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。      

我国台风路径业务预报误差及成因分析

利用2005 2009年中国气象局(CMA)提供的西北太平洋(包括南海)台风路径业务预报资料,比较了各类型台风路径、台风登陆位置及登陆时间的预报误差,登陆台风不同阶段以及华东登陆和华南登陆台风的路径预报误差。结果表明:CMA在2005 2009年的路径预报水平与1999 2003年的相比有了显著提高。平均南海台风预报误差大于西北太平洋。异常路径台风主要出现于南海,三个预报时效(24、48和72 h)异常路径的预报误差平均都小于正常路径。将登陆台风分为远海、登陆期间和登陆后三个阶段,显示登陆期间台风预报误差最大,同一阶段华南登陆台风的预报误差大于华东登陆台风。台风登陆位置在24、48和72 h预报时效的平均预报误差分别为71.1、122.6和210.6 km,48和72 h台风实际登陆时间有70%早于预报时间,平均分别提早8和12 h。比较大尺度引导气流与台风移动的偏差及24 h路径预报误差,得到南海三种典型登陆台风路径的大尺度引导气流与台风移动的偏差及其与路径预报误差的关系不一样,即误差成因不同。南海倒抛物线型的大尺度引导气流与台风移动的偏差最大,其预报误差最小;西一西北型的大尺度引导气流与台风移动的偏差最小,其预报误差最大,可能与大尺度环流预报准确性差有关。登陆华东的预报误差小于登陆华南台风的预报误差,这与台风登陆华南时其大尺度引导气流和台风移动的偏差大于登陆华东的台风有关。     

2007年国内台风模式路径预报效果评估

使用中央气象台提供的台风中心定位报文资料、国内台风业务预报部门提供的台风模式路径预报报文资料和台风所气候持续性方法路径预报报文资料等,对2007年中央气象台编号的热带气旋对国内5种台风业务数值预报模式的路径预报效果进行了检验评估,检验内容主要包括距离误差、技巧评分和稳定度指标等.结果表明:(1)国内各模式24h/48h预报平均距离误差最小值为122.8km/246.3km,最大值为180.7km/304.4km.各模式24h/48h预报最大误差为1429.7km/1003.7km, 最小误差为11.2km/10.1km.24h/48h预报平均距离误差为147km/267km.平均而言,导致路径预报误差最大的是0707号热带气旋帕布,误差在其登陆后尤为明显.(2)相对于上海台风研究所路径预报气候持续法做了各种数值预报方法的技巧评分:各模式24h/48h预报平均技巧评分为32%/43%,最高的技巧评分为48%/54%, 最低的技巧评分为3.17%/33.53%.其中4个模式的24h/48h技巧评分高于36%/33%.(3)检验了距离稳定度、方向稳定度、有效稳定度等指标,以全面评估各模式的路径预报性能.该评估结果在一定程度上反映了当前国内台风路径数值预报相对于常用气候统计方法的优越性,同时也表明,尽管国内台风数值预报模式对于热带气旋在海上时的路径预报有一定的稳定性,但对热带气旋登陆后转向过程的预报表现出了较大误差.因此,在模式开发的下一步工作中,除台风初始化之外,还应结合登陆台风的特点,对边界层和陆面过程参数化等作针对性的研究.     

2017年西北太平洋台风活动特征和预报难点分析

利用历史台风最佳路径、中央气象台台风路径强度实时预报,以及ECMWF数值预报和NCEP海温实况等资料,对2017年西北太平洋台风活动的主要特征和预报难点进行了分析,结果表明：2017年台风生成具有源地偏西、南海台风偏多和台风群发特征明显等特征;台风活动具有年度活跃程度低、台风极值强度偏弱和超强台风异常偏少等特征;台风登陆具有登陆台风个数多、登陆地点偏南、登陆强度偏弱等特征。对2017年度的预报误差进行分析,结果显示：24、48、72、96和120 h台风路径预报误差分别为74、137、233、318、428 km,各时效误差均较2016年有所增加;但与日本、美国相比,除120 h外,中国路径预报水平依然处于领先地位。 24、48、72、96和120 h台风强度误差分别为3.6、5.4、6.6、7.4和6.8 m·s-1,较2016年有所减小,24 h误差为历史最低值。强度预报水平居于日本、美国之间。另外,2017年最主要的预报难点是双台风或多台风之间复杂的相互作用和近海快速加强台风的强度预报。     

