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Response of Growing Season Gross Primary Production to El Nino in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on bayesian model averaging

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Advances in Atmospheric Sciences 2021年第9期38卷 1580-1595页

作者： Yueyue LI Li DAN Jing PENG Junbang WANG Fuqiang YANG Dongdong GAO Xiujing YANG Qiang YU Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of ScienceBeijing 100049China Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National Ecosystem Science Data Center Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F UniversityShaanxi 712100China School of Life Sciences University of Technology SydneySydney 2007NSWAustralia

Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial model Intercomparison Project using bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two *** consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this *** previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically *** paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.

关键词： East China bayesian model averaging Gross primary production El Nino Pacific Decadal Oscillation Monsoon rainfall

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Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive models and bayesian model averaging

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Acta meteorologica Sinica 2012年第1期26卷 1-12页

作者：杨赤严中伟邵月红 College of Global Change and Earth System Science Beijing Normal University Key Laboratory of Regional Climate-Environment Research for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

关键词： bayesian model averaging generalized additive model probabilistic precipitation forecasting,TIGGE Tweedie distribution

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

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Atmospheric and Oceanic Science Letters 2018年第4期11卷 322-329页

作者： LIU Jian-Guo JIA Bing-Hao XIE Zheng-Hui SHI Chun-Xiang School of Mathematics and Computational Science and Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University State Key Laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics Chinese Academy of Sciences National Meteorological Information Center China Meteorological Administration

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 ***,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 *** 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 *** using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–*** the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–*** 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 *** captured the spatial distribution and temporal variations of TWSAs over China's Mainland 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 *** 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.

关键词： Terrestrial water storage anomalies multi-forcing and multi-model ensemble simulation bayesian model averaging spatiotemporal variation uncertainty

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A new approach for bayesian model averaging

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Science China Earth Sciences 2012年第11期55卷 1336-1344页

作者： TIAN XiangJun XIE ZhengHui WANG AiHui YANG XiaoChun The International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Meterological Bureau of Xi'an City Xi'an 710016China

bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather ***,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the *** methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model *** methods have their own respective strengths and *** this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM *** the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.

关键词： bayesian model averaging multi-model ensemble forecasts BMA-BFGS limited memory quasi-Newtonian algorithm,land surface models soil moisture

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A Mixture-Based bayesian model averaging Method

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Open Journal of Statistics 2016年第2期6卷 220-228页

作者： Georges Nguefack-Tsague Walter Zucchini Biostatistics Unit Department of Public Health Faculty of Medicine and Biomedical Sciences University of Yaounde 1 Yaounde Cameroon Institute for Statistics and Econometrics University of Goettingen Goettingen Germany

bayesian model averaging (BMA) is a popular and powerful statistical method of taking account of uncertainty about model form or assumption. Usually the long run (frequentist) performances of the resulted estimator are hard to derive. This paper proposes a mixture of priors and sampling distributions as a basic of a Bayes estimator. The frequentist properties of the new Bayes estimator are automatically derived from bayesian decision theory. It is shown that if all competing models have the same parametric form, the new Bayes estimator reduces to BMA estimator. The method is applied to the daily exchange rate Euro to US Dollar.

关键词： Mixture bayesian model Selection bayesian model averaging bayesian Theory Frequentist Performance

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Online remaining-useful-life estimation with a bayesian-updated expectation-conditional-maximization algorithm and a modified bayesian-model-averaging method

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Science China(Information Sciences) 2021年第1期64卷 146-161页

作者： Yong YU Xiaosheng SI Changhua HU Jianfei ZHENG Jianxun ZHANG School of Missile Engineering Rocket Force University of Engineering School of Mechanical Engineering Xi'an Jiaotong University

Online remaining-useful-life(RUL) estimation is an effective method with respect to ensuring the safety of complex-huge systems. Generally, current methods assume a specific degradation model when degradation values are observed in the initial degradation phase. However, this assumption may not always be robust enough owing to the often-ambiguous inherent incipient-degradation characteristic. Therefore,besides model-parameter uncertainty, the uncertainty of the degradation model is worth examining in online RUL estimations. In this paper, a bayesian-updated expectation-conditional-maximization(ECM) algorithm is adopted to address the uncertainty of prior parameters, and a modified bayesian-model-averaging method is used to deal with the uncertainty of the degradation model. Then, simulation studies are conducted to analyze the effectiveness of the proposed fusion algorithm. Results suggest that the bayesian-updated ECM algorithm and modified bayesian-model-averaging method effectively address the associated uncertainties of model parameters and the degradation model itself. Finally, we apply the proposed fusion algorithm to predict the RUL of a gyroscope.

