The Uncertainty of Tropical Cyclone Intensity and Structure Based on Different Parameterization Schemes of Planetary Boundary Layer

Based on different parameterization schemes of planetary boundary layer (PBL), the uncertainty of intensity and structure of the Super-strong Typhoon Rammasun (1409) is investigated using the WRF model (v3.4) with six PBL parameterization schemes. Results indicate that PBL uncertainty leads to the uncertainty in tropical cyclone (TC) prediction, which increases with forecast time. The uncertainty in TC prediction is mainly reflected in the uncertainty in TC intensity, with significant differences in the TC intensity forecasts using various PBL schemes. The uncertainty in TC prediction is also reflected in the uncertainty in TC structures. Greater intensity is accompanied by smaller vortex width, tighter vortex structure, stronger wind in the near-surface layer and middle and lower troposphere, stronger inflow (outflow) wind at the lower (upper) levels, stronger vertical upward wind, smaller thickness of the eye wall, smaller outward extension of the eye wall, and warmer warm core at the upper levels of eye. PBL height, surface upward heat flux and water vapor flux are important factors that cause the uncertainty in TC intensity and structure. The more surface upward heat flux and water vapor flux and the lower PBL height, the faster TC development and the stronger TC intensity.     

Effects of a Dry-Mass Conserving Dynamical Core on the Simulation of Tropical Cyclones

The accurate forecasting of tropical cyclones(TCs) is a challenging task. The purpose of this study was to investigate the effects of a dry-mass conserving(DMC) hydrostatic global spectral dynamical core on TC simulation. Experiments were conducted with DMC and total(moist) mass conserving(TMC) dynamical cores. The TC forecast performance was first evaluated considering 20 TCs in the West Pacific region observed during the 2020 typhoon season. The impacts of the DMC dynamical core on forecasts o...     

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.     

数值模式的热带气旋强度预报订正及其集成应用

提供热带气旋强度预报产品的业务数值天气预报模式有很多,并已表现出一定的预报技巧,为提高对模式热带气旋强度预报产品的定量应用能力,分析2010—2012年7个业务数值模式的西北太平洋热带气旋强度预报,发现预报误差不仅受到模式热带气旋初始强度误差的显著影响,还与热带气旋及其所处环境的初始状况有密切关系,包括热带气旋初始强度、尺度、移速、环境气压、环境风切变、热带气旋发展潜势等。根据这些因子与各模式热带气旋强度预报误差之间的相关性,采用逐步回归方法建立热带气旋强度预报误差的统计预估模型,并通过逐个热带气旋滚动式建模来进行独立样本检验。检验结果表明,基于误差预估的模式订正预报比模式直接输出的热带气旋强度预报有显著改进,在此基础上建立的热带气旋强度多模式集成预报方案相对气候持续性预报方法在12 h有28%的正技巧,在24—72 h则稳定在15%—20%,具有业务参考价值。     

热带气旋奇异向量在GRAPES全球集合预报中的初步应用

基于副热带奇异向量的初值扰动方法已应用于GRAPES （Global and Regional Assimilation PrEdiction System）全球集合预报系统,但存在热带气旋预报路径离散度不足的问题。通过分析发现,热带气旋附近区域初值扰动结构不合理导致预报集合不能较好地估计热带气旋预报的不确定性,是路径集合离散度不足的可能原因之一。通过建立热带气旋奇异向量求解方案,将热带气旋奇异向量和副热带奇异向量共同线性组合生成初值扰动,以弥补热带气旋区域初值扰动结构不合理这一缺陷,进而改进热带气旋集合预报效果。利用GRAPES全球奇异向量计算方案,以台风中心10个经纬度区域为目标区构建热带气旋奇异向量求解方案,针对台风“榕树”个例进行集合预报试验,并开展批量试验,利用中国中央气象台最优台风路径和中国国家气象信息中心的降水观测资料进行检验,对比分析热带气旋奇异向量结构特征和初值扰动特征,评估热带气旋奇异向量对热带气旋路径集合预报和中国区域24 h累计降水概率预报技巧的影响。结果表明,热带气旋奇异向量具有局地化特征,使用热带气旋奇异向量之后,热带气旋路径离散度增加,路径集合平均预报误差和离散度的关系得到改善,路径集合平均预报误差有所减小,集合成员更好地描述了热带气旋路径的预报不确定性;中国台风降水的小雨、中雨、大雨、暴雨各量级24 h累计降水概率预报技巧均有一定提高。总之,当在初值扰动的生成中考虑热带气旋奇异向量后,可改进热带气旋初值扰动结果,并有助于改善热带气旋路径集合预报效果。      

