variables. ALADIN-LAEF adds more value to precipitation forecasts and has greater skill for 10-m wind and mean sea level pressure results than does ECMWF-EPS. For 2-m temperature, ALADIN-LAEF forecasts have larger spread, are statistically more consistent, but also have less skill than ECMWF-EPS due to the strong cold bias in the ALADIN forecasts. For the upper-air weather parameters, the forecast of ALADIN-LAEF has a larger spread, but the forecast skill of ALADIN-LAEF is from neutral to slightly inferior compared to ECMWF-EPS. It may be concluded that a regional single-model-based EPS with fewer ensemble members could provide more added value in terms of greater skill for near-surface weather variables than the global EPS with larger ensemble size, whereas it may have limitations when applied to upper-air weather variables.

scholarly journals Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression

2018 ◽  
Vol 31 (20) ◽  
pp. 8573-8588 ◽  
Author(s):  
Matz A. Haugen ◽  
Michael L. Stein ◽  
Elisabeth J. Moyer ◽  
Ryan L. Sriver
Keyword(s):  
Quantile Regression ◽  
Radiative Forcing ◽  
Initial Conditions ◽  
Model Simulation ◽  
Extreme Temperature ◽  
Single Model ◽  
New Approach ◽  
Temperature Distributions ◽  
Climate Simulations ◽  
Community Earth System Model

Understanding future changes in extreme temperature events in a transient climate is inherently challenging. A single model simulation is generally insufficient to characterize the statistical properties of the evolving climate, but ensembles of repeated simulations with different initial conditions greatly expand the amount of data available. We present here a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes. Specifically, our approach uses a continuous representation of seasonality rather than breaking the dataset into seasonal blocks; that is, we assume that temperature distributions evolve smoothly both day to day over an annual cycle and year to year over longer secular trends. To demonstrate our method’s utility, we analyze an ensemble of 50 simulations of the Community Earth System Model (CESM) under a scenario of increasing radiative forcing to 2100, focusing on North America. As previous studies have found, we see that daily temperature bulk variability generally decreases in wintertime in the continental mid- and high latitudes (>40°). A more subtle result that our approach uncovers is that differences in two low quantiles of wintertime temperatures do not shrink as much as the rest of the temperature distribution, producing a more negative skew in the overall distribution. Although the examples above concern temperature only, the technique is sufficiently general that it can be used to generate precise estimates of distributional changes in a broad range of climate variables by exploiting the power of ensembles.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

Sign in / Sign up

Export Citation Format

Share Document