BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts

Liu, Jianguo; Xie, Zhenghui

doi:10.1175/mwr-d-13-00031.1

Cited by 38 publications

(20 citation statements)

References 29 publications

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“…Various approaches have been devised for combining the ensemble forecasts of several models, the BMA being one of the most widely used. For example, the construction of a grand ensemble using the BMA via combining the UKMO, NCEP, ECMWF and CMA models showed that the UKMO and ECMWF had greater skills, while the grand ensemble of the four centres was recommended for heavy precipitation forecast in the study region (Liu and Xie, 2014). Qu et al (2017) constructed a BMA grand ensemble, and through a distributed hydrologic model, they forecasted river discharge associated with 24-120 hr lead times and concluded that the BMA post-processing resulted in more reliable probabilistic discharge forecast compared with relying on raw ensemble numerical precipitation forecasts.…”

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confidence: 99%

Performance evaluation of sub‐daily ensemble precipitation forecasts

Saedi

Saghafian

Moazami

et al. 2020

Meteorological Applications

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Nowadays, major advances have been made in meteorological forecasts. For instance, ensemble forecast systems have been developed to quantify prediction uncertainty. In this research, sub‐daily ensemble precipitation forecasts of five THORPEX Interactive Grand Global Ensemble (TIGGE) models from 2014 to 2018 were evaluated in 10 major basins located in north and west Iran. Furthermore, Bayesian model averaging (BMA) was used to combine five prediction models and construct a grand ensemble. The results indicate that the models had the best performance in the Karun and Western Border basins to the southwest and west, with average performance in the Sefidrood basin in the north. In terms of the prediction of precipitation depth, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the UK Met Office (UKMO) models, and in terms of prediction of precipitation occurrence and non‐occurrence, the National Centers for Environmental Prediction (NCEP) model, performed best. Overall, the Japan Meteorological Agency (JMA) and the China Meteorological Administration (CMA) models acquired medium scores. The BMA technique greatly improved the probability forecasts, reducing uncertainties in the numerical models. Moreover, the models’ forecasts were weaker with a 6 hr lead time compared with those with 24 hr, which may be attributable to the inaccurate detection of the initiation time of precipitation by the models. In addition, the performance of the UKMO (ECMWF) models with increasing basin elevation increased (decreased), while all models better forecasted precipitation in wet years/seasons than they did in dry years/seasons. Overall, the evaluations showed that the ECMWF, UKMO and NCEP models performed well in the majority of the northern and western basins of Iran.

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confidence: 99%

Performance evaluation of sub‐daily ensemble precipitation forecasts

Saedi

Saghafian

Moazami

et al. 2020

Meteorological Applications

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“…The BMA approach can produce calibrated and sharply predictive PDFs from the ensembles of dynamic models and provide probability forecasting, as pointed out by Liu and Xie (2014) and Li and Lin (2015). In future work, we intend to extend the BMA TWSA simulation to probability prediction, and investigate the warning schemes of extreme events such as floods and droughts, based on the BMA TWSA prediction.…”

Section: Discussionmentioning

confidence: 99%

Improving the simulation of terrestrial water storage anomalies over China using a Bayesian model averaging ensemble approach

Liu

Jia

Xie

et al. 2018

Atmospheric and Oceanic Science Letters

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The ability to estimate terrestrial water storage (TWS) is essential for monitoring hydrological extremes (e.g., droughts and floods) and predicting future changes in the hydrological cycle. However, inadequacies in model physics and parameters, as well as uncertainties in meteorological forcing data, commonly limit the ability of land surface models (LSMs) to accurately simulate TWS. In this study, the authors show how simulations of TWS anomalies (TWSAs) from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging (BMA) ensemble approach to improve monitoring and predictions. Simulations using three forcing datasets and two LSMs were conducted over mainland China for the period 1979-2008. All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004-08. The correlation coefficient ranged between 0.5 and 0.8 in the humid regions (e.g., the Yangtze river basin, Huaihe basin, and Zhujiang basin), but was much lower in the arid regions (e.g., the Heihe basin and Tarim river basin). The BMA ensemble approach performed better than all individual member simulations. It captured the spatial distribution and temporal variations of TWSAs over mainland China and the eight major river basins very well; plus, it showed the highest R value (> 0.5) over most basins and the lowest root-mean-square error value (< 40 mm) in all basins of China. The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term, high-resolution spatial and temporal TWSA data.

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“…Some research achievements, such as the extreme flood warning scheme, may not be suitable in practice until similarly regular patterns are found in additional studies [19]. Table 1 shows that the TIGGE ensemble forecasts provide an opportunity to allow lead times of up to 240 h, yet only the first 120 h were used in the study.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the EM algorithm was selected, on account of its high computation efficiency. Note that for the BMA model, there is an implicit assumption that lead times are completely independent, that is to say, that the BMA parameters and weights for each lead time are totally irrelevant and need to be estimated separately [19,22,32].…”

Section: Bayesian Model Averaging (Bma) Modelmentioning

confidence: 99%

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Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

Zhang

Pappenberger

et al. 2017

Water

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Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members), using seven different "THORPEX Interactive Grand Global Ensemble" (TIGGE) weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs), over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.

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BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts

Cited by 38 publications

References 29 publications

Performance evaluation of sub‐daily ensemble precipitation forecasts

Performance evaluation of sub‐daily ensemble precipitation forecasts

Improving the simulation of terrestrial water storage anomalies over China using a Bayesian model averaging ensemble approach

Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

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