Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.

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

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 regions.The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon.The 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 credible.This 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.

Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging

Climate change in mountainous regions has significant impacts on hydrological and ecological systems. This research studied the future temperature, precipitation and snowfall in the 21^（st） century for the Tianshan and northern Kunlun Mountains（TKM） based on the general circulation model（GCM） simulation ensemble from the coupled model intercomparison project phase 5（CMIP5） under the representative concentration pathway（RCP） lower emission scenario RCP4.5 and higher emission scenario RCP8.5 using the Bayesian model averaging（BMA） technique. Results show that（1） BMA significantly outperformed the simple ensemble analysis and BMA mean matches all the three observed climate variables;（2） at the end of the 21^（st） century（2070–2099） under RCP8.5, compared to the control period（1976–2005）, annual mean temperature and mean annual precipitation will rise considerably by 4.8°C and 5.2%, respectively, while mean annual snowfall will dramatically decrease by 26.5%;（3） precipitation will increase in the northern Tianshan region while decrease in the Amu Darya Basin. Snowfall will significantly decrease in the western TKM. Mean annual snowfall fraction will also decrease from 0.56 of 1976–2005 to 0.42 of 2070–2099 under RCP8.5; and（4） snowfall shows a high sensitivity to temperature in autumn and spring while a low sensitivity in winter, with the highest sensitivity values occurring at the edge areas of TKM. The projections mean that flood risk will increase and solid water storage will decrease.

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

作为全球能量和水分循环的关键参量,陆地水储量包括土壤水、地表水、地下水、积雪和生物体水等,在水文、气候、农业、生态等众多领域起重要影响。与地面观测和遥感反演相比,陆面模式在刻画陆地水储量的时空变率等方面具有明显优势。然而不同模式参数化方案以及大气强迫驱动导致陆地水储量模拟存在不确定。为了减少陆地水储量模拟不确定性,本研究建立了基于贝叶斯模型平均(BMA)和多强迫多模式集合的陆地水储量模拟系统,获得了中国区域1979–2008年陆地水储量数据集。选取2004–08年的数据与GRACE重力卫星数据比较分析,结果显示BMA集合模拟的陆地水储量异常(Terrestrial water storage anomalies,TWSA)优于所有单个模拟结果,与GRACE观测的TWSA有更高的相关系数和更小的误差。

Comparison between Different ESI Methods on Refractory Epilepsy Patients Shows a High Sensitivity for Bayesian Model Averaging

Electrical Source Imaging (ESI) is a non-invasive technique of reconstructing brain activities using EEG data. This technique has been applied to evaluate epilepsy patients being evaluated for epilepsy surgery, showing encouraging results for mapping interictal epileptiform discharges (IED). However, ESI is underused in planning epilepsy surgery. This is basically due to the wide availability of methods for solving the electromagnetism inverse problem (e-IP) associated to few studies using EEG setups similar to those most commonly used in clinical setting. In this study, we applied six different methods of solving the e-IP based on IEDs of 20 focal epilepsy patients that presented abnormalities in their MRI. We compared the ESI maps obtained by each method with the location of the abnormality, calculating the Euclidian distances from the center of the lesion to the closest border of the method solution (CL-BM) and also to the solution’s maxima (CL-MM). We also applied a score system in order to allow us to evaluate the sensitivity of each method for temporal and extra temporal patients. In our patients, the Bayesian Model Averaging method had a sensitivity of 86% and the shortest CL-MM. This method also had more restricted solutions that were more representative of epileptogenic activities than those obtained by the other methods.

A Mixture-Based Bayesian Model Averaging Method

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.

Effects of Bayesian Model Selection on Frequentist Performances: An Alternative Approach

It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selection estimator (PMSE) whose properties are hard to derive. Conditioning on data at hand (as it is usually the case), Bayesian model selection is free of this phenomenon. This paper is concerned with the properties of Bayesian estimator obtained after model selection when the frequentist (long run) performances of the resulted Bayesian estimator are of interest. The proposed method, using Bayesian decision theory, is based on the well known Bayesian model averaging (BMA)’s machinery;and outperforms PMSE and BMA. It is shown that if the unconditional model selection probability is equal to model prior, then the proposed approach reduces BMA. The method is illustrated using Bernoulli trials.

