基于GFS预报资料的强对流相似预报研究（英文）

本文基于数值模式预报发展了强对流相似预报方法。该方法采用对流参数作为相似预报量,对比当前预报与历史预报的相似物理量场挑选与当前预报最相似的时刻,并利用这些时刻对应的强对流观测权重叠加作为当前预报时刻的强对流预报。以NCEP/GFS预报场资料,对该方法的有效性进行了检验。检验结果表明,该方法对中国地区的强对流有良好预报效果。作为数值预报的有效补充,该方法具有以下几点优势:简便易实现,具有很高的可移植性:以对流参数作为相似预报量,充分利用了模式对温,压,湿,风等基本气象要素预报比较准确的优势;有效避免了对流参数进行强对流预报时的临界值选取问题,考虑了强对流发生的地域性,气候性特点。     

一种台风过程雨量的相似预报方法

提出一个台风过程雨量的定点、定量预报方法。方法应用台风初始参数、历史过程的天气形势场和物理量场及数值预报产品,构造预报区域内当前时刻至未来时刻环境要素场的多元客观相似判据。通过定义非线性的相似指数,综合评估历史台风样本与预报台风在多元判据下的连续动态相似程度,以此找到相似样本。应用相似样本的历史雨量记录进行相似指数的权重综合,得到台风未来雨量的定点、定量预报值。预报试验表明该方法具有一定的预报技巧。     

基于关键对流参数分级的强对流潜势预报

通过对上海地区1998—2009年4—9月各类强对流天气的统计分析,选取42个对流参数及其时间变量,采用逐步回归方法建立了针对各类强对流天气的0～12h潜势预报方程。在此基础上,提出了基于关键对流参数进行分级的强对流潜势预报方法,选取K指数、SI指数、PWV(大气可降水含量)指数和θsedif85(500hPa和850hPa假相当位温差)等反映大气热力和水汽条件的关键对流参数,根据对流分布情况将各对流参数分别分为3个等级,并分级建立了针对不同强对流天气的潜势预报方程。与未分级方程对比表明:基于关键对流参数分级的预报方程对雷雨大风、强雷电和所有对流等预报效果上有明显提升,采用如下组合评分更佳:雷雨大风的预报采用SI分类方程,强雷电和所有对流采用PWV分类方程。将基于关键对流参数分级的强对流潜势预报方法在数值预报模式中进行了业务应用,取得了较好效果。     

对流能量计算及强对流天气落区预报技术研究

文章分析了两种典型的大气湿绝热过程及其处理方法，对大气对流能量参数的计算技术进行了研究。结合实际个例，利用可逆饱和绝热过程，对包含液态水重力拖曳作用的修正对流有效位能(MCAPE)和修正下沉对流有效位能(MDCAPE)进行了定量计算。文章结合数值模式输出探空分析，预报不稳定和对流能量的区域分布，在此基础上建立了综合多指标叠套强对流天气落区预报方法，用MM5 及国家气象中心T106模式输出及诊断产品预报强对流天气落区，并检验强对流落区预报技术。     

长江中下游汛期降水数值预报业务模式误差场预报研究

根据中国国家气候中心(NCC)数值预报业务模式(ONPM)预报结果,利用气候因子对业务模式的误差场进行预报试验。文中所用114项逐月气候因子在历年汛期前期总会出现部分因子异常的状况,在此基础上对因子异常的相似阈值进行数值试验,提出利用交叉检验平均距平相关系数(ACC)的大小来确定相似阈值的方法。依此选择影响该区域的前期关键异常因子,根据该部分因子的相似程度选取相似年,同时对模式误差场利用经验正交函数压缩维度,用前3个主分量对模式误差制作预报,针对业务模式的预报误差场,提出了根据因子异常挑选相似和压缩维度的一个预报方法。2005—2009年独立样本回报结果表明,该方法可以将5a平均距平相关系数由系统误差订正的0.22提高到0.47,具有较好的业务应用价值。     

热带气旋定量降水预报的动力相似方案

该文提出一个热带气旋定量降水预报(QPF) 的动力相似方案。方案应用热带气旋初始参数、历史过程的天气形势场和物理量场及数值预报产品, 构造预报区域内当前时刻至未来时刻环境要素场的多元客观相似判据。通过定义非线性的相似指数综合评估历史热带气旋样本与预报热带气旋在多元判据下的连续动态相似程度, 以此找到相似样本。应用相似样本的历史降水量记录进行相似指数的权重综合, 得到热带气旋未来6~48 h降水量的定点、定量预报值。预报试验表明该方案具有一定的预报技巧。     