2019年西北太平洋台风活动特征和预报难点分析

2019年在西北太平洋及南海共生成台风29个,比多年同期平均偏多2个,其中6个台风登陆我国,比多年平均偏少1个;台风整体强度偏弱,但全年最强台风夏浪极值强度达到68 m·s~(-1)(17级以上);登陆台风整体强度偏弱,但"利奇马"登陆强度强(52 m·s~(-1),超强台风级)、影响重;秋季台风生成数较常年明显偏多,尤其是11月生成台风数达到6个。2019年中央气象台台风路径预报平均误差与近5年(2014—2018年)的平均误差相比,在24～72 h的预报误差有所增大,而96～120 h的预报误差则明显减小,尤其是120 h的预报准确率创新高。与日、美官方预报相比,中国在24 h和96～120 h的预报水平处于领先地位,在48～72 h的预报误差比日本略高,但低于美国,与EC确定性模式相当。     

改进的物理过程参数化对台风路径数值预报的影响

文章介绍了国家气象中心的台风路径数值预报试验模式和新开发的改进物理过程参数化方案，对1992年的5个台风个例初步试验结果表明:改进物理过程参数化模式对台风路径和台风强度的预报比简单物理过程模式有明显提高，24 h和48 h台风中心位置预报误差分别为188 km和337 km，比原模式减小38 km和190 km；48 h台风中心气压与实况之差平均减小3 hPa。     

2013年西北太平洋热带气旋预报精度评定

本文以中国气象局上海台风研究所整编的最佳路径集为依据,对2013年西北太平洋热带气旋（TC）定位、路径和强度预报精度进行了评定,并对部分全球和区域模式在路径和强度预报中存在的系统性偏差进行了分析,结果表明：2013年定位总平均误差为21.7 km,比往年略偏小。国内各省(自治区)主观预报方法路径预报平均误差为80.2 km（24 h）、143.3 km（48 h）和221.7 km（72 h）,与2012年同比分别降低了13.9%、13.4%和20.9%,中央气象台24 h路径预报误差首次低于90 km。全球和区域模式的路径预报性能稳步提升,并表现出一定的系统性偏差。统计预报方法的强度预报整体性能仍然领先于数值模式,而在数值模式中,区域模式的强度预报性能则略优于全球模式。部分全球和区域模式在强度预报中也存在着系统性偏差。各主观方法对台风温比亚(1306)、台风尤特(1311)、台风潭美(1312)和台风菲特(1323)的24 h登陆点预报效果较好,而对台风西马仑(1308)的24 h登陆点预报不是十分理想。     

2012年中国南海台风模式预报情况

通过统计和评估中国南海台风模式2012年的预报结果,得出了2012年各种类型热带气旋的预报质量情况。由于在动力过程优化及水物质和边界层的改进方面做了不少工作,使得中国南海台风模式的全年平均距离误差第一次在历史上突破了100 km以下,且无论是24 h、48 h及72 h的路径还是强度预报都比历年要好很多,从2012年全年的台风路径预报效果来看,有一半的台风24 h路径预报误差在100 km以下大部分台风48 h路径预报误差在200 km以下。另外,2012年中国南海台风模式对强TC的路径预报仍然象往年一样要优于弱TC;而强度预报则相反,对于较弱的强热带风暴预报其效果反而最好。个例分析发现副高强度位置及相关的西伸活动对台风路径预报有较大影响;同时,减少模式位势高度的总体平均RMSE误差对台风路径预报准确率的提高有相当重要的作用。      

