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Ensemble Spread (ensemble + spread)
Selected AbstractsThe local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction systemTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 640 2009Neill E. Bowler Abstract The Met Office has been routinely running a short-range global and regional ensemble prediction system (EPS) since the summer of 2005. This article describes a major upgrade to the global ensemble, which affected both the initial condition and model uncertainty perturbations applied in that ensemble. The change to the initial condition perturbations is to allow localization within the ensemble transform Kalman filter (ETKF). This enables better specification of the ensemble spread as a function of location around the globe. The change to the model uncertainty perturbations is the addition of a stochastic kinetic energy backscatter scheme (SKEB). This adds vorticity perturbations to the forecast in order to counteract the damping of small-scale features introduced by the semi-Lagrangian advection scheme. Verification of ensemble forecasts is presented for the global ensemble system. It is shown that the localization of the ETKF gives a distribution of the spread as a function of latitude that better matches the forecast error of the ensemble mean. The SKEB scheme has a substantial effect on the power spectrum of the kinetic energy, and with the scheme a shallowing of the spectral slope is seen in the tail. A k,5/3 slope is seen at wavelengths shorter than 1000 km and this better agrees with the observed spectrum. The local ETKF significantly improves forecasts at all lead times over a number of variables. The SKEB scheme increases the rate of growth of ensemble spread in some variables, and improves forecast skill at short lead times. ©Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons Ltd. [source] Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 630 2008A. P. Weigel Abstract The success of multi-model ensemble combination has been demonstrated in many studies. Given that a multi-model contains information from all participating models, including the less skilful ones, the question remains as to why, and under what conditions, a multi-model can outperform the best participating single model. It is the aim of this paper to resolve this apparent paradox. The study is based on a synthetic forecast generator, allowing the generation of perfectly-calibrated single-model ensembles of any size and skill. Additionally, the degree of ensemble under-dispersion (or overconfidence) can be prescribed. Multi-model ensembles are then constructed from both weighted and unweighted averages of these single-model ensembles. Applying this toy model, we carry out systematic model-combination experiments. We evaluate how multi-model performance depends on the skill and overconfidence of the participating single models. It turns out that multi-model ensembles can indeed locally outperform a ,best-model' approach, but only if the single-model ensembles are overconfident. The reason is that multi-model combination reduces overconfidence, i.e. ensemble spread is widened while average ensemble-mean error is reduced. This implies a net gain in prediction skill, because probabilistic skill scores penalize overconfidence. Under these conditions, even the addition of an objectively-poor model can improve multi-model skill. It seems that simple ensemble inflation methods cannot yield the same skill improvement. Using seasonal near-surface temperature forecasts from the DEMETER dataset, we show that the conclusions drawn from the toy-model experiments hold equally in a real multi-model ensemble prediction system. Copyright © 2008 Royal Meteorological Society [source] Limited-area ensemble predictions at the Norwegian Meteorological InstituteTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 621 2006Inger-Lise Frogner Abstract This study aims at improving 0,3 day probabilistic forecasts of precipitation events in Norway. For this purpose a limited-area ensemble prediction system (LAMEPS) is tested. The horizontal resolution of LAMEPS is 28 km, and there are 31 levels in the vertical. The state variables provided as initial and lateral boundary conditions for the limited-area forecasts are perturbed using a dedicated version of the European Centre for Medium-Range Weather Forecasts (ECMWF) global ensemble prediction system, TEPS. These are constructed by combining initial and evolved singular vectors that at final time (48 h) are targeted to maximize the total energy in a domain containing northern Europe and adjacent sea areas. The resolution of TEPS is T255 with 40 levels. The test period includes 45 cases with 21 ensemble members in each case. We focus on 24 h accumulated precipitation rates with special emphasis on intense events. We also investigate a combination of TEPS and LAMEPS resulting in a system (NORLAMEPS) with 42 ensemble members. NORLAMEPS is compared with the 21-member LAMEPS and TEPS as well as the regular 51-member EPS run at ECMWF. The benefit of using targeted singular vectors is seen by comparing the 21-member TEPS with the 51-member operational EPS, as TEPS has considerably larger spread between ensemble members. For other measures, such as Brier Skill Score (BSS) and Relative Operating Characteristic (ROC) curves, the scores of the two systems are for most cases comparable, despite the difference in ensemble size. NORLAMEPS has the largest ensemble spread of all four ensemble systems studied in this paper, while EPS has the smallest spread. Nevertheless, EPS has higher BSS with NORLAMEPS approaching for the highest precipitation thresholds. For the area under the ROC curve, NORLAMEPS is comparable with or better than EPS for medium to large thresholds. Copyright © 2006 Royal Meteorological Society [source] High-resolution limited-area ensemble predictions based on low-resolution targeted singular vectorsTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 582 2002Inger-Lise Frogner Abstract The operational limited-area model, HIRLAM, at the Norwegian Meteorological Institute is used at 0.25° latitude/longitude resolution for ensemble weather prediction over Northern Europe and adjacent parts of the North Atlantic Ocean; this system is called LAMEPS. Initial and lateral boundary perturbations are taken from coarse-resolution European Centre for Medium-Range Weather Forecasts global ensemble members based on targeted singular vectors (TEPS). Five winter and five summer cases in 1997 consisting of 20 ensemble members plus one control forecast are integrated. Two sets of ensembles are generated, one for which both initial and lateral boundary conditions are perturbed, and another with only the initial fields perturbed. The LAMEPS results are compared to those of TEPS using the following measures: r.m.s. ensemble spread of 500 hPa geopotential height; r.m.s. ensemble spread of mean-sea-level pressure; Brier Skill Scores (BSS); Relative Operating Characteristic (ROC) curves; and cost/loss analyses. For forecasts longer than 12 hours, all measures show that perturbing the boundary fields is crucial for the performance of LAMEPS. For the winter cases TEPS has slightly larger ensemble spread than LAMEPS, but this is reversed for the summer cases. Results from BSS, ROC and cost/loss analyses show that LAMEPS performed considerably better than TEPS for precipitation, a result that is promising for forecasting extreme precipitation amounts. We believe this result to be linked to the high predictability of mesoscale flows controlled by complex topography. For two-metre temperature, however, TEPS frequently performed better than LAMEPS. Copyright © 2002 Royal Meteorological Society [source] Using ensemble forecasts to predict the size of forecast changes, with application to weather swap value at riskATMOSPHERIC SCIENCE LETTERS, Issue 1-4 2003Stephen Jewson We show that the standard deviation of the distribution from which changes in the ensemble mean are drawn can be predicted using the ensemble spread. Such a forecast has direct application for companies that trade weather swaps and need to evaluate their risk. Copyright © 2003 Royal Meteorological Society. Published by Elsevier Ltd. All rights reserved. [source] | ||||||||||