基于TIGGE资料的地面气温多模式超级集合预报

基于TIGGE资料, 采用均方根误差分别对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和英国气象局4个中心集合预报的地面气温场集合平均结果进行检验评估, 比较各中心地面气温的预报效果。并利用超级集合、多模式集合平均和消除偏差集合平均3种方法对4个中心的地面气温预报进行集成, 同时对预报结果进行分析。结果表明: 2007年夏季日本气象厅与欧洲中期天气预报中心在北半球大部分地区预报效果最好, 各中心在不同地区预报效果不同。超级集合与消除偏差集合平均降低了预报误差, 预报效果优于最好的单个中心预报和多模式集合平均。对于较长的预报时效, 消除偏差集合平均表现出了更好的预报性能。     

北半球中纬度地区地面气温的超级集合预报

基于TIGGE资料中的ECMWF、JMA、NCEP和UKMO四个中心2007年6月1日-8月31日北半球中纬度地区地面气温24～168 h集合预报资料,分别利用固定训练期超级集合(SUP, Superensemble)和滑动训练期超级集合(R-SUP, Running Training Period Superensemble )对2007年8月8-31日预报期24 d进行超级集合预报试验.采用均方根误差对预报结果进行检验评估,比较了两种超级集合方法与最好的单个中心模式预报、多模式集合平均的预报效果.结果表明,SUP预报有效降低了预报误差,24～144 h的预报效果优于多模式集合平均(EMN, Ensemble Mean)和最好的单个中心预报,168 h的预报效果略差于EMN.R-SUP预报进一步改善了预报效果.对于24～168 h的预报,R-SUP预报效果都要优于EMN.尤其对于168 h的预报,R-SUP改进了预报效果,优于EMN.     

ECMWF模式地面气温预报的四种误差订正方法的比较研究

采用均方根误差对欧洲中期天气预报中心（ECWMF）确定性预报模式2007年1月至2010年12月的地面气温预报结果进行评估,并分别利用一元线性回归、多元线性回归、单时效消除偏差和多时效消除偏差平均的订正方法,对ECMWF模式地面气温预报结果进行订正。结果表明,4种订正方法都能有效地减小地面气温多个时效预报的误差,改进幅度约为1℃。在短期预报中仅考虑最新预报结果的一元线性回归订正方法要优于考虑多个预报结果的多元集成预报订正方法。在中期预报中考虑多个预报结果的多元集成预报订正方法更优,更稳定。在模式预报误差较大的情况下,多时效集成的订正方法能更稳定地减小误差。     

Operative Scheme for the Short-range Complex Forecasting of Surface Air Temperature and Humidity

The statistical scheme is proposed for the forecast of surface air temperature and humidity using operative weather forecasts with 3–5-day lead time from the best forecasting hydrodynamic models as well as the archives of forecasts of these models and observational data from 2800 weather stations of Russia, Eastern Europe, and Central Asia. The output of the scheme includes the forecasts of air temperature for the standard observation moments with the period of 6 hours and extreme temperatures with the lead times of 12–120 hours. The accuracy of temperature and humidity forecasts for the period from July 2014 till June 2017 is much higher than that for the forecasts of original hydrodynamic models. The skill scores for extreme temperature forecasts based on the proposed method are compared with the similar results of the Weather Element Computation (WEC) forecasting scheme and forecasts by weathermen.     

On the Evaluation of the Extremality Index EFI

The Extreme Forecast Index (EFI) calculations are performed using the ECMWF 2-m air temperature forecasts produced in the framework of the Subseasonal to Seasonal (S2S) Prediction Project. Four computation schemes are implemented using empirical and theoretical distributions of heat wave characteristics as well as the one-dimensional test statistics of histograms and linear interpolation formulas. Case studies (for different initial dates and regions) characterized by the significant air temperature anomalies in Northern Eurasia are performed using traditional forecast skill scores and the spatial verification methods to evaluate the efficiency of the proposed schemes for different threshold values of EFI. It is shown that the forecast quality can be considered satisfactory in most cases. The dependence of forecast skill on the intensity, spatial scales, and duration of temperature anomalies is revealed. Further studies should be carried out using larger samples based on several hydrodynamic models and the multimodel approach.     

