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Ensemble Members (ensemble + member)
Selected AbstractsTropical Pacific Ocean model error covariances from Monte Carlo simulationsTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 613 2005O. Alves Abstract As a first step towards the development of an Ensemble Kalman Filter (EnKF) for ocean data assimilation in the tropical oceans, this article investigates a novel technique for explicitly perturbing the model error in Monte Carlo simulations. The perturbation technique involves perturbing the surface zonal stress. Estimates of the characteristics of the wind stress errors were obtained from the difference between zonal wind fields from the NCEP and ECMWF re-analyses. In order to create random zonal wind stress perturbations, an EOF analysis was performed on the intraseasonally time-filtered difference between the two re-analysis products. The first 50 EOFs were retained and random wind stress fields for each ensemble member were created by combining random amounts of each EOF. Ensemble runs were performed using a shallow-water model, with both short forecasts and long simulations. Results show covariance patterns characteristic of Kelvin wave and Rossby wave dynamics. There are interesting differences between covariances using short forecasts and those using long simulations. The use of the long simulations produced non-local covariances (e.g. negative covariances between east and west Pacific), whereas short forecasts produced covariances that were localized by the time it takes Kevin and Rossby waves to travel over the forecast period and the scales of spatial covariance in the wind stress errors. The ensembles of short forecasts produced covariances and cross-covariances that can be explained by the dynamics of equatorial Rossby and Kevin waves forced by wind stress errors. The results suggest that the ensemble generation technique to explicitly represent the model error term can be used in an EnKF. Copyright © 2005 Royal Meteorological Society [source] An example of the dependence of the transient climate response on the temperature of the modelled climate stateATMOSPHERIC SCIENCE LETTERS, Issue 1 2009Chris M. Brierley Abstract The range in absolute global mean surface temperature projected with a small, perturbed ocean physics ensemble reduces as the levels of CO2 increase. The initial temperature state of an ensemble member is correlated to the amount of global warming seen in that member. The correlation arises, in approximately equal amounts, by variations in the ocean heat uptake within the ensemble and a dependency of the strength of the atmosphere,surface climate feedbacks on the initial climate. This relationship provides a clear warning that some uncertainty in global change projections derives from the simulation of the mean state. Copyright © 2008 Royal Meteorological Society and Crown Copyright [source] Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspectiveINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 11 2009Willem A. Landman Abstract The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied. Copyright © 2008 Royal Meteorological Society [source] A framework for developing high-resolution multi-model climate projections: 21st century scenarios for the UKINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 7 2008Jean-Philippe Vidal Abstract This article proposes a framework for building climate projections from an ensemble of global circulation models (GCMs) at the local scale required for impact studies. The proposed method relies on a fine-scale gridded baseline climatology and consists of the following steps: (1) building appropriate precipitation and temperature time series from land areas covered by GCM sea cells; (2) correction of GCM outputs inherent biases through ,quantile-based mapping'; and (3) disaggregation of bias-corrected outputs with monthly spatial anomalies between GCM-specific and observed spatial scales. The overall framework is applied to derive 21st century seasonal climate projections and inter-annual variability for the UK based on an ensemble of six GCMs run under two different emissions scenarios. Results show a large dispersion of changes within the multi-GCM ensemble, along with a good comparison between scenarios from individual ensemble members and from previous UK and European studies using dynamically downscaled outputs from corresponding GCMs. The framework presented in this article provides appropriate outputs to take account of the uncertainty in global model configuration within impacts studies that are influencing current decisions on major investments in flood risk management and water resources. Copyright © 2007 Royal Meteorological Society [source] A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2009Paul J. Block Abstract:, Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974-1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi-model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast. [source] Adaptive ensemble reduction and inflationTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 626 2007B. Uzunoglu Abstract In this paper we address the question of whether it is possible consistently to reduce the number of ensemble members at a late stage in the assimilation cycle. As an extension, we consider the question: given this reduction, is it possible to reintroduce ensemble members at a later time, if the accuracy is decreasing significantly? To address these questions, we present an adaptive methodology for reducing and inflating an ensemble by projecting the ensemble onto a limited number of its leading empirical orthogonal functions, through a proper orthogonal decomposition. We then apply this methodology with a global shallow-water-equations model on the sphere in conjunction with an ensemble filter developed at Florida State University and the Cooperative Institute for Research in the Atmosphere at Colorado State University. An adaptive methodology for reducing and inflating ensembles is successfully applied in two contrasting test cases with the shallow-water-equations model. It typically results in a reduction in the number of ensemble members required for successful implementation, by a factor of up to two. Copyright © 2007 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] Probabilistic forecasting from ensemble prediction systems: Improving upon the best-member method by using a different weight and dressing kernel for each memberTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 617 2006Vincent Fortin Abstract Ensembles of meteorological forecasts can both provide more accurate long-term forecasts and help assess the uncertainty of these forecasts. No single method has however emerged to obtain large numbers of equiprobable scenarios from such ensembles. A simple resampling scheme, the ,best member' method, has recently been