长江流域夏季降水的贝叶斯多模式集合概率预测

A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).     

最优子集回归方法在季节气候预测中的应用

利用DEMETER计划多个模式的模拟资料研究1959～2001年多模式集合预报的季节降水在中国区域的表现, 并结合最优子集回归(OSR)方法对中国区域的季节降水进行降尺度预报, 比较其与多模式集合预报的技巧。研究表明: 多个单模式在中国区域对季节降水的模拟性能普遍较差, 多元线性回归(MLR)集合的预报技巧不如集合平均(EM)。利用OSR方法进行降尺度预报可以极大改善中国区域季节降水的预报技巧。夏季, 降水距平相关系数(ACC)在长江以南、西藏以及内蒙古中部等地区提高很显著, ACC在中国区域的平均达到0.29, 明显高于多模式集合平均与多元线性回归集合。冬季, OSR方法可以改善多模式集合在中国北方地区较低的预报技巧。概率Brier技巧评分(BSS)也表明了OSR方法对季节降水预报的改善。需要说明的是, 虽然OSR方法在中国区域能明显提高季节降水的预报技巧, 但是其选取的预报因子与中国区域季节降水的物理机制问题仍有待于进一步的研究。     

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, wh     

Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes

The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from ˉve coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.     

中国夏季降水多模式集成概率预报研究

基于TIGGE资料中的中国气象局（CMA）、欧洲中期天气预报中心（ECMWF）、日本气象厅（JMA）、美国国家环境预报中心（NCEP）以及英国气象局（UKMO）五个中心2007-2011年5月25日-8月31日中国地区逐日12-36 h、36-60 h、60-84 h、84-108 h、108-132 h与132-156 h累积降水集合预报资料,分别利用PoorMan （POOL）和多模式消除偏差（MBRE）两种方法对2011年各中心降水概率预报进行集成,并采用RPS和BS评分方法对预报效果进行评估。结果表明,对于12-156 h逐24 h累积降水量概率预报,多模式集成预报效果优于单模式预报效果,且多模式消除偏差概率预报效果最好;针对小雨、中雨以及大雨以上降水,PoorMan和MBRE概率预报较单中心预报效果均有提高,MBRE概率预报效果优于PoorMan方法。     

基于IPCC A1B情景的中国未来气候变化预估:多模式集合结果及其不确定性

利用耦合模式比较计划(CMIP3)提供的20世纪气候模拟试验(20C3M)及A1B情景预估试验,讨论了全球增暖情景下21世纪中期中国气候的可能变化。结果表明,A1B情景下,中国夏季降水变化在-0.1～1.1mm/d,冬季降水变化在-0.2～0.2mm/d。模式对降水变化的预估存在较大不确定性。无论冬夏,预估的全国表面气温都将升高,升温幅度在1.2～2.8℃;随纬度升高,增暖幅度相应增大。模式对表面气温变化的预估能力强于对降水变化的预估能力。在A1B情景下,东亚夏季风增强,而冬季风则略为减弱,东亚夏季风雨带到达最北后南撤的时间较之20C3M滞后约一个月。     

全国网格化多模式集成空气质量预报的初步建立

为降低单个模式预报的不确定性和提高多模式集成空气质量预报系统的精细化程度,利用Cressman插值初步建立了我国0.25°X0.25°网格化污染物实况。结合4套空气质量数值预报模式,通过均值集成、权重集成和多元线性回归集成分别逐格点建立了集成预报。在预报当天各单一模式和集成方法前50 d预报效果评估基础上,建立了最优集成预报。对2018年12月19一22日一次重污染过程中集成预报的PM_(2.5)浓度评估结果显示:在污染较重时刻,最优集成预报与观测之间的归一化平均偏差(NMB)值在重污染地区保持在—20%～40%,对污染程度为良及以上区域的预报范围相较于单个模式更接近观测。整个过程中,最优集成在大部分污染区域与观测之间的NMB值为—20%～20%,均方根误差(RMSE)值为35～75μg·m~(-3),相关系数(R)值大于0.4。相较于所有单一模式和其他集成方法,最优集成在全国最多的格点有着较高的总体评分。在污染最重区域的8个城市,最优集成预报的污染过程平均开始和结束时间分别比观测时间早1.8和6.9 h。未来需融合卫星反演和地表观测来提高网格化污染物实况的精细化程度,利用降尺度、主客观融合和滚动订正等方法进一步提高网格化多模式集成空气质量预报的准确率。     

11个S2S模式对MJO预报效果的评估分析

利用次季节一季节预报研究计划(Subseasonal to Seasonal Prediction Project,S2S)的多模式产品集,系统评估了产品集中11个模式对MJO的实际预报技巧.如果以距平相关系数ACC为0.5作为有效预报技巧的阈值,S2S各模式的MJO实际预报时效为8～32 d.S2S各模式预报普遍低估...     