强对流天气监测预报预警技术进展

强对流天气预报业务包括监测、分析、预报、预警和检验等方面。对流初生识别、对流系统强度识别和对流天气类型识别等监测技术取得新进展,综合多源资料的监测技术已应用于中国气象局中央气象台业务。对流系统的触发、发展和维持机制等获得了新认识,我国不同类型强对流天气及其环境条件统计气候特征、分析规范及相应业务产品等为业务预报提供了必要基础和技术支撑。光流法、多尺度追踪技术以及应用模糊逻辑方法的临近预报技术等有明显进展,融合短时预报技术得到广泛应用,对流可分辨高分辨率数值 (集合) 预报及其后处理产品预报试验取得了显著成效,基于数值 (集合) 预报应用模糊逻辑方法的分类强对流天气短期预报技术为业务预报提供了技术支撑。强对流天气综合监测和多尺度自适应临近预报技术、多尺度分析技术以及融合短时预报技术、发展并应用模糊逻辑等方法的、基于高分辨率数值 (集合) 模式的区分不同强度等级和极端性的分类强对流天气精细化 (概率) 预报技术等是未来发展的主要方向。     

基于BJ-RUC系统的临近探空及其对强对流发生潜势预报的指示性能初探

缺乏对流天气发生临近时刻的探空观测资料是制约强对流短时预报水平进一步发展的重要因素.随着数值预报技术水平的持续提高,高时空分辨率数值预报输出的模式探空性能越来越好,这为获取对流发生前的临近探空提供了有益的补充.本文针对北京地区2008年夏季20次较强的对流过程,得出北京市气象局BJ-RUC数值预报系统输出的54511站在分析时次(t=0)和预报时效分别为3和6 h的预报偏差均略差于t=0的分析探空,但是此两个时次的预报探空误差仍然处于可接受的范围内.对比20个对流个例54511站分别基于观测和分析或预报的对流不稳定能量、水平风的垂直切变等基本对流参数可以发现t=0时次模式分析探空在趋势和量级上与基于实际探空观测诊断的对流参数均有较好的一致性,基本上能够较为正确地在模式中反映临近对流发生前实际大气的环境特征;但是当考虑实际的预报时效时,t=3和6 h的预报探空能够提前约1和4 h为预报员提供对未来对流发生潜势预报的判断.因此,此两个时次探空更具有实际的业务应用价值.以两个强对流个例为例分析了BJ-RUC系统提供的分析和预报临近探空的性能及其对强对流潜势预报的指示性作用.结果表明,BJ-RUC输出的分析和预报探空对未来对流发生潜势具有较好的指示作用.但依据模式输出的探空变化要对对流发生时间、强度、落区乃至对流类型进行准确预报仍有一定的难度.     

福建强天气短时潜势预报方法研究

利用2003—2007年中尺度数值模式MM5及探空资料计算的物理量,选取与强对流天气相关性好的热力、动力、能量参数作为预报因子,通过对各参数的空间分布特征和数值与强对流天气落区进行诊断统计分析,及通过参数的搭配,并在考虑因子季节变化特征的基础上,建立强对流诊断预报方程,从而制作福建省未来0～12小时、0～6小时有否出现强对流天气及落区的潜势预报。并用此法回报了2003—2007年3—9月和检验了2008年3—9月的强对流天气。结果表明3—6月潜势预报技巧比7—9月的更高,区域性预报技巧比非区域性的更高,2008年检验结果是0～6小时的预报技巧比0～12小时的低。此法对冰雹、雷雨大风等强对流天气的临近监测预警有较好的指示意义。     

用探空资料检验中尺度数值模式对强对流天气的诊断分析能力

提高对流天气临近预报准确率的关键问题之一是了解大气的垂直稳定度和垂直风切变。中尺度数值模式产品提供了高时空分辨率的大气稳定度和垂直风切变信息，需要首先检验其精度才能进一步考虑其在对流天气预报中的应用。利用北京加密探空资料检验北京市气象局3km分辨率的MM5模式结果，对强对流天气的背景参数包括温湿风垂直廓线、对流有效位能CAPE和垂直风切变进行模式分析和预报与探空对比检验。结果表明：模式模拟的各种大气廓线中，风廓线和温度廓线都具有一定的参考价值，与实况有较好的一致性，但在廓线出现转折的地方，如：逆温层和风向转折时，模式预报较差。露点（湿度）廓线的预报误差较大，不能反映出真实水汽场的分布。因此，模式预报的深层（地面至500hPa）垂直风切变与探空具有较好的一致性，而模式给出的对流有效位能CAPE由于露点预报结果不理想，其值与实际偏差较大。因此模式输出的对流有效位能CAPE必须经过适当订正才能用于诊断强对流天气发生的可能性。     

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.     

