基于位置误差的分布制作热带气旋路径袭击概率预报

为了探讨热带气旋路径预报的不确定性,利用2004-2007年4年中央气象台综合预报的误差分布状况,建立热带气旋路径综合预报的统计集合模型,制作热带气旋路径的袭击概率预报及产品,并为警报发布工作中有关登陆地段的预报提供有意义的参考.     

登陆广东北上路径热带气旋强降水的分布特征

利用1981-2018年中国气象局上海台风研究所热带气旋最佳路径资料和国家基本气象站逐日降水资料,对登陆广东北上路径热带气旋活动及强降水特征进行统计分析.结果表明北上热带气旋从登陆时到登陆后48 h是陆地强降水产生的集中时段,登陆时24 h是陆地大暴雨最集中的时段.强降水从沿海向内陆逐渐减少,一般登陆后热带气旋持续时间...     

近50a广东登陆热带气旋的若干气候特征

利用《台风年鉴》／《热带气旋年鉴》[3，4]和广东降水资料，研究了近52a(1949～2000年)来广东登陆热带气旋的时、空变化特征。结果表明，52a间，登陆广东的热带气旋总数占全国总数的38％，居沿海各省之首。每年可有1到7个数量不等的热带气旋登陆广东，年平均数为3．9个。小波分析结果表明，至少对于广东的登陆热带气旋而言，70年代中期是一个明显的气候跃变期。前期(1949～70年代中)，登陆热带气旋数存在三种主要变化周期，即准3a(2～4a)、准7a(6～8a)和准12a(10～16a)周期的变化；后期(70年代中～2000年)，主要的变化周期为4～6a和7～9a。在年代际尺度上，登陆广东的热带气旋较多的时段为：1949～1954年、1959～1965年、1972～1976年1982～1987年和1991～1996年等。登陆广东的热带气旋总数与广东7～9月累计雨量有较类似的变化趋势。热带气旋主要在6～9月登陆广东，高峰期是7～9月。热带气旋登陆地点分布特征是，西多东少，粤西(珠海及其以西)沿海登陆的热带气旋总数占广东登陆总数的63％。     

热带气旋登陆地段与湛江市风雨的相关统计分析

湛江市每年5～11月份都可受到热带气旋的影响,每个热带气旋造成风力、降水的大小,与热带气旋的登陆地段、登陆时的强度及登陆后的移动路径密切相关.本文利用50年来在广东台山至海南省南部沿海地区登陆或影响该地区的热带气旋资料与湛江市的风雨关系进行统计分析,从中找出相关规律,为热带气旋风雨预报提供参考.     

应用数值产品作热带气旋登陆地段释用预报

本方法是在对数值预告图作大量统计的基础上,将数值产品中５００ｈＰａ涡度场、实时５００ｈＰａ华南区域高度场、能量场,广东沿海各指标站气压变量等有机结合,制作出热带气旋未来３６～４８小时移动路径和可能登陆的地段。这是一种较客观的预报方法,简便易操作,自９０年代初使用至今,效果较好,尤其对一些路径异常的热带风暴,有很好的预报能力。１ 技术规定 （１）当热带气旋进人江门市热带气旋警戒区第二防区（或在该区域生成的热带气旋）作为进防。 （２）热带气旋进防当日为起报日,作出热带气旋移动路径和登陆点及登陆地段预报…     

应用指数曲线模式对热带气旋路径的模拟预报方法

应用非线性数理模型中指数曲线方法,对2000～2001年3个不同路径类型的热带气旋进行移动路径的模拟,并对结果进行了误差、登陆时间、地段以及转向点等分析,结果表明:热带气旋路径受许多确定和不确定因子影响,在方程拟合时非线性项起很大的作用,且拟合误差随k值的增大而增大,对路径复杂的热带气旋无法做出准确预报,但可以判断其转向趋势,仍不失为有效的模拟预报方法.     

0308号强热带风暴“天鹅”影响过程分析

利用天气图、卫星云图等资料，分析了0308号强热带风暴“天鹅”的高空环流形势及演变、双热带气旋的影响、日本24小时地面预报图和降水预报图以及FY-2云图，探讨了如何预报热带气旋的移动路径、登陆地点和时段，切实提高热带气旋活动的预报准确率。     

0308号强热带风暴"天鹅"影响过程分析

利用天气图、卫星云图等资料,分析了0308号强热带风暴"天鹅"的高空环流形势及演变、双热带气旋的影响、日本24小时地面预报图和降水预报图以及FY-2云图,探讨了如何预报热带气旋的移动路径、登陆地点和时段,切实提高热带气旋活动的预报准确率.     