Statistical Downscaling of Pattern Projection Using Multi-Model Output Variables as Predictors

A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps: coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling.     

多模式集合预报技术及其分析与检验

基于国家气象中心天气预报业务平台,对德国、日本、欧洲中心数值预报模式和我国T213模式的夏季预报产品进行检验,在此基础上,通过不同模式对目标区域预报能力的分析,分别应用神经元网络预报技术和基于Ts评分的客观多模式权重系数法(ME),建立了4个模式的集合预报方法,并应用于2005年汛期业务运行。结果表明:ME对短期降水预报技巧高于简单集合平均,因此具有一定的业务应用前景。     

集合数值天气预报的研究进展

集合预报是目前国外广泛应用的一种新的数值预报技术,国内由于计算条件的限制,集合预报的研究应用起步较晚,目前虽已取得了一些研究成果,但还没有广泛的应用到实际的业务预报之中。集合预报的应用,在天气预报上主要是概率预报,另外在“目标观测”、资料同化等方面也有广泛应用。集合概率预报的一系列适用的验证,增加了概率预报的信度。     

台风路径多模式集成预报技术研究

利用NCEP、ECMWF、日本数值、英国数值、上海台风模式和广州模式包含全球模式和区域在内的6家数值模式资料,利用近似SEAV方法,设计台风路径多模式集成预报方法(SHME),并用2014—2016年的台风客观预报数据进行多模式集成预报的效果检验,且与ECMWF模式进行比较,通过比较发现,SHME方法较ECMWF在12～48 h预报上有明显改进,在72～120 h预报2014年尤其突出,2015—2016年均与ECMWF预报效果相当。     

NCEP、ECMWF及CMC全球集合预报业务系统发展综述

总结了目前最具代表性的3个全球集合预报系统（global ensemble forecast system,GEFS）——美国国家环境预报中心（National Centers for Environmental Prediction,NCEP）、欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts,ECMWF）和加拿大气象中心（Canadian Meteoro-logical Centre,CMC）建成至今的发展概况。由于计算资源的不断扩展,各中心集合预报系统的模式分辨率、集合成员数也随之增加。同时各中心都在不断地致力于发展和完善初始和模式扰动方法,来更好地估计与初值和模式有关的不确定性,促进预报技巧的提高。其中初始扰动方法从最初的奇异向量法（ECMWF）、增殖向量法（NCEP）和观测扰动法（CMC）更新为现在的集合资料同化—奇异向量法（ECMWF）、重新尺度化集合转换法（NCEP）和集合卡尔曼滤波（CMC）。在估计模式不确定性方面,ECMWF和CMC都修订了各自的随机参数化方案和多参数化方案,NCEP最近也在模式中加入了随机全倾向扰动。为提高全球高影响天气预报的准确率,TIGGE计划（the THORPEX interactive grand global ensemble）的提出增进了国际间对多模式、多中心集合预报的合作研究,北美集合预报系统（North American ensemble forecast system,NAEFS）为建立全球多模式集合预报系统提供了业务框架,这都将有助于未来全球交互式业务预报系统的构建     

基于TIGGE资料的地面气温延伸期多模式集成预报

基于TIGGE资料中心提供的CMC、ECMWF、UKMO及NCEP四个集合预报中心2008年7月1日-9月30日北半球中纬度地区地面气温10 ～ 15 d延伸期集合预报产品,首先采用Tala-grand分布及离散度—误差关系评估了单个预报系统的预报性能,然后分别利用多模式集成平均(Ensemble Mean,EMN)、消除偏差集成平均(Bias-Removed Ensemble Mean,BREM)及多模式超级集合(Multi-model Superensemble,SUP)对地面气温进行多模式集成预报试验.由于逐日的延伸期预报准确率相对较低,因此人们更关注延伸期预报对天气过程的预报准确率.对各个集合预报系统的逐日预报资料以及逐日“观测”资料做滑动平均,并对处理后的资料进行多模式集成,最后对超级集合预报的训练期长度进行调试,以获得最佳训练期长度.结果表明,四个集合预报系统的离散度相对于均方根误差都偏小,ECMWF预报效果最好,NCEP次之,UKMO预报效果最差.EMN、BREM及SUP三种多模式集成方法的预报效果均优于单个系统且SUP对预报效果的改善最明显.滑动平均后,预报误差进一步降低,且滑动步长越长,误差越小.对于SUP的训练期,逐日预报和3d滑动平均10～12 d预报最佳训练期长度为75 d;13 ～ 15 d预报最佳训练期长度为35 d;5 d及7d滑动平均其训练期长度在各个时效均以35 d为宜.     