陆面特征量初始扰动的敏感性及集合预报试验

文章利用中尺度模式Weather Research and Forecasting Model(WRF)3.2.1版本及National Centers for Environmental Prediction(NCEP)分析资料,研究了陆面变量(土壤湿度、土壤温度)和陆面参数(植被覆盖率)初始场随机扰动对长江中下游暴雨预报的影响并进行了集合预报试验。试验结果表明,短期暴雨过程对陆面变量(参数)扰动是敏感的;陆面变量(参数)初始场扰动影响降水的时间尺度小于10 h甚至可以小于6 h。从影响机理上来看,陆面变量(参数)扰动首先改变地表的潜热通量和感热通量,而地表通量的改变会通过陆气相互作用对局地大气的温、压、湿、风产生较大影响,从而对暴雨的强度和落区产生较大影响。集合预报结果表明,利用陆面变量(参数)扰动制作集合预报,预报的集合平均结果要好于控制预报的结果,且比集合成员稳定可靠,降水概率预报可以提供一些有用的信息,对预报强降水有一定的指示意义。在初值集合预报中,以这些参数或变量的扰动来引进集合成员是十分有意义的。     

2020年梅汛期强降水事件的预报误差来源分析

基于WRF模式（Weather Research and Forecasting Model）分析2020年超长梅汛期内11次强降水事件的预报误差来源。分别以FNL（Final Global Data Assimilation System）、TIGGE_EC（THORPEX Interactive Grand Global Ensemble from European Centre for Medium-Range Weather Forecasts）作为初始场进行预报,对比预报结果发现,TIGGE_EC初始场的预报结果普遍优于FNL,这说明初始条件的不确定性对预报结果有重要影响。进一步探究初始条件不确定性（初始误差）来源的区域（敏感区）和变量（敏感变量）发现,敏感区集中分布于降水区西侧上游,相对应的敏感变量为水汽场。分别考察动能、有效位能以及比湿能在初始误差总能量中的占比,结果表明,扰动比湿能占比最小,但敏感性试验 表明比湿场扰动对预报效果的影响最大。选取比湿场扰动对预报效果影响最大且WRF_EC具有更好预报效果的6个暴雨事件,通过HYSPLIT后向轨迹模式分别追踪其累计降水量最大值点的水汽来源及路径发现,有6个事件均有向降水区西侧上游延伸的水汽来源通道,进一步表明了敏感区的初始水汽场的准确性对暴雨预报的影响。因此降水区西侧上游的水汽场的误差是这11次梅汛期暴雨过程重要的预报误差来源,对其准确描述可有助于预报效果的提升。     

Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations

This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error.     

Possible sources of forecast errors generated by the global/regional assimilation and prediction system for landfalling tropical cyclones. Part I: Initial uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). The GRAPES forecasts were made for 16 landfalling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions (“initials”, hereafter), which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.     

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅰ: Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). The GRAPES forecasts were made for 16 landfalling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions("initials", hereafter), which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.     

Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF

The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution.     

Impact of Soil Moisture Uncertainty on Summertime Short-range Ensemble Forecasts

To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts(SREFs), a fivemember SREF experiment with perturbed initial soil moisture(ISM) was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 h Pa, and produced skillful ensemble-mean 24-h accumulated precipitation(APCP24) forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.     

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.     

上海区域数值预报模式集合预报系统的建立与试验

以目前运行的上海区域业务数值预报模式为基础,从预报模式的不确定性出发构造8个预报成员,建立了上海区域数值预报模式集合预报系统的初步模型,并对2004年夏季进行了逐日48 h预报试验。结果表明:集合平均对华东地区城市降水、温度、海平面气压等气象要素的总体预报能力与分辨率高3倍的业务模式相当,其中对雨量较大降水、最低温度、海平面气压(0~24 h)的预报效果好于业务模式;集合预报还能提供客观化、定量化的降水概率预报,对降水的发生、尤其是特大降水的发生有着很好的提示作用。     

潜热加热纳近方法在地面降水资料同化中的应用

基于国家气象中心GRAPES_Meso高分辨率区域模式,针对中尺度数值预报模式中预报雨带形成滞后问题,研究了潜热加热纳近方法在地面降水资料同化中的应用,以期提高短时数值天气预报的水平。2013年6月20日—7月20日的初步试验结果表明:通过调整模式潜热加热廓线,可以改进初始场中温、湿、风等要素的合理分布,增加降水区的对流不稳定性;潜热加热纳近方法可以缩短模式的调整适应 (spin-up) 时间,改进短时降水预报的落区和强度,提高3 h,6 h,12 h的降水预报TS,ETS评分;与传统的冷潜热加热纳近的试验结果相比,改进的暖潜热加热纳近试验对降水落区和强度的预报更接近观测,但强降水中心范围略大。     

GRAPES区域集合预报系统对登陆台风预报的检验评估

针对2015年7—9月登陆中国大陆沿海的台风,利用GRAPES-REPS区域集合预报资料和集合统计诊断分析方法,对登陆台风的移动路径、时间、地点、强度和降水等进行检验评估,以期为预报员应用GRAPES登陆台风概率预报提供依据。检验结果表明,(1)集合平均移动路径要优于控制预报,集合预报各成员登陆地点存在20~340 km差异,但实况登陆地点均能落在集合成员登陆地点中。(2)对24 h和48 h登陆地点误差而言,集合平均较控制预报更接近实况。(3)随着预报时间的趋近,集合平均、控制预报和集合成员登陆地点距离误差逐渐缩小,登陆地点空间位置预报也没有明显的系统性误差。(4)集合成员对台风登陆时间预报偏早,平均提前2.3 h。(5)在强度预报中,尽管最低气压和近中心最大风速存在登陆前偏弱而登陆后偏强的趋势,但登陆点预报值区间包含了实况观测值,表明GRAPES-REPS集合预报能够较好展示多种可能信息。(6)不同量级降水AROC评分为0.56~0.76,具有预报参考价值;另外AROC评分的高低及台风暴雨落区的准确性与台风登陆点和登陆时间误差密切相关。可见,GRAPES-REPS区域集合预报可以在台风登陆地点、时间、强度和降水预报等方面提供更多的预报不确定性信息,有助于做出正确的预报决策。      

