The THORPEX Interactive Grand Global Ensemble

Bougeault, Philippe; Tóth, Zoltán; Bishop, Craig H.; Brown, Barbara G.; Burridge, D. M.; Chen, De Hui; Ebert, Barbara; Fuentes, M.; Hamill, Thomas M.; Mylne, Ken; Nicolau, Jean; Paccagnella, Tiziana; Park, Young-Youn; Parsons, David B.; Raoult, Baudouin; Schuster, Doug; Dias, Pedro Silva; Swinbank, Richard; Takeuchi, Yoshinori; Tennant, Warren; Wilson, Laurence J.; Worley, Steve

doi:10.1175/2010bams2853.1

Cited by 382 publications

(268 citation statements)

References 7 publications

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“…The first forecast data set consists in four NWP ensembles from the TIGGE project (Bougeault et al 2010). Ten-meter high wind speed forecasts have been extracted from four operational ensemble models issued by meteorological forecast services: the US National Centers for Environmental Prediction (NCEP), the Canadian Meteorological Center (CMC), the European Center for medium-range weather forecasts (ECMWF) and Météo-France (MF).…”

Section: Raw and Calibrated Ensemble Forecast Data Setsmentioning

confidence: 99%

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Zamo

Naveau

2017

Math Geosci

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The continuous ranked probability score (CRPS) is a much used measure of performance for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference between the forecast cumulative distribution function (CDF) and the empirical CDF of the observation. Analytic formulations of the CRPS can be derived for most classical parametric distributions, and be used to assess the efficiency of different CRPS estimators. When the true forecast CDF is not fully known, but represented as an ensemble of values, the CRPS is estimated with some error. Thus, using the CRPS to compare parametric probabilistic forecasts with ensemble forecasts may be misleading due to the unknown error of the estimated CRPS for the ensemble. With simulated data, the impact of the type of the verified ensemble (a random sample or a set of quantiles) on the CRPS estimation is studied. Based on these simulations, recommendations are issued to choose the most accurate CRPS estimator according to the type of ensemble. The interest of these recommendations is illustrated with real ensemble weather forecasts. Also, relationships between several estimators of the CRPS are demonstrated and used to explain the differences of accuracy between the estimators.

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Section: Raw and Calibrated Ensemble Forecast Data Setsmentioning

confidence: 99%

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Zamo

Naveau

2017

Math Geosci

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“…The data of this assessment is obtained from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) project portals (Bougeault et al 2010). In comparison to the NWP of NOAA and CPTEC, the ECMWF forecast has the best skill for the Três Marias river basin (Fan et al 2014).…”

Section: Ensemble Forecast Datamentioning

confidence: 99%

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Schwanenberg

Fan

Naumann

et al. 2015

Water Resour Manage

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State-of-the-art applications of short-term reservoir management integrate several advanced components, namely hydrological modelling and data assimilation techniques for predicting streamflow, optimization-based techniques for decision-making on the reservoir operation and the technical framework for integrating these components with data feeds from gauging networks, remote sensing data and meteorological weather predictions. In this paper, we present such a framework for the short-term management of reservoirs operated by the a drainage area of approximately 55,000 km and its operation for flood mitigation. Basis for the anticipatory short-term management of the reservoir over a forecast horizon of up to 15 days are streamflow predictions of the MGB hydrological model. The semi-distributed model is well suited to represent the watershed and shows a Nash-Sutcliffe model performance in the order of 0.83-0.90 for most streamflow gauges of the data-sparse basin. A lead time performance assessment of the deterministic and probabilistic ECMWF forecasts as model forcing indicate the superiority of the probabilistic model. The novel short-term optimization approach consists of the reduction of the ensemble forecasts into scenario trees as an input of a multi-stage stochastic optimization. We show that this approach has several advantages over commonly used deterministic methods which neglect forecast uncertainty in the short-term decision-making. First, the probabilistic forecasts have longer forecast horizons that allow an earlier and therefore better anticipation of critical flood events. Second, the stochastic optimization leads to more robust decisions than deterministic procedures which consider only a single future trajectory. Third, the stochastic optimization permits to introduce advanced chance constraints for refining the system operation.

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“…The THORPEX Interactive Grand Global Ensemble (TIGGE; Bougeault et al, 2010) was recently established as a pathway towards establishing a Global Interactive Forecast System. Currently, ensemble forecasts from several operational global models are archived in a common format on servers such as http://tigge.ecmwf.int/ and are freely available to researchers.…”

Section: Tigge Ensemblesmentioning

confidence: 99%

Characteristics of Ensemble Transform Kalman Filter adaptive sampling guidance for tropical cyclones

Majumdar

Chen

2011

Quart J Royal Meteoro Soc

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The Ensemble Transform Kalman Filter (ETKF) is one candidate strategy for targeting observations around tropical cyclones. The characteristics of target areas identified by a revised version of the ETKF are investigated using the THORPEX Interactive Grand Global Ensemble. In order to emphasize sensitivity to the environmental flow, the axisymmetric circulation associated with the tropical cyclone is removed from each ensemble member. The guidance is found to differ markedly from the ensemble variance, and the guidance products based on ensembles from different operational centres often disagree. For the pre-recurvature stage of typhoon Sinlaku (2008), the targets correspond to asymmetries close to the typhoon, neighbouring features including the adjacent subtropical ridge and a nearby upperlevel trough, and far-field features including the monsoon trough and areas of flow convergence. During and after recurvature, targets exist in the midlatitude jet upstream and downstream of the typhoon. The midlatitude targets are discernible for forecasts beyond 3 days. The sensitivity to hypothetical observations of wind is more pronounced than those of temperature or specific humidity. The targets based on upper-level observations correspond to the outflow of the tropical cyclone and nearby and remote jets, while the lower-level targets correspond to confluent flow near the cyclone. Multiple remote targets are evident, particularly for longer forecast times, and some of these may be spurious. When ensemble forecasts initialized at different times are used for the same case, the characteristics of the guidance differ, although not dramatically. Future priorities include the evaluation of the effects of assimilating observations sampled in target areas versus those in non-target areas, and the improvement of the data assimilation methodology assumed in the ETKF.

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The THORPEX Interactive Grand Global Ensemble

Cited by 382 publications

References 7 publications

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Characteristics of Ensemble Transform Kalman Filter adaptive sampling guidance for tropical cyclones

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