On Ensemble Prediction Using Singular Vectors Started from Forecasts

2005 ◽  
Vol 133 (10) ◽  
pp. 3038-3046 ◽  
Author(s):  
Martin Leutbecher
Keyword(s):  
Singular Vector ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Singular Vectors ◽  
The Tropics ◽  
The Impact ◽  
Very High ◽  
Tropical Cyclone Tracks

Abstract The impact on the ECMWF Ensemble Prediction System of using singular vectors computed from 12-h forecasts instead of analyses has been studied. Results are based on 34 cases in November–December 1999 and 28 cases in September 2003. The similarity between singular vectors started from a 12-h forecast and singular vectors started from an analysis is very high for the extratropical singular vectors in each of the 62 cases and for both hemispheres. Consistently, ensemble scores and spread measures show close to neutral impact on geopotential height in the extratropics. The sensitivity of the singular vectors to the choice of trajectory is larger in the Tropics than in the extratropics. However, the spread in tropical cyclone tracks is not significantly decreased if singular vectors computed from 12-h forecasts are used. The computation of singular vectors from forecasts could be used to disseminate the ensemble forecasts earlier or to allocate more resources to the nonlinear forecasts. Furthermore, it greatly facilitates the implementation of computationally more demanding configurations for the singular-vector-based initial perturbations.

scholarly journals Sensitivity of Global Ensemble Forecasts to the Initial Ensemble Mean and Perturbations: Comparison of EnKF, Singular Vector, and 4D-Var Approaches

2010 ◽  
Vol 138 (10) ◽  
pp. 3886-3904 ◽  
Author(s):  
Mark Buehner ◽  
Ahmed Mahidjiba
Keyword(s):  
Ensemble Prediction ◽  
Boreal Summer ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Var Analysis ◽  
Initial Ensemble ◽  
Ensemble Mean ◽  
The Tropics ◽  
The Impact

Abstract This study examines the sensitivity of global ensemble forecasts to the use of different approaches for specifying both the initial ensemble mean and perturbations. The current operational ensemble prediction system of the Meteorological Service of Canada uses the ensemble Kalman filter (EnKF) to define both the ensemble mean and perturbations. To evaluate the impact of different approaches for obtaining the initial ensemble perturbations, the operational EnKF approach is compared with using either no initial perturbations or perturbations obtained using singular vectors (SVs). The SVs are computed using the (dry) total-energy norm with a 48-h optimization time interval. Random linear combinations of 60 SVs are computed for each of three regions. Next, the impact of replacing the initial ensemble mean, currently the EnKF ensemble mean analysis, with the higher-resolution operational four-dimensional variational data assimilation (4D-Var) analysis is evaluated. For this comparison, perturbations are provided by the EnKF. All experiments are performed over two-month periods during both the boreal summer and winter using a system very similar to the global ensemble prediction system that became operational on 10 July 2007. Relative to the operational configuration that relies on the EnKF, the use of SVs to compute initial perturbations produces small, but statistically significant differences in probabilistic forecast scores in favor of the EnKF both in the tropics and, for a limited set of forecast lead times, in the summer hemisphere extratropics, whereas the results are very similar in the winter hemisphere extratropics. Both approaches lead to significantly better ensemble forecasts than with no initial perturbations, though results are quite similar in the tropics when using SVs and no perturbations. The use of an initial-time norm that does not include information on analysis uncertainty and the lack of linearized moist processes in the calculation of the SVs are two factors that limit the quality of the resulting SV-based ensemble forecasts. Relative to the operational configuration, use of the 4D-Var analysis to specify the initial ensemble mean results in improved probabilistic forecast scores during the boreal summer period in the southern extratropics and tropics, but a near-neutral impact otherwise.

scholarly journals Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System

2008 ◽  
Vol 136 (11) ◽  
pp. 4092-4104 ◽  
Author(s):  
Linus Magnusson ◽  
Martin Leutbecher ◽  
Erland Källén
Keyword(s):  
Singular Vector ◽  
Initial Perturbation ◽  
Error Variance ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Perturbation Energy ◽  
Analysis Error ◽  
The Tropics ◽  
Model Environment

Abstract In this paper a study aimed at comparing the perturbation methodologies based on the singular vector ensemble prediction system (SV-EPS) and the breeding vector ensemble prediction system (BV-EPS) in the same model environment is presented. A simple breeding system (simple BV-EPS) as well as one with regional rescaling dependent on an estimate of the analysis error variance (masked BV-EPS) were used. The ECMWF Integrated Forecast System has been used and the three experiments are compared for 46 forecast cases between 1 December 2005 and 15 January 2006. By studying the distribution of the perturbation energy it was possible to see large differences between the experiments initially, but after 48 h the distributions have converged. Using probabilistic scores, these results show that SV-EPS has a somewhat better performance for the Northern Hemisphere compared to BV-EPS. For the Southern Hemisphere masked BV-EPS and SV-EPS yield almost equal results. For the tropics the masked breeding ensemble shows the best performance during the first 6 days. One reason for this is the current setup of the singular vector ensemble at ECMWF yielding in general very low initial perturbation amplitudes in the tropics.

scholarly journals On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

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 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.

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 8 (2) ◽  
pp. 2739-2782 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Criterion Score ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

scholarly journals Evaluation of the Plant–Craig stochastic convection scheme in an ensemble forecasting system

2015 ◽  
Vol 8 (12) ◽  
pp. 10199-10236
Author(s):  
R. J. Keane ◽  
R. S. Plant ◽  
W. J. Tennant
Keyword(s):  
Random Parameter ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Convection Scheme ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Version 2.0 ◽  
Convection Parameterization ◽  
The Impact

Abstract. The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

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.

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