OPERATIONAL ENSEMBLE FORECASTING AND ANALYSIS OF TROPICAL CYCLONES OVER THE WESTERN NORTH PACIFIC (INCLUDING THE SOUTH CHINA SEA)

Based on the tropical cyclone data from the Central Meteorological Observatory of China, Japan Meteorological Agency, Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts （ECMWF） during the period of 2004 to 2009, three consensus methods are used in tropical cyclone （TC） track forecasts. Operational consensus results show that the objective forecasts of ECMWF help to improve consensus skill by 2%, 3%-5% and 3%-5%, decrease track bias by 2.5 kin, 6-9 km and 10-12 km for the 24 h, 48 h and 72 h forecasts respectively over the years of 2007 to 2009. Analysis also indicates that consensus forecasts hold positive skills relative to each member. The multivariate regression composite is a method that shows relatively low skill, while the methods of arithmetic averaging and composite （in which the weighting coefficient is the reciprocal square of mean error of members） have almost comparable skills among members. Consensus forecast for a lead time of 96 h has negative skill relative to the ECMWF objective forecast.     

基于BMA方法的地面气温的10～15 d延伸期概率预报研究

利用TIGGE资料提供的欧洲中期天气预报中心(ECMWF)、美国国家环境预报中心(NCEP)、英国气象局(UKMO)三个预报中心2013年6月1日至8月31日的地面2 m气温10~15 d预报资料,对延伸期地面气温进行贝叶斯模式平均(Bayesian Model Averaging,BMA)预报试验。结果表明,BMA方法的预报效果随训练期长度而改变,训练期长度为30 d时预报效果最优。BMA方法可提供全概率密度函数,定量描述预报不确定性的大小,且陆地上预报不确定性大于海洋上的预报不确定性,高纬度地区预报不确定性大于低纬度地区的预报不确定性。利用CRPS评分对BMA概率预报技巧进行评估,发现预报技巧随预报时效的延长降低,且预报技巧在海洋上优于陆地、低纬度地区优于高纬度地区。此外,3 d、5 d和7 d滑动平均的预报值反映某些天气过程的平均要素预报,对于提高10~15 d延伸期概率预报技巧有一定效果,且滑动天数越长,预报效果越好。     

NMC与HLAFS降水预报的比较

建立了一套既适用于对预报员降水预报评估,又适用于对数值预报模式降水预报评估的客观检验方法,从对１９９６年７－９月检验的结果分析表明预报员对大雨预报Ｔｓ评分高于ＨＬＡＦＳ并且除西北地区外,其它区的２４小时大雨预报都超过气候概率,表现为大于０的技巧评分,预报员和ＨＬＡＦＳ对大雨预报的漏报率多于空报率,而对小雨和中雨的预报相反,两者都是空报多于漏报,预报中对西北地区的降水预报漏报较多。     

2009年夏季西太平洋台风路径和强度的多模式集成预报

基于TIGGE资料中的中国气象局、欧洲中期天气预报中心、日本气象厅和英国气象局等四个中心的2009年5月1日-8月31日台风预报资料,利用多模式集合平均、消除偏差集合平均和加权消除偏差集合平均等方法,对2009年8月1-31日预报期的西太平洋的台风路径和强度(中心气压)进行24～ 72 h预报时效的多模式集成预报,并对0907号台风“天鹅”和0908号台风“莫拉克”进行个例分析.结果表明:各中心对于不同时效的预报,预报技巧有明显差异.消除偏差集合平均与加权消除偏差集合平均显著地减小了预报误差,预报效果优于最好的单个中心预报和多模式集合平均.对于24 ～ 72 h预报,加权消除偏差集合平均方法始终表现出最好的预报性能.     

基于深度学习的中国地面气温的多模式集成预报研究

基于TIGGE资料中的欧洲中期天气预报中心、英国气象局、美国国家环境预报中心、韩国气象厅和日本气象厅2015年1月1日—9月30日中国及周边地区地面2 m气温24～168 h集合预报资料,利用长短期记忆神经网络(Long Short-Term Memory,LSTM)、浅层神经网络(Neural Networks,NN)、滑动训练期消除偏差集合平均(BREM)和滑动训练期多模式超级集合(SUP)方法对2015年9月5—30日26 d预报期进行集成预报试验。结果表明,BREM对5个单模式进行等权集成,预报结果易受预报效果较差模式的影响,整体预报技巧略低于单个最优模式ECMWF的预报技巧。其中在新疆南部,等权集成后的预报技巧更低。SUP的预报结果比所有单个模式预报更为准确。在144 h之前,SUP的误差明显小于ECMWF的预报误差,但随预报时效增加,误差增长幅度增大。NN对地面气温的预报效果与SUP的预报效果相当。LSTM整体预报效果最好,特别是在预报时效较长(超过72 h)时,比其他方法预报准确率明显提高。LSTM神经网络方法明显改进了我国西北、华北、东北、西南和华南大部分地区的气温预报,但在南疆部分地区误差较大。     