2012年西北太平洋热带气旋预报精度评定

本文对2012年西北太平洋热带气旋定位、路径和强度预报精度进行了评定,结果表明：2012年定位总平均误差23.4 km,与往年相当。国内各综合方法的路径预报平均误差分别为94.3 km（24 h）、168.2 km（48 h）和284.2 km（72 h）,中央气象台24 h路径预报准确率相对于2011年有了较大幅度的提高。全球模式的平均距离误差分别为96.8 km（24 h）、177.2 km（48 h）、283.6 km（72 h）、382.3 km（96 h）和583.6 km（120 h）,其中部分数值模式预报水平接近主观方法的平均预报水平,但是最优的主观预报具有相对所有数值预报的正技巧,表现出较强的数值预报应用能力。通过对比国际先进数值预报模式的误差表明,国内区域模式的路径预报能力与国外先进模式相比仍有较大差距。4个台风业务中心强度预报的平均绝对误差分别为4.11~4.63 m·s-1（24 h）、6.10~6.90 m·s-1（48 h）和6.84~8.71 m·s-1（72 h）。部分客观强度预报方法表现出一定的系统性偏差。各方法对“海葵”在象山县鹤浦镇的24 h登陆点预报较为成功,而对“苏拉”在台湾花莲的24 h登陆点预报较为失败。     

2014年西北太平洋热带气旋预报精度评定

本文以中国气象局上海台风研究所整编的最佳路径集为依据,对2014年西北太平洋热带气旋（TC）定位、路径、登陆点和强度预报精度进行了评定,结果表明：2014年定位总平均误差25.3 km,比2013年略偏大。CMA主观预报24、48、72、96和120 h路径预报误差分别为84.3、145.6、205.4、280.2和415.3 km,与2013年相比,长时效路径预报误差有显著减小。而全球模式在相应预报时效的总平均路径误差分别为88.1、159.6、253.9、393.6和572.1 km。区域模式24、48和72 h的总平均路径误差分别为97.4、188.2和302.7 km。统计预报方法的强度预报整体性能仍然领先于数值模式,而在数值模式中,区域模式的强度预报性能则略优于全球模式。     

VERIFICATION OF TYPHOON FORECASTS BY THE GRAPES MODEL

Four landfalling typhoon cases in 2005 were selected for a numerical simulation study with the Global/Regional Assimilation and Prediction System (GRAPES) model. The preliminary assessment results of the performance of the model, including the predictions of typhoon track, landfall time, location and intensity, etc., are presented and the sources of errors are analyzed. The 24-hour distance forecast error of the typhoon center by the model is shown to be about 131 km, while the 48-hour error is 252 km. The model was relatively more skilful at forecasts of landfall time and locations than those of intensity at landfall. On average, the 24-hour forecasts were slightly better than the 48-hour ones. An analysis of data impacts indicates that the assimilation of unconventional observation data is essential for the improvement of the model simulation. The model could also be improved by increasing model resolution to simulate the mesoscale and fine scale systems and by improving methods of terrain refinement processing.     

GRAPES气象-水文模式在一次洪水预报中的应用

尝试将GRAPES (Global-Regional Assimilation and PrEdiction System) 模式与水文模型结合,构建GRAPES气象-水文单向耦合模式,进行洪水预报。气象模式选取GRAPES_Meso模式,分别采用15 km×15 km和5 km×5 km水平分辨率,15 km×15 km的GRAPES模式由NCEP全球预报场提供初始场和侧边界条件;5 km×5 km的GRAPES模式由15 km×15 km GRAPES模式提供初始场和侧边界条件,将GRAPES_Meso模式的定量降水预报分辨率统一降尺度到5 km×5 km分辨率,用于驱动水文模式。水文模型选取新安江模型与分布式新安江模型。以淮河王家坝站以上流域和息县流域为试验流域,将GRAPES降水预报场驱动水文模型进行单向耦合,构建GRAPES气象-水文单向耦合模式,选择2009年8月28日08:00(北京时,下同)—9月9日14:00汛期一次洪水过程,进行实际预报试验。结果表明:15 km×15 km和5 km×5 km的GRAPES模式预报降水与实况降水分布相一致;与水文站观测降水驱动水文模型洪水模拟结果相比,GRAPES气象-水文模式对洪水预报的预见期延长效果明显,对洪水模拟精度也较高,与水文模型输入场分辨率要求相匹配的降水产品对洪水模拟的精度更高。     