关键词： online RUL estimation parameter uncertainty model uncertainty bayesian method ECM algorithm bayesian model averaging

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bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data

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Underground Space 2024年第3期16卷 79-93页

作者： Zilong Zhang Tingting Zhang Xiaozhou Li Daniel Dias School of Civil Engineering Central South UniversityChangshaHunan 410075China Laboratory 3SR Grenoble Alpes UniversityCNRS UMR 5521Grenoble 38000France

Numerous analytical models have been developed to predict ground deformations induced by tunneling,which is a critical issue in tunnel ***,the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings,given the paucity of monitoring data in field *** paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying bayesian model averaging to several representative prediction *** accounting for both model and parameter uncertainties,this approach enables more realistic predictions of ground deformations than individual ***,our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements,while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal ***,our analysis reveals that when monitoring data are sparse,model uncertainties may contribute up to 78.7%of the total ***,obtaining sufficient data for parameter fitting is crucial for accurate *** proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations.

关键词： Tunnelling-induced ground deformations Sparse data model uncertainties bayesian model averaging

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Learning Dynamic Causal Relationships Among Sugar Prices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 809-818页

作者： Jing XU Xing-wei TONG Fang WANG Jian-ping CHEN School of Statistics University of International Business and EconomicsBeijing 100029China School of Statistics Beijing Normal UniversityBeijing 100875China COFCO Tunhe Co. LtdUrumqiXinjiang 830000China

In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market （ZC）, spot sugar price in Zhengzhou （ZS） and futures sugar closing price in New York futures exchange market（NC） and the other includes futures sugar opening price in Zhengzhou （ZO）, ZS and NC. For each quarter, we first use bayesian model selection to obtain the optimal causal graph with the highest BD scores and then use bayesian model averaging approach to explore the causal relationship between every two variables. From the real data analysis, the two conclusions almost coincide, which shows that the two methods are practical.

关键词： bayesian Network dynamic causal relationships bayesian model selection bayesian model averaging

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BMA probability quantitative precipitation forecasting of land-falling typhoons in south-east China

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Frontiers of Earth Science 2019年第4期13卷 758-777页

作者： Linna ZHAO Xuemei BAI Dan QI Cheng XING Chinese Academy of Meteorological Sciences Beijing 100081China Heilongjiang Meteorological Observatory Harbin 150001China National Meteorological Centre Beijing 100081China Heilongjiang Mulan County Meteorological Observatory Mulan 151900China

The probability of quantitative precipitation forecast(PQPF)of three bayesian model averaging(BMA)models based on three raw super ensemble prediction schemes(i.e.,A,B,and C)are established,which through calibration of their parameters using 1-3 day precipitation ensemble prediction systems(EPSs)from the China Meteorological Administration(CMA),the European Centre for Medium-Range Weather Forecasts(ECMWF)and the National Centers for Environmental Prediction(NCEP)and observation during land-falling of three typhoons in south-east China in *** comparison of PQPF shows that the performance is better in the BMA than that in raw ensemble *** average,the mean absolute error(MAE)of 1 day lead time forecast is reduced by 12.4%,and its continuous ranked probability score(CRPS)of 1-3 day lead time forecast is reduced by 26.2%,*** the amount of precipitation prediction by the BMA tends to be underestimated,but in view of the perspective of probability prediction,the probability of covering the observed precipitation by the effective forecast ranges of the BMA are increased,which is of great significance for the early warning of torrential rain and secondary disasters induced by it.

关键词： bayesian model averaging probabilistic quantitative precipitation forecasting ensemble prediction typhoon precipitation

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Clinical characteristics and mortality risk prediction model in children with acute myocarditis

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World Journal of Pediatrics 2023年第2期19卷 180-188页

作者： Shi-Xin Zhuang Peng Shi Han Gao Quan-Nan Zhuang Guo-Ying Huang Children’s Hospital of Fudan University 399 Wan Yuan RoadShanghai201102China Pediatric Clinical Research Unit Department of Research ManagementChildren’s Hospital of Fudan UniversityShanghaiChina

Background Acute myocarditis(AMC)can cause poor outcomes or even death in *** aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital *** This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January *** demographics,clinical examinations,types of AMC,and laboratory results were collected at hospital ***-hospital survival or death was *** characteristics associated with death were *** Among 67 children,51 survived,and 16 *** most common symptom was digestive disorder(67.2%).Based on the bayesian model averaging and Hosmer–Lemeshow test,we created a final best mortality prediction model(acute myocarditis death risk score,AMCDRS)that included ten variables(male sex,fever,congestive heart failure,left-ventricular ejection fraction4,fulminant myocarditis,abnormal creatine kinase-MB,and hypotension).Despite differences in the characteristics of the validation cohort,the model discrimination was only marginally lower,with an AUC of 0.781(95%confidence interval=0.675–0.852)compared with the derivation *** calibration likewise indicated acceptable fit(Hosmer‒Lemeshow goodness-of-fit,P¼=0.10).Conclusions Multiple factors were associated with increased mortality in children with *** prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.

关键词： Acute myocarditis bayesian model averaging Fulminant myocarditis Hosmer–Lemeshow test Mortality risk prediction model Pediatrics

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