Forecast skill of the tropical intraseasonal oscillation in the NCEP GFS dynamical extended range forecasts

This study examines the forecast performance of tropical intraseasonal oscillation (ISO) in recent dynamical extended range forecast (DERF) experiments conducted with the National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) model. The present study extends earlier work by comparing prediction skill of the northern winter ISO (Madden-Julian Oscillation) between the current and earlier experiments. Prediction skill for the northern summer ISO is also investigated. Since the boreal summer ISO exhibits northward propagation as well as eastward propagation along the equator, forecast skill for both components is computed. For the 5-year period from 1 January, 1998 through 31 December, 2002, 30-day forecasts were made once a day. Compared to the previous DERF experiment, the current model has shown some improvements in forecasting the ISO during winter season so that the skillful forecasts (anomaly correlation>0.6) for upper-level zonal wind anomaly extend from the previous shorter-than 5 days out to 7 days lead-time. A similar level of skill is seen for both northward and eastward propagation components during the summer season as in the winter case. Results also show that forecasts from extreme initial states are more skillful than those from null phases for both seasons, extending the skillful range by 3–6 days. For strong ISO convection phases, the GFS model performs better during the summer season than during the winter season. In summer forecasts, large-scale circulation and convection anomalies exhibit northward propagation during the peak phase. In contrast, the GFS model still has difficulties in sustaining ISO variability during the northern winter as in the previous DERF run. That is, the forecast does not maintain the observed eastward propagating signals associated with large-scale circulation; rather the forecast anomalies appear to be stationary at their initial location and decay with time. The NCEP Coupled Forecast System produces daily operational forecasts and its predication skill of the MJO will be reported in the future.     

2010年8月环流形势的多模式超级集合预报

利用多模式超级集合预报法,以欧洲中期天气预报中心、日本气象厅、德国气象局、中国气象局和中国空军气象中心共5个决定性7 d预报产品为集合成员,对2010年8月500 hPa高度场和850 hPa温度场分别进行固定训练期和滑动训练期超级集合预报。采用均方根误差和相关系数对超级集合预报、单一模式预报和简单集合平均预报进行对比检验,同时对各预报结果的均方根误差空间分布进行对比分析。结果表明:超级集合预报在所有预报结果中最佳,且滑动集合预报对8月后期时段预报要略好于固定集合预报,两者预报效果均好于参与集合预报的各模式,也好于集合平均预报。但随着预报时效的延长,集合平均预报的优势也随之提升。从预报结果均方根误差的空间分布可知,多模式超级集合预报相比于单一模式预报效果提高的区域,500 hPa位势高度场主要位于印度半岛、印度洋、青藏高原及以西地区,而850 hPa温度场则主要位于蒙古、青藏高原、中国新疆及以西地区。     

Application of a serial extended forecast experiment using the ECMWF model to interpret the predictive skill of tropical intraseasonal variability

The extended-range forecast skill of the ECMWF operational forecast model is evaluated during tropical intraseasonal oscillation (ISO) events in the Indo-West Pacific warm pool. The experiment consists of ensemble extended serial forecasts including winter and summer ISO cases. The forecasts are compared with the ERA-40 analyses. The analysis focuses on understanding the origin of forecast errors by studying the vertical structure of relevant dynamical and moist convective features associated with the ISO. The useful forecast time scale for circulation anomalies is in average 13 days during winter compared to 7–8 days during summer. The forecast skill is not stationary and presents evidence of a flow-dependent nature, with states of the coupled system corresponding to long-lived convective envelopes associated with the ISO for which the skill is always low regardless of the starting date of the forecast. The model is not able to forecast skillfully the generation of specific humidity anomalies and results indicate that the convective processes in the model are associated with the erosion of the ISO forecast skill in the model. Circulation-associated anomalies are forecast better than moist convective associated anomalies. The model tends to generate a more stable atmosphere, limiting the model’s capability to reproduce deep convective events, resulting in smaller humidity and circulation anomalies in the forecasts compared to those in ERA-40.     