Impacts of a Large-scale Adaptive Blending Scheme for CMA-MESO on Regional Forecasts-A Case Study of Typhoon Haima

Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone (TC). However, it is difficult to be fully represented in regional models due to domain size and a lack of observation data, particularly at sea used in regional data assimilation. Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis. Research of the impact of this large-scale blending scheme for the Global / Regional Assimilation and PrEdiction System (CMA-MESO) regional model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform (2D-DCT) filter on the model forecast of Typhoon Haima over Shenzhen, China in 2016 and considering various cut-off wavelengths. Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme, indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields. The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors (RMSEs) by comparing the experiments with and without blending analysis. Furthermore, the higher equitable threat score (ETS) provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the CMA-MESO analysis. Furthermore, significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast. It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast. In this paper, the methods and data applied in this study will be firstly introduced, before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly, followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section.     

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.     

中国数值天气预报的自主创新发展

数值天气预报是天气预报业务和防灾、减灾的核心科技。中国数值天气预报研究和业务应用一直受到高度重视,在理论、方法和数值模式研究方面取得了有广泛国际影响的研究成果。在回顾新中国数值天气预报自主创新研究成果的基础上,重点对GRAPES（Global Regional Assimilation and PrEdiction System）半隐式半拉格朗日格点模式与物理过程的研发和业务应用的状况以及所取得的重要科学进展进行了综述。近年来,通过自主研发建立了中国数值天气预报业务体系—GRAPES体系。首次以自主技术实现了从区域3—10 km到全球25—50 km分辨率的确定性预报和集合预报系统,并在模式动力框架、四维变分同化和卫星资料同化技术等方面有所突破,建立了大气化学数值天气预报、台风数值预报和海浪预报等系统。自主研发的数值天气预报体系的建立是长期坚持既定科学技术方向以及研究和业务紧密结合、经验不断积累的结果,是中国自主发展数值天气预报技术的重要起点。      

2013年华中区域中尺度业务数值预报的客观检验

应用国家气象中心模式检验方法,对华中区域中尺度业务数值预报模式WRF和中国气象局下发的GRAPES模式2013年1-12月的预报产品（包括降水、2 m温度场、850 hPa温度场、850 hPa风场和500 hPa位势高度场预报）进行统计检验。检验结果表明：所有要素24 h预报均优于48 h;对于晴雨预报,GRAPES模式TS评分高于WRF,但随着降水量级增大,WRF的TS评分基本都高于GRAPES,同时WRF降水预报范围明显偏大;分析2 m温度场的均方根误差及预报准确率发现,WRF的2 m温度场预报优于GRAPES,且暖季预报优于冷季;形势场要素分析表明,WRF对850 hPa温度场和风场预报具有相当大的优势,全年误差变化较稳定,而对500 hPa位势高度场的预报误差存在一定的季节性特征,即夏半年WRF高度场预报优于冬半年,GRAPES模式则相反。总体上,华中区域中尺度业务数值模式产品对天气预报具有一定的参考价值。     

Early Flood Warning for Linyi Watershed by the GRAPES/XXT Model Using TIGGE Data

Early and effective flood warning is essential for reducing loss of life and economic damage.Three global ensemble weather prediction systems of the China Meteorological Administration (CMA),the Europe...     

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty

This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II. Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60°S and 60°N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.     

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。