热带气旋预报的人工神经网络方法

根据１９４９－１９８８年登陆珠江口的热带气旋资料，运用了人工神经网络的一个典型模型－“反向传播”模型，建立了该地区３５－４９小时热带气旋登陆地点的预报模型，并应用于热带气旋登陆地点的预报，即珠江口的热带气旋登陆地点预报，其拟合最大相对误差不超过０．７％。结果表明，神经网络预报模型具有容错能力强、预报速度快的特点，可望成为望带气旋预报的有效辅助手段。     

西太平洋热带气旋影响惠州的降水预报

众所周知，热带气旋会带来狂风暴雨，但每次对降水的影响程度有轻重之分，对登陆本地以外的热带气旋是否对本地有影响，影响程度如何又是一个问题。我们曾对这些问题进行过探索，并得出一些预报指标，现简单介绍如下。l几点说明1．1预报思路将海上划分为三个区，20”N以北、125”E以西为1区，17～20oN、120～125”E为11区，17～20oN、120oE以西为m区。当08时热带气旋进入这些区域内，当天做热带气旋登陆地段预报，并在此基础上分类做降水预报，预报的时段从登陆当天起48小时内。1．2热带气旋资料、图次和预报因子据统计，在22”N以北进入…     

登陆热带气旋路径和强度预报的效益评估初步研究

近年来有关热带气旋(TC)灾情的评估指标和方法的研究取得明显进展,但较少涉及TC预报对减少灾害损失的贡献(即效益)分析。基于中央气象台的TC实时路径和强度预报,针对登陆中国大陆的TC,初步分析了TC的路径和强度预报误差与其造成的直接经济损失之间的可能关系,并在此基础上建立了包含TC路径和强度预报误差的TC直接经济损失的预估模型。TC登陆前后24 h的路径和强度预报误差与TC所致直接经济损失均呈正相关关系;对于单个登陆TC而言,若24 h TC路径预报误差每减小1 km可减少因灾直接经济损失约0.97亿元,若强度预报每减小1 m/s可减少因灾直接经济损失约3.8亿元(以2014年为基准年)。可见,提高TC路径和强度预报精度对于减灾的效益巨大,且当前尤以提高强度预报能力的效益为佳。      

Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot(2009)Using WRF-based Ensemble Kalman Filter Data Assimilation

This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot(2009)using a WRF-based ensemble Kalman filter(EnKF)data assimilation(DA)system.The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone(TC).It was found that assimilating radial velocity(Vr)data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall.The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled.Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment.Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line.However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts.Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.     

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.     

An Evaluation of Tropical Cyclone Genesis Forecast over the Western North Pacificand the South China Sea from the CMA-TRAMS

Tropical cyclone (TC) genesis forecasting is essential for daily operational practices during the typhoon season.The updated version of the Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) offersforecasters reliable numerical weather prediction (NWP) products with improved configurations and fine resolution. Whiletraditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesisforecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims toevaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South ChinaSea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has beenproposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, pre dicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 hin advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors ofoperational models according to the annual evaluation. The study also briefly investigated the forecast of Noul (2011). Theforecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, con sistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized three dimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis,fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters.     

Improving Multi-Model Ensemble Forecasts of Tropical Cyclone Intensity Using Bayesian Model Averaging

This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging (BMA), aiming to improve the accuracy of tropical cyclone (TC) intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center (P_min). The multi-model ensemble comprises three operational forecast models: the Global Forecast System (GFS) of NCEP, the Hurricane Weather Research and Forecasting (HWRF) models of NCEP, and the Integrated Forecasting System (IFS) of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution (or probability density function, PDF) of P_min was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models varied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distribution transformation, provided more accurate forecasts than did the best of the ensemble members (HWRF), with a 5%–7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced “predictive variance” that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the potential to be applied to routine operational forecasting.     