黄河流域资源型城市碳达峰情景模拟研究

自然灾害与极端天气频发,各城市有效推进碳减排已刻不容缓,尤其是黄河流域的资源型城市,地区经济发展主要依赖高耗能、高排放的资源产业,使得减排降碳任务更为艰巨.本文基于黄河流域资源型城市碳排放数据,首先采用贝叶斯模型平均法进行因子选择,从影响碳排放的多种影响因素中提取重要信息,找出后验概率值大于50%时的几个变量作为影响碳排放的重要因素.其次,基于黄河流域资源型城市面板数据,结合情景分析法,在基准情景、政策导向型情景和科技驱动型情境下,构建个体时点双固定效应面板数据模型预测不同情境下各地区的碳排放量.结果表明:煤炭消费量、城镇化率、水泥产量、人均GDP、天然气消费量和第二产业在GDP中的比重是影响黄河流域资源型城市碳排放量的主要因素;通过比较政策导向方案和技术驱动方案,可以发现政策导向方案是一个渐进的过程,而技术驱动的碳减排效果更加快速和明显,基准情形下黄河流域资源型地区难以实现2030碳达峰目标.

清江流域降水的多模式BMA概率预报试验

基于TIGGE资料中的ECMWF、UKMO、JMA、CMA四套模式的2016年6月1至7月31日逐日降水集合预报资料,结合清江流域10个国家基准站观测数据,建立了流域贝叶斯模型平均(BMA)概率预报模型,开展流域多模式集合BMA技术的概率预报试验与评估。结果表明,在清江流域多模式集合的BMA模型最佳滑动训练期长度为40 d,BMA模型预报比原始集合预报有更高预报技巧,比四个原始集合预报MAE平均值减少近11%左右,而对于CRPS除了CMA中心无订正效果外,较其他三个模式平均值提高近15%左右。多模式集合BMA技术能预报降水全概率PDF曲线和大于某个降水量级的概率,同时能给出确定性降水预报,对于极端强降水(大暴雨一特大暴雨量级),BMA 75~90百分位数预报效果较好,对于强降水(暴雨量级),BMA 50~75百分位数预报效果较好,对于一般性降水(小雨一大雨量级),BMA确定性预报结果或50百分位数预报效果较好。

基于校准窗口集成与耦合市场特征的可解释双层日前电价预测

随着电力市场之间耦合程度不断加深,只局限于单个市场内部的传统特征集不足以支撑高精度预测的需求。而且模型预测性能对校准窗口的选择敏感,而传统电价预测仅使用一个固定时间长度的数据集,同时预测模型的“黑盒”结构导致预测结果在工程应用中可信度偏低。针对上述问题,该文提出一种考虑校准窗口集成与耦合市场特征的可解释双层日前电价预测框架。内层框架为基于改进自适应噪声完备集合经验模态分解(improved complete ensemble empirical mode decomposition,ICEEMDAN)的择优预测,首先分解原始电价序列,然后应用Lasso估计回归(lassoestimated autoregressive,LEAR)、长期和短期时间序列网络(long-term and short-term time-series networks,LSTNet)、卷积神经网络-长短记忆神经网络(convolutionalneuralnetworks-longshort termmemory,CNN-LSTM)、移动平均(autoregressive integrated moving average,ARIMA)和核极限学习机(kernel extreme learning machines,KELM)模型预测子序列并选择最优预测算法。外层框架为基于贝叶斯模型平均(bayes modelaveraging,BMA)的校准窗口集成预测,针对每个不同校准窗口长度数据集下的预测分配权重并集成得到预测电价。最后,通过可解释方法沙普利加性解释模型(shapley additiveexplanations,SHAP)分析耦合市场特征如何影响预测电价。该文通过北欧电力市场数据集的算例分析证明了所提算法的优越性和校准窗口集成方案的有效性。

基于TIGGE多模式集合的24小时气温BMA概率预报

利用TIGGE(THORPEXInteractiveGrandGlobalEnsemble)单中心集合预报系统(ECMWF、UnitedKingdomMeteorologicalOffice、ChinaMeteorologicalAdministration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesianmodelaveraging,BMA)参数,从而建立地面日均气温BMA概率预报模型。由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好。多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好。它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%。基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义。