“7.21” 暴雨过程动力因子分析和预报研究

针对2012年7月21～22日发生在我国华北地区的暴雨过程,利用美国全球预报系统资料对湿热力平流参数、对流涡度矢量的垂直分量、热力波作用密度、热力位涡波作用密度、热力位势散度波作用密度和湿斜压涡度等动力因子进行计算和诊断分析,结果表明,该暴雨过程是由高、低空急流、高空槽、副热带高压、冷锋和辐合切变线等多个天气系统共同作用造成的。降水区具有垂直上升运动强烈,垂直热量输送明显,湿等熵面向下伸展和水平风垂直切变显著等动、热力学特征。湿热力平流参数等动力因子综合反映了上述动、热力垂直结构特征,因而与6小时观测降水的发展移动有一定相关性。全球预报系统48小时预报的动力因子高值区在走向和落区上与6小时观测降水区比较接近,代表动力因子对降水落区有一定的指示意义。利用全球预报系统的预报场资料对动力因子暴雨预报方程进行计算,结果表明,在降水中心位置预报方面,动力因子降水预报比全球预报系统本身的降水预报更接近观测实况。ETS（Equitable Threat Score）评分计算表明,对于降水的早期预报,动力因子降水预报评分略高于全球预报系统本身的降水预报评分,说明动力因子暴雨预报方程有一定的降水预报能力,可以应用到实际天气业务预报中。     

A SIMILARITY SCHEME FOR QUANTITATIVE FORECAST OF PRECIPITATION OF TYPHOONS

A quantitative scheme is put forward in our work of forecasting the storm rainfall of typhoons for specific sites.Using the initial parameters,weather situations and physical quantities as well as numerical weather prediction products,the scheme constructs multivariate,objective and similarity criteria for environmental factors for the time between the current and forthcoming moment within the domain of forecast.Through defining a non-linear similarity index,this work presents a comprehensive assessment of the similarity between historical samples of typhoons and those being forecast in terms of continuous dynamic states under the multivariate criteria in order to identify similar samples.The historical rainfall records of the similar samples are used to run weighted summarization of the similarity index to determine site-specific and quantitative forecasts of future typhoon rainfall.Samples resembling the typhoon being forecast are selected by defining a non-linear similarity index composed of multiple criteria.Trial tests have demonstrated that this scheme has positive prediction skill.     

GRAPES-GFS模式暴雨预报天气学检验特征

本文采用天气学检验方法,对2016年度国家气象中心GRAPES全球数值预报系统(GRAPES-GFS)业务预报暴雨过程及2013-2015年部分回算个例进行了检验,并结合对比欧洲中期天气预报中心确定性预报模式(EC模式)和国家气象中心全球谱模式T639L60(T639模式)降水预报,梳理总结业务GRAPES-GFS模式预报性能优势和系统性偏差特征。被检验暴雨过程共38次,其中南方暴雨过程20次,北方暴雨过程6次,热带扰动或台风降水过程12次。依靠预报员主观天气学检验分析,从降水预报效果检验出发,结合主要影响天气系统和示踪物理量检验,梳理总结模式预报系统性偏差,以期全面发掘该业务预报模式性能。结果表明对短期时效内的降水预报,GRAPES-GFS模式预报稳定性较好,整体明显优于T639模式。但还存在诸如对对流性降水预报较实况偏北或对主雨带南侧暖区降水预报不足的偏差特征;另对弱高空波动背景下的对流性降水预报偏弱;而在降水预报强度大致正确的情况下,对降水系统南侧偏南气流控制区域预报湿度偏大,对副热带地区的低涡系统预报偏强。     

Evaluation of the 2.5-km Cloud-Resolving Storm Simulator in Predicting Local Afternoon Convection during the Summer in Taiwan