proposed to this effect: individual members of an ensemble are ,dressed' with error patterns drawn from a database of past errors made by the ,best' member of the ensemble at each time step. It has been shown that the best-member method can lead to both underdispersive and overdispersive ensembles. The error patterns can be rescaled so as to obtain ensembles which display the desired variance. However, this approach fails in cases where the undressed ensemble members are already overdispersive. Furthermore, we show in this paper that it can also lead to an overestimation of the probability of extreme events. We propose to overcome both difficulties by dressing and weighting each member differently, using a different error distribution for each order statistic of the ensemble. We show on a synthetic example and using an operational ensemble prediction system that this new method leads to improved probabilistic forecasts, when the undressed ensemble members are both underdispersive and overdispersive. Copyright © 2006 Royal Meteorological Society. [source] Investigating atmospheric predictability on Mars using breeding vectors in a general-circulation modelTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 603 2004C. E. Newman Abstract A breeding vectors approach is used to investigate the hypothesis that the Martian atmosphere is predictable at certain times of year, by identifying the fastest-growing modes of instability at different times in a Mars general-circulation model. Results indicate that the period from northern mid-spring until mid-autumn is remarkably predictable, with negative global growth rates for a range of conditions, in contrast to the situation on the earth. From northern late autumn to early spring growing modes do occur, peaking in northern high latitudes and near winter solstice. Reducing the size of the initial perturbations increases global growth rates in most cases, supporting the idea that instabilities which saturate nonlinearly at lower amplitudes have generally faster growth rates. In late autumn/early winter the fastest-growing modes (,bred vectors') are around the north pole, increase with dust loading, and probably grow via barotropic as well as baroclinic energy conversion. In northern late winter/early spring the bred vectors are around the north pole and are strongly baroclinic in nature. As dust loading (and with it the global circulation strength) is increased their growth rates first decrease, as the baroclinic mode is suppressed, then increase again as the fastest-growing instabilities switch to being those which dominated earlier in the year. If dust levels are very low during late northern autumn (late southern spring) then baroclinic modes are also found around the spring pole in the south, though for a slight increase in dust loading the dominant modes shift back to northern high latitudes. The bred vectors are also used as perturbations to the initial conditions for ensemble simulations. One possible application within the Mars model is as a means of identifying regions and times when dust-lifting activity (related to surface wind stress) might show significant interannual variability for a given model configuration, without the need to perform long, computationally expensive multi-year model runs with each new set-up. This is tested for a time of year when previous multi-year experiments showed significant variability in dust storm onset in the region north of Chryse. Despite the model having no feedbacks between dust lifting and atmospheric state (unlike the original multi-year run), the ensemble members still show maximum divergence in this region in terms of near-surface wind stress, suggesting both that this application deserves further testing, and that the intrinsic atmospheric variability alone may be important in producing interannual variability in this storm type. Copyright © 2004 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] Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble sizeTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 577 2001David S. Richardson Abstract Ensemble forecasts provide probabilistic predictions for the future state of the atmosphere. Usually the probability of a given event E is determined from the fraction of ensemble members which predict the event. Hence there is a degree of sampling error inherent in the predictions. In this paper a theoretical study is made of the effect of ensemble size on forecast performance as measured by a reliability diagram and Brier (skill) score, and on users by using a simple cost-loss decision model. The relationship between skill and value, and a generalized skill score, dependent on the distribution of users, are discussed. The Brier skill score is reduced from its potential level for all finite-sized ensembles. The impact is most significant for small ensembles, especially when the variance of forecast probabilities is also small. The Brier score for a set of deterministic forecasts is a measure of potential predictability, assuming the forecasts are representative selections from a reliable ensemble prediction system (EPS). There is a consistent effect of finite ensemble size on the reliability diagram. Even if the underlying distribution is perfectly reliable, sampling this using only a small number of ensemble members introduces considerable unreliability. There is a consistent over-forecasting which appears as a clockwise tilt of the reliability diagram. It is important to be aware of the expected effect of ensemble size to avoid misinterpreting results. An ensemble of ten or so members should not be expected to provide reliable probability forecasts. Equally, when comparing the performance of different ensemble systems, any difference in ensemble size should be considered before attributing performance differences to other differences between the systems. The usefulness of an EPS to individual users cannot be deduced from the Brier skill score (nor even directly from the reliability diagram). An EPS with minimal Brier skill may nevertheless be of substantial value to some users, while small differences in skill may hide substantial variation in value. Using a simple cost-loss decision model, the sensitivity of users to differences in ensemble size is shown to depend on the predictability and frequency of the event and on the cost-loss ratio of the user. For an extreme event with low predictability, users with low cost-loss ratio will gain significant benefits from increasing ensemble size from 50 to 100 members, with potential for substantial additional value from further increases in number of members. This sensitivity to large ensemble size is not evident in the Brier skill score. A generalized skill score, dependent on the distribution of users, allows a summary performance measure to be tuned to a particular aspect of EPS performance. [source] | ||||||||||