中国环境规制政策工具的比较与选择——基于贝叶斯模型平均(BMA)方法的实证研究

改革开放以来,中国政府逐步构建起了命令—控制型、市场激励型、公众参与型和自愿行动型"四维一体"的环境政策工具体系。针对不同政策类型工具的有效性,很多学者已经运用多种方法进行了大量研究,但大多数学者只关注其中某一种工具的治理效果,同时考虑所有政策工具效果的文献并不多见。本文首次运用贝叶斯模型平均(BMA)方法实证分析了不同类型环境政策工具在当前中国环境治理体系下的相对贡献程度,实证结果表明:命令—控制型工具和市场激励型工具仍然是当前中国治理环境污染最为有效的政策工具,公众参与型工具和自愿行动型工具的有效性相对较差。基于此,本文的政策建议是:首先,中国政府不仅需要构建完善的环保法律法规体系,更需要加大环保执法投入,提升环保执法的主动性;其次,中国政府应该进一步完善市场激励型工具,建立更加弹性化的排污收费标准和更为严格的排污惩罚制度,推动排污权交易制度更广泛地实施;再次,积极推动社会公众参与环境保护,降低社会公众的参与成本,使得社会公众能更加便捷地参与环境治理;最后,积极鼓励非政府组织、企业发起自愿性环保项目,对于推动环保标准的提升和环保法律法规的逐步完善,加强居民、企业的环境保护意识具有重要意义。因此,全社会环境问题的治理是一个系统性工程,必须采取相应的措施,充分运用命令—控制、市场激励、公众参与、自愿行动等正式和非正式的环境治理措施,形成一个有机、有序的环境治理体系,才能提升所有环境规制政策工具的有效性,促进经济社会可持续发展。

中国经济增长的决定因素分析——基于贝叶斯模型平均(BMA)方法的实证研究

采用贝叶斯模型平均(Bayesian Model Averaging)方法,使用1990-2007年省际数据,对长期影响中国经济增长的诸多因素的有效性和稳健性进行了识别和检验。研究结论表明:高等教育发展阶段、工业化推进速度、对外开放程度、东部区位优势、消费能力和对内开放水平等6个解释变量对中国经济增长具有长期、持续和稳健的影响,是中国经济增长的长期决定因素。城市规模、中部区位优势和初始经济条件等3个解释变量对经济增长也具有一定的解释能力。此外,从解释变量对经济增长边际影响的程度来看,工业化推进速度变量对经济增长的边际影响最强,其次是消费能力变量和对外开放程度变量。

基于气候因子的川中柏木人工林直径分布预测模型

构建含气候因子的柏木人工林径级分布模型,分析林分直径分布的规律性及其对气候变化的响应,为气候变化下人工林经营决策提供科学依据。利用川中柏木人工林样地调查数据,应用极大似然法估计Weibull分布参数,采用贝叶斯模型平均法(BMA)构建柏木人工林径级分布模型,并以此模拟气候变化对直径分布的影响。结果表明,极大似然法均较好地估计柏木人工林的直径分布;基于BMA构建包含坡度、腐殖质层厚度、年龄、每公顷株数、林分断面积、最热月平均温和年降水量的径级分布模型,五折交叉验证得到的参数b的平均绝对偏差、决定系数和均方根误差分别为0.78、0.78、0.92,参数c分别为0.26、0.67、0.32,预测精度较好;随着最热月平均温和年降水量的增加,直径分布逐渐右移。采用BMA构建的径级分布模型具有较好的精度,且最热月平均温度和年降水量的增加会促进林分生长。

基于贝叶斯模型平均(BMA)方法的中国房地产价格影响因素分析

对可能影响中国房价的诸多因素的重要性问题进行识别和检验,基于模型不确定性的视角,使用中国30个省（区）2002~2013年面板数据,采用贝叶斯模型平均（BMA）方法进行模型设定与分析。研究认为,在可能对中国房价产生影响的19个指标中,信贷政策、心理预期、物价水平、房屋竣工面积和产业结构合理化等5个解释变量的后验概率大于90%,它们是影响现阶段中国房地产价格的决定因素;应通过差别化的信贷政策分区域控制房价,通过新闻媒体公开统计和发布房地产数据正确引导人们的心理预期,通过适宜的货币政策有效控制物价,通过保障性住房建设增加房地产供给,通过合理化的产业结构引导房价调控等,促进中国房地产市场的健康发展。