The forecast of summertime afternoon convection by numerical weather prediction models is highly challenging because of its weak dynamical forcing, small scale, and low predictability. To assess such an ability for future improvement, we evaluated the performance of the 2.5-km Cloud-Resolving Storm Simulator (CReSS) in predicting afternoon convection in Taiwan under weak synoptic conditions during the summers (May–October) of 2011 and 2012. For a total of 89 target days, daily CReSS forecasts in three ranges, starting at 0000 UTC (0800 LST) on the same day (D0), the day before (D-1), and 2 days before (D-2), were examined. With regard to the occurrence of afternoon convection anywhere in Taiwan, the predictive skill of the model was considerably high, as evidenced by the threat score (TS) and post-agreement (PA) of D0 (D-1) forecasts reaching 0.75 and 0.90 (0.50 and 0.79), respectively. While the score values decrease when Taiwan is divided into four regions (i.e., the forecast must be in the correct region to be considered a hit), the TS and PA for D0 (D-2) forecasts remain respectable at 0.44 and 0.73 (0.29 and 0.59). Among the four regions, the TS (0.48–0.65) and PA (0.77–0.83) for Central Taiwan are the highest with the best predictive skill. Overall, while the prediction of afternoon convection in the correct region is challenging, the 2.5-km CReSS model has considerable skill (TS ~ 0.30) even 2 days in advance, and can provide useful guidance for afternoon convection in Taiwan.     

统计-动力相结合的相似误差订正法

根据大气相似性原理,提出了利用历史资料的相似信息估计模式误差的反问题,并发展了一种相似误差订正(ACE)方法。该方法将统计和动力两种方法有机结合,在不改变现有数值预报模式的前提下,既充分利用了动力学发展的成就,又能够有效提取大量历史资料中的相似信息,达到减小模式误差、改进当前预报的目的。而且,ACE方法能够针对当前预报的特殊性来区分所利用过去资料的特殊性,提取历史相似信息间接求解反问题。定性分析表明,ACE方法与以往相似-动力模式原理是等价的,但无需重新建立复杂的相似离差预报模式,更具可行性和业务应用前景。在理想化的极限情形下,当数值模式或历史相似完全准确时,ACE方法的预报结果将分别蜕变为动力或统计学方法的预报结果。     

Analogue Correction Method of Errors by Combining Statistical and Dynamical Methods

Based on the atmospheric analogy principle, the inverse problem that the information of historical analogue data is utilized to estimate model errors is put forward and a method of analogue correction of errors (ACE) of model is developed in this paper. The ACE can combine effectively statistical and dynamical methods, and need not change the current numerical prediction models. The new method not only adequately utilizes dynamical achievements but also can reasonably absorb the information of a great many analogues in historical data in order to reduce model errors and improve forecast skill. Furthermore, the ACE may identify specific historical data for the solution of the inverse problem in terms of the particularity of current forecast. The qualitative analyses show that the ACE is theoretically equivalent to the principle of the previous analogue-dynamical model, but need not rebuild the complicated analogue-deviation model, so has better feasibility and operational foreground. Moreover, under the ideal situations, when numerical models or historical analogues are perfect, the forecast of the ACE would transform into the forecast of dynamical or statistical method, respectively.     

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results. In recent years, post-processing models based on deep learning have become prominent. In this paper, a deep learning model named ED-ConvLSTM based on encoder-decoder structure and ConvLSTM is developed, which appears to be able to effectively correct numerical weather forecasts. Compared with traditional post-processing methods and convolutional neural networks, ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field. In this paper, the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics, convolutional neural network postprocessing methods, and the original prediction by the ECMWF. The results show that the correction effect of ED-ConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes, especially in the long forecast time.     

Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 and T382

Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.     

基于重组降水集合预报的洪水概率预报

采用条件亚正态模型方法,生成了具有包含不同可能性的降水集合预报。为了保持各子流域降水集合预报变量之间的空间相关性,采用集合预报重组方法对降水集合预报进行重新排列。使用重组后的降水集合预报驱动水文模型,实现了淮河上游大坡岭-息县、淮河上游息县-王家坝和汝河-洪河上游3个子流域的12次洪水过程的洪水概率预报,并对1988年9月7日和1991年7月31日两次洪水概率预报进行个例分析。结果表明:相对于单一确定性预报,通过条件亚正态分布模型生成降水集合预报后,再经过Schaake洗牌法空间相关性重新组合的降水集合预报,捕捉洪峰出现时间和流量的能力更强。对洪水概率预报来说,降水概率预报更能达到对未来的水文事件进行最大可能估计的目的,并尽可能综合了降水预报不确定性因素,同时也说明维持变量原有的空间相关特征对于降水概率预报具有重要意义。