基于TIGGE资料的北半球地面气温预报的统计降尺度研究

基于TIGGE(THORPEX Interactive Grand Global Ensemble,全球交互式大集合)资料中欧洲中期天气预报中心(European Centre for Medium-Range Weather,ECMWF)、日本气象厅(Japan Meteorological Agency,JMA)、美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)和英国气象局(United Kingdom Met Office,UKMO)4个中心的北半球地面2 m气温集合平均预报资料,利用插值技术与回归分析,并引入了消除偏差集合平均(bias-removed ensemble mean,BREM)和多模式超级集合(superensemble,SUP)方法进行统计降尺度预报研究。结果表明,在2007年夏季3个月中,4个单中心的降尺度预报明显地改善了预报效果。引入SUP和BREM两种集成预报方法后,预报误差得到进一步减小。对比综合表现最好的单中心ECMWF的预报,1~7 d的降尺度预报误差改进率均达20%以上。研究还发现,引入SUP方法的降尺度预报效果优于引入BREM方法的降尺度预报,利用双线性插值方法在上述两方案中的预报效果优于其他3种插值方法。     

Western North Pacific Tropical Cyclone Intensity Guidance Evaluations Using an Alternative Verification Technique

In this study, six intensity forecast guidance techniques from the East China Regional Meteorological Center are verified for the 2008 and 2009 typhoon seasons through an alternative forecast verification technique. This technique is used to verify intensity forecasts if those forecasts call for a typhoon to dissipate or if the real typhoon dissipates. Using a contingency table, skill scores, chance, and probabilities are computed. It is shown that the skill of the six tropical cyclone intensity guidance techniques was highest for the 12-h forecasts, while the lowest skill of all the six models did not occur in 72-h forecasting. For both the 2008 and 2009 seasons, the average probabilities of the forecast intensity having a small error (6 m s-1) tended to decrease steadily. Some of the intensity forecasts had small skill scores, but the associated probabilities of the forecast intensity errors > 15 m s-1 were not the highest.     

邻域空间检验技术在集合降水预报检验中的应用

常规降水检验受空间及时间微小差异所带来的"双重惩罚"影响严重,邻域空间检验FSS(Fraction Skill Score)方法在确定性预报中已体现出弥补这一不足的明显优势。随着集合预报分辨率的不断提高,集合降水预报同样存在与确定性预报相似的问题。本研究将FSS方法拓展至集合预报领域,构建适用于集合预报的降水空间检验指标EFSS(Ensemble Fraction Skill Score),利用欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)集合预报模式2018年夏季降水预报产品及国家气象信息中心提供的格点化降水融合产品进行分析,发现EFSS评分不受集合成员数影响,可获取一致性的评估结论。通过与适用于集合预报的常规技巧评分EETS(Ensemble Equitable Threat Score)对比分析发现,常规技巧评分受限于评分过低而无法有效反映强降水过程间差异性特征,EFSS方法则可有效提升强降水预报检验辨识度。     

Multimodel based superensemble forecasts for short and medium range NWP over various regions of Africa

Summary This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis. The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase. This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and all the members. Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within the ITCZ over the region. These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range weather forecasts in real-time.     

ENSO预测的目标观测敏感区在热带太平洋海温的多模式集合预报中的应用

本文将ENSO预测的目标观测敏感区与多模式集合预报方法相结合, 提出了一种能够有效提高预报技巧且又具有较小计算成本的多模式集合预报方法。该方法在目标观测敏感区内采用模式不等权的多模式超级集合预报方法(SUP), 而在其他区域采用相对简单的等权的多模式消除偏差集合平均方法(BREM)。利用CMIP5中15个气候系统模式的工业革命前参照试验(pi-Control)数据, 针对热带太平洋海温的长期演变开展了理想预报试验。将新集合预报方法与现有的多模式集合预报方法进行了比较。结果表明, 在所考察的预报期内(即1～20年), 新集合预报方法与整个热带太平洋区域使用SUP方法具有相当的预报技巧, 但前者的计算成本明显小于后者, 计算时间仅为后者的1/4。可见, 新方法是一个具有较高预报技巧且计算成本较小的多模式集合预报方法。同时, 其较高的预报技巧强调了热带太平洋SST预测对ENSO目标观测敏感区内的模式误差也是极端敏感的, 也正因如此, 多模式集合预报方法才能够有效过滤模式误差的影响, 具有较高的预报技巧。     

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.     

两类天气预报评分问题研究及一种新评分方法

探讨了预报评价的意义及应遵循的原则, 对常用的几种两类预报评分方法进行分析, 指出其应用的局限性, 得到一个判定所作预报水平是否高于随机预报、具有预报技巧的简易判别式; 提出评分权重的概念, 指出以往评分存在问题的根源是评分权重分配不当, 使评分结果的真实性受到影响, 评分无可比性, 进而提出一种考虑了评分权重的新评分方法。新评分方法满足预报评价的原则, 侧重于对两类事件中事件概率较小一方预报效果的评估, 评分结果不受事件概率影响, 具有可比性。对比分析表明:新方法比其他方法优越, 能更准确地反映预报水平, 使不同季节、不同地域的预报评分可进行比较, 是一个通用的评分方法。