Performance evaluation of sub-daily ensemble precipitation forecasts
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This article is published in Meteorological Applications.The article was published on 2020-01-01 and is currently open access. It has received 15 citations till now. The article focuses on the topics: Thesaurus (information retrieval).read more
Citations
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Bias correction of global ensemble precipitation forecasts by Random Forest method
TL;DR: In this article, the ensemble precipitation forecasts of six numerical models from the TIGGE (THORPEX Interactive Grand Global Ensemble) database, associated with four basins in Iran for 2008-2018, were extracted and bias-corrected by the Quantile Mapping (QM) and Random Forest (RF) methods.
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How reliable are TIGGE daily deterministic precipitation forecasts over different climate and topographic conditions of Iran
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The adaptability of typical precipitation ensemble prediction systems in the Huaihe River basin, China
TL;DR: In this paper, the authors evaluated the performance of five typical operational global ensemble prediction systems (EPSs) from TIGGE (i.e., The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble) and the observed daily precipitation data of 40 meteorological stations over the Huaihe River basin (HB).
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Evaluation of quantitative precipitation forecast in five Indian river basins
TL;DR: In this paper, the performance of a deterministic deterministic model for streamflow forecasting was evaluated using the QPF obtained from a Numerical Weather Prediction model, which is used for stream flow forecasting.
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Adaptive precipitation nowcasting using deep learning and ensemble modeling
TL;DR: In this article , six DNNs and three corrected numerical weather prediction (NWP) models are used for adaptive rainfall nowcasting in the eastern drainage catchment of Tehran city in Iran.
References
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Statistical Methods in the Atmospheric Sciences
TL;DR: In this article, statistical methods in the Atmospheric Sciences are used to estimate the probability of a given event to be a hurricane or tropical cyclone, and the probability is determined by statistical methods.
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Using Bayesian Model Averaging to Calibrate Forecast Ensembles
TL;DR: The authors proposed a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources, and demonstrated that BMA performs reasonably well when the underlying ensemble is calibrated, or even overdispersed.
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Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging
TL;DR: In this article, the predictive probability density functions (PDFs) for weather quantities are represented as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts' relative contributions to predictive skill over a training period.
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The TIGGE Project and Its Achievements
Richard Swinbank,Masayuki Kyouda,Piers Buchanan,Lizzie S. R. Froude,Thomas M. Hamill,Tim Hewson,Julia H. Keller,Mio Matsueda,John Methven,Florian Pappenberger,Michael Scheuerer,Helen Titley,Laurence J. Wilson,Munehiko Yamaguchi +13 more
TL;DR: It is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble for a range of forecast parameters, and alternative methods to correct systematic errors are discussed.
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Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa
TL;DR: In this paper, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1-5-day accumulated precipitation based on the monsoon seasons during 2007-14 for three regions within northern tropical Africa.