STUDY OF THE MODIFICATION OF MULTI-MODEL ENSEMBLE SCHEMES FOR TROPICAL CYCLONE FORECASTS

This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble (TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean (BREM) and superensemble (SUP), are compared with the ensemble mean (EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible.     

欧洲业务集合预报系统进展

由于大气初值与数值模式中物理过程存在不确定性等客观事实,集合预报无疑代表着数值天气预报未来前进与发展的方向,它标志着天气预报的预报范式转变,即用户不仅可以得到未来大气状态的单一现实,还可得到未来大气可能出现的一系列场景。文中扼要地梳理了欧洲全球业务集合预报与有限区域模式高分辨业务集合预报的研究动态与技术发展、基本问题及其未来最新发展方向,包括:1)欧洲中期天气预报中心的业务集合预报系统发展沿革及概况;2)欧洲国家主要业务高分辨率集合预报系统概况;3)当前业务集合预报存在的问题、挑战及未来前进的方向。文中除了关注欧洲中期天气预报中心的集合预报应用,还梳理了目前欧洲高分辨业务集合预报取得的成就,以引起有关研究人员的注意。总之,借鉴欧洲业务集合预报的发展思路,不仅有助于集合预报理论创新,还对发展集合预报业务有重要指导意义。     

多模式短期集合降水概率预报试验

利用AREM、MM5和WRF3个中尺度数值模式,通过积云参数化和边界层方案组合构成15个集合成员,对中国2003年7月汛期降水分别采用平均法、相关法、Rank法开展多模式短期集合降水概率预报试验。结果表明：用上述3种方法制作的多模式短期集合概率预报都能对降水落区及中心做出较准确的预报,但平均法和相关法易使降水落区虚假放大,Rank法则能较好地刻画降水落区边界及强度,其概率预报效果优于平均法和相关法结果。采用BS（Brier score）、RPS（ranked probability score）评分和ROC（relative operating characteristic）曲线对3种方法的降水概率预报效果评价时发现,对某一临界值等级的概率预报,3种方法结果差异较小;但对某一天降水概率预报结果的综合评价表明,Rank法显著优于前两种方法;降水强度大、范围广的降水的RPS评分和各级的BS评分较高,表明多模式降水概率预报也具艰巨性。     

气候系统模式对Hadley环流的模拟和未来变化预估

针对全球变暖背景下未来Hadley环流将如何变化这一问题,评估了气候系统模式对1970~1999年Hadley环流时空特征的模拟效能,并在此基础上选取能合理模拟Hadley环流空间结构、强度指数和边界指数变化的3个模式,通过多模式集合方法预估了未来Hadley环流在A1B排放情景下的可能演变。预估结果表明,在全球变暖背景下,相比于1970~1999年,到本世纪末期(2070~2099年),北半球Hadley环流在4个季节都将减弱,春季变化幅度相对较弱;南半球Hadley环流在冬季和夏季也会减弱,而在春季和秋季的变化不明显。另外,北半球Hadley环流的北边界除在夏季向南收缩外,在其它3个季节均向北伸展;南半球Hadley环流的南边界在4个季节均向极地方向移动。两个半球的Hadley环流在垂直方向还将向对流层上层伸展。       

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model’s capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans.     

中国冬季气温的集合典型相关分析和预报

以欧亚大陆地面温度、北半球500 hPa高度、热带印度洋SST(sea surface temperature)以及北太平洋SST为预报因子,通过典型相关分析(canonical correlation analysis,简称CCA)建立预报关系,然后用集合典型相关分析预报(ensemble canonical correlation prediction,简称ECC)方法预报中国冬季气温,并分析预报技巧及进行独立样本检验.结果表明,不同的预报因子对各个地区有不同的预报技巧,以欧亚大陆地面温度为预报因子预报技巧较高,而ECC模式对中国冬季气温有更好的预报能力,预报技巧高于任何一个单因子场的CCA预报;采用回归法的集合平均比简单的等权集合平均预报技巧更稳定.