Impact of Different Cloud Microphysics Parameterization Schemes on Typhoon Intensity and Structure

The impact of different cloud microphysics parameterization schemes on the intensity and structure of the Super-strong Typhoon Rammasun (1409) in 2014 is investigated using the Weather Research and Forecasting model version 3.4 with eight cloud microphysics parameterization schemes. Results indicate that the uncertainty of cloud microphysics schemes results in typhoon forecast uncertainties, which increase with forecast time. Typhoon forecast uncertainty primarily affects intensity predictions, with significant differences in predicted typhoon intensity using the various cloud microphysics schemes. Typhoon forecast uncertainty also affects the predicted typhoon structure. Greater typhoon intensity is accompanied by smaller vortex width, tighter vortex structure, stronger wind in the middle and lower troposphere, greater height of the strong wind region, smaller thickness of the eyewall and the outward extension of the eyewall, and a warmer warm core at upper levels of the eye. The differences among the various cloud microphysics schemes lead to the different amounts and distributions of water vapor and hydrometeors in clouds. Different hydrometeors have different vertical distributions. In the radial direction, the maxima for the various hydrometeors forecast by a single cloud microphysics scheme are collocated with each other and with the center of maximum precipitation. When the hydrometeor concentration is high and hydrometeors exist at lower altitudes, more precipitation often occurs. Both the vertical and horizontal winds are the strongest at the location of maximum precipitation. Results also indicate that typhoon intensities forecast by cloud microphysics schemes containing graupel processes are noticeably greater than those forecast by schemes without graupel processes. Among the eight cloud microphysics schemes investigated, typhoon intensity forecasts using the WRF Single-Moment 6-class and Thompson schemes are the most accurate.     

EFFECT OF INITIAL PERTURBATION OF LAND SURFACE PROCESSES ON TROPICAL CYCLONE FORECAST

Many studies have explored the importance and influence of planetary boundary layer processes on tropical cyclones (TCs). However, few studies have focused on the influence of land surface processes on the activity of TCs. To test the effect of initial perturbations of land surface processes on TCs, a land surface process perturbation module is built in a global ensemble prediction system. Ensemble experiments for the TCs that occurred from 12 UTC 22 August to 18 UTC 24 November, 2006 show that consideration of the uncertainties within the land surface process could increase the predictability of the global ensemble prediction system. Detailed analysis on TC Xangsane (2006) indicates that the perturbation of land surface processes may increase the variation of sensible heat flux and latent heat flux. Meanwhile, the effect from land surface perturbation can be transferred to the upper atmosphere, which leads to better TC forecasts.     

对流尺度数值预报中的云物理初始化方法改进及个例试验

通过在云初始化方案中增加由地表感热和潜热通量确定的对流尺度速度作为对流判据,同时增加层云云冰、云水计算方案,改进云分析方法,并基于第2代华东快速更新循环同化模式预报系统,针对2015年4月28日华东强对流个例,进行对比试验,分析了改进的云初始化方案对云分析结果和模式预报效果的影响。试验表明:在云分析中增加对流判据,使得平均40%左右的云分析格点判定为非对流格点,对流格点分布与正的感热通量分布相似,在陆地上有显著日变化。在对流和层云格点判定之后,增加层云云冰、云水计算方案分析层云格点,显著地减小了模式初始场的云冰、云水混合比,有效地减弱了模式积分初始阶段云冰、云水含量的剧烈调整,尤其是在陆地区域。采用改进的云初始化方案进行预报,可以减少模式前1 h和前6 h的降水强度;尤其在个例的循环试验中,强降水中心强度和面积的预报比原方案显著减弱。      

西北太平洋(含南海)热带气旋路径集成预报分析

基于2004—2009 年中国中央气象台、日本气象厅、美国联合台风警报中心、欧洲中心对西北太平洋和南海编号热带气旋主客观预报资料,利用算术平均、多元回归以及历史平均误差等三种集成方法,建立了热带气旋路径集成预报业务化系统。通过2007—2009 年的业务运行结果分析发现,欧洲中心客观预报参与的24、48 和72 h 集成比主观预报三个成员集成预报水平分别提高约2%、3%～5%和3%～5%,减小误差2.5 km左右、6～9 km 和10～12 km。技巧分析发现,24～72 h 集成预报有正技巧,多元回归集成技巧相对稍低,而算术平均和以各成员平均误差的平方倒数为权重系数的集成技巧对于各集成成员来说技巧差异不大。96 h 集成预报对欧洲中心的客观预报没有正技巧。