全球数值模式中的台风初始化Ⅱ: 业务应用

由于缺少大量有效的观测资料,台风初始化对数值天气预报业务模式而言,仍然是一个悬而末决的难题.中国国家气象中心自从1996年将台风数值预报系统投入业务运行以来,一直使用经验的人造bogus涡旋台风初始化技术.实际上,不同时期的台风有着不同的环流结构,即使同一个台风在不同的生命期也具有不同的结构特征,而这些结构特征的差异并不能依靠现有的bogus涡旋技术体现出来,这种主观方法的统一性与台风在时空上的差异性形成了强烈的反差.最近,基于国家气象中心全球资料分析同化-预报循环系统,设计和发展了一套新的台风初始化业务方案,它主要由初始涡旋形成、涡旋重定位和涡旋调整3部分过程组成.相比于业务中使用的人造bogus涡旋台风初始化方案,新方案在很大程度上减少了人为因素对台风涡旋结构的影响,而更多地是依靠数值模式自身的动力和物理过程来协调约束产生三维空间的涡旋结构.应用新方案,文中对生成于西北太平洋的2006年0605号台风格美(Kaemi)进行了数值试验,初步分析表明,新方案在实现台风涡旋环流结构的初始化方面效果较好,同时,对台风格美多个时次的预报结果也显示,相比于业务使用的bogus方案而言,新方案对台风路径平均预报误差有了大幅度的降低.     

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model.     

登陆台风强度变化过程预报的影响试验研究

探索了基于WRF模式的集合卡尔曼滤波同化方法（WRF-EnKF,简称EnKF）在近海有可能达到更强台风连续循环同化中国大陆高时空分辨率多普勒天气雷达径向风观测资料的效果,同时检验台风Vicente（2012）的三维结构演变及其动力学特征。通过短期集合预报得到跟随当前流场变化着的背景误差协方差的台风涡旋和动力学结构。研究发现,EnKF同化预报系统能有效地同化高时空分辨率雷达径向速度观测资料,显著改善初始场中台风Vicente的中小尺度内核结构,同时提高对台风Vicente的路径和强度及其相伴随的短期强降水预报。在台风最强时刻同化雷达径向风观测能快速（1~2 h）得到真实的暖核台风结构,同时进一步提高台风路径和强度的预报。另外,EnKF同化雷达径向风观测资料还能有效提高短期降水预报,1 h和3 h累积降水的分布、降水中心以及降水随时间演变都能得到显著改善,这与改善台风路径、结构和强度有密切关系。因此,对中国东南沿海有可能达到较强的台风进行同化雷达径向风观测资料可改善登陆台风的预报水平,这为利用我国地基多普勒天气雷达观测资料改善模式的初始场从而提高台风预报提供一定的指示作用。      

基于BGM与ETKF的台风"苏拉"(1209)集合预报的对比试验Ⅰ:路径预报

采用FNL再分析资料和美国联合台风警报中心(Joint Typhoon Warning Center,JTWC)资料,运用中尺度WRF(Weather Research and Forecasting)模式,分别使用增长模繁殖法(Breeding of Growing Mode,BGM)和集合卡尔曼变换方法(Ensemble Transform Kalman Filter,ETKF),对1209号台风"苏拉"进行了台风路径的集合预报试验,并对预报效果进行对比分析。结果表明:采用BGM或ETKF初始扰动的集合预报系统,集合平均预报对风场、温度场、位势高度场的预报效果均优于控制预报;ETKF方法的预报改进程度较BGM方法更大,且对风场和温度场预报技巧的优势尤为明显。BGM方法所得到的集合成员离散度小于ETKF方法,对大气真实状态的表征能力不及后者;两种扰动方法的集合平均都明显改善了台风"苏拉"的路径预报结果,尤其是控制预报在福建沿海第二次登陆后移速过快的问题,但对台风登陆位置预报的改进不明显;此外,采用ETKF方法的集合平均对台风"苏拉"路径预报的改进效果远优于采用BGM方法的集合平均预报。     

Uncertainty in Tropical Cyclone Intensity Predictions due to Uncertainty in Initial Conditions

Focusing on the role of initial condition uncertainty,we use WRF initial perturbation ensemble forecasts to investigate the uncertainty in intensity forecasts of Tropical Cyclone(TC)Rammasun(1409),which is the strongest TC to have made landfall in China during the past 50 years.Forecast results indicate that initial condition uncertainty leads to TC forecast uncertainty,particularly for TC intensity.This uncertainty increases with forecast time,with a more rapid and significant increase after 24 h.The predicted TC develops slowly before 24 h,and at this stage the TC in the member forecasting the strongest final TC is not the strongest among all members.However,after 24 h,the TC in this member strengthens much more than that the TC in other members.The variations in convective instability,precipitation,surface upward heat flux,and surface upward water vapor flux show similar characteristics to the variation in TC intensity,and there is a strong correlation between TC intensity and both the surface upward heat flux and the surface upward water vapor flux.The initial condition differences that result in the maximum intensity difference are smaller than the errors in the analysis system.Differences in initial humidity,and to a lesser extent initial temperature differences,at the surface and at lower heights are the key factors leading to differences in the forecasted TC intensity.These differences in initial humidity and temperature relate to both the overall values and distribution of these parameters.