自然资本对中国经济增长的深层影响及机制

自然资本是经济增长的重要物质支撑,对可持续发展具有关键作用。该研究将多类经济增长理论的代理变量共同纳入扩展的MRW模型,利用BMA方法解决模型不确定性难题,明确区分可再生自然资本与不可再生自然资本,兼顾依赖度与丰裕度,基于省级面板数据考察自然资本对中国经济增长的直接与深层影响,并借助CART算法开展多重机制分析。研究发现:①兼顾表层理论与深层理论构建模型,自然资本对中国经济增长具有深层影响,且影响方向因代理指标而异,其依赖度“诅咒”效应明显但丰裕度“祝福”效应突出,该全景视角可以调和已有文献对“资源诅咒”存在性的严重分歧。②两类自然资本的增长效应迥然不同,可再生自然资本的影响符合“资源中性”假说,不可再生自然资本依赖度的“诅咒”效应和丰裕度的“祝福”效应都很强烈,其对经济增长的整体效应取决于资源利用模式。③高物质资本积累机制下,自然资本依赖度对经济增长的“诅咒”效应消失,而其丰裕度对经济增长的“祝福”效应显著提升,城市化率和纬度分别从发展阶段和地理区位视角丰富并深化物质资本积累的调节机制。中国各地应立足自身资源禀赋与经济发展阶段,将自然资源租金用于国民财富再投资和资本组合优化,通过自然资源保护利用与投资补偿摆脱“资源诅咒”,走上经济发展与生态文明双赢的可持续发展之路。

2020年超长梅汛期降水概率预报应用与检验

基于欧洲中期天气预报中心(European Center for Medium-range Weather Forecasts, ECMWF)集合预报资料及浙江全省自动站降水观测资料,采用贝叶斯模型平均(Bayesian Model Average, BMA)方法对2020年浙江超长梅汛期开展降水概率预报订正试验。采用平均绝对误差、连续等级概率评分、布莱尔评分B_S、Talagrand、概率积分变换(Probability Integral Transform, PIT)直方图及属性图检验方法对本次过程BMA订正前后的概率预报进行对比分析,结果表明:(1)50 d为适用于浙江梅汛期ECMWF集合预报订正的BMA最优训练期,经最优训练期的BMA订正后,预报离散度有所增加,预报误差有所下降;(2)BMA对0.1 mm、10.0 mm和25.0 mm阈值降水的订正效果显著,经BMA订正后3个阈值的降水预报B_S下降率分别为25.92%、19.29%、4.76%,但对超过50.0 mm的降水订正效果不明显,且随着降水阈值增加,BMA的订正效果减弱;(3)在强降水个例中,BMA能有效减少各阈值降水预报概率大值落区偏差,使订正后的降水预报概率大值区与观测落区更一致。

基于数据与机理驱动的密云水库洪水预报技术研究及应用

密云水库作为北京市重要的防洪控制工程、地表饮用水水源地和水资源战略储备基地,其安全运行对首都防洪安全、供水安全和生态安全至关重要。近年,在“自然-人工”二元因素的作用及影响下,流域的产汇流特征发生了很大变化,高水位运行对水库洪水预报精度提出了更高要求,原有的洪水预报模型系统已经不能满足需求。在原洪水预报模型系统的基础上,以密云水库上游流域作为研究对象,系统研究了高强度人类活动影响的水文模拟技术、水文模型参数高效率定技术、基于数据驱动的洪水预报技术、基于贝叶斯平均的多模型集合预报技术,并对其进行应用。结果表明:机理驱动模型在洪峰预测上精度更高,但呈现低谷且峰现时间滞后特点;数据驱动模型的峰现时间预测更准,洪峰预报精度整体上不如机理驱动模型;集成两类模型的贝叶斯平均贴近实际过程,预报精度大幅度提高。

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

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.