基于骨架的线状对流系统客观量化识别算法研究

本文将计算机图形学骨架概念应用到气象学领域,发展了回波图像预处理、骨架修剪处理以及长宽比量化处理技术,该方法能自动识别出雷达回波拼图中符合气象学标准的线状对流系统（quasi-linear convective systems,QLCSs）。首先结合2016年黄淮地区一次双QLCSs过程给出了基于骨架的QLCSs客观量化算法的具体技术流程,然后利用该方法对2016年6月安徽地区的QLCSs进行客观筛选,并进一步量化识别QLCSs的移动特征,结合灾害天气实况与主观识别进行对比评估,结果表明:结合气象学标准改造的骨架图像识别算法,较好保留了气象回波形状信息,在准确量化对流系统长短轴的基础上,实现QLCSs的有效识别。而获得的量化移动矢量等特征,一方面可应用于致灾QLCSs的分类研究,为开展长序列统计及致灾机理分析提供个例识别方法和量化特征,另一方面也为QLCSs的短临监测预警业务提供新的思路。     

热带气旋降水客观分离业务化探讨

针对中国热带气旋(Tropical Cyclone,简称TC)降水的分离问题,利用国家气候中心任福民等提出的客观天气图分析法(Objective Synoptic Analysis Technique,简称OSAT)对2005年影响我国强度达到台风以上的6个TC降水进行了客观分离,并与人工的主观识别方法进行了对比.结果表明,OSAT客观方法由于对TC外围流系最大范围半径D1设置偏大,导致原来的判别测站数比主观方法多,其他系统或TC和其他系统共同作用的降水被包含进来,所以误判率较高,但遗漏少.为了减少误判率,将OSAT方法中的D1根据实况环流场给出,判别准确率得到了提高,但会出现判别结果比主观方法略偏少的现象,原因是TC和其它系统(冷锋、西风槽等)的相互作用结果没有考虑进去.总的来说,对D1的设定由原来根据TC强弱度给出常参数方案组改为根据实况观测、云图等资料获得后,提高了识别效果.使用该客观方法识别TC降水的精确度能满足业务需求,可以投入业务使用.     

Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

A major component of flood alert broadcasting is the short-term prediction of extreme rainfall events, which remains a challenging task, even with the improvements of numerical weather prediction models. Such prediction is a high priority research challenge, specifically in highly urbanized areas like Mumbai, India, which is extremely prone to urban flooding. Here, we attempt to develop an algorithm based on a machine learning technique, support vector machine (SVM), to predict extreme rainfall with a lead time of 6–48 h in Mumbai, using mesoscale (20–200 km) and synoptic scale (200–2,000 km) weather patterns. The underlying hypothesis behind this algorithm is that the weather patterns before (6–48 h) extreme events are significantly different from those of normal weather days. The present algorithm attempts to identify those specific patterns for extreme events and applies SVM-based classifiers for extreme rainfall classification and prediction. Here, we develop the anomaly frequency method (AFM), where the predictors (and their patterns) for SVM are identified with the frequency of high anomaly values of weather variables at different pressure levels, which are present before extreme events, but absent for non-extreme conditions. We observe that weather patterns before the extreme rainfall events during nighttime (1800 to 0600Z) is different from those during daytime (0600 to 1800Z) and, accordingly, we develop a two-phase support vector classifier for extreme prediction. Though there are false alarms associated with this prediction method, the model predicts all the extreme events well in advance. The performance is compared with the state-of-the-art statistical technique fingerprinting approach and is observed to be better in terms of false alarm and prediction.     

An Objective Identification Method for Wintertime Cold Fronts in Eurasia

The present study identifies wintertime cold fronts in Eurasia from gridded datasets using a new objective two-step identification scheme. The simple and classic conception of a front is adopted, where a cold front is identified as the warm boundary of the frontal zone with a suitable horizontal temperature gradient and cold advection. We combine the traditional thermal front parameter with temperature advection to first identify the cold frontal zone, and then its eastern and southern boundaries are objectively plotted as a cold front in Eurasia. By comparing different cold front identification methods, the results from this two-step cold front identification method and subjective analysis are more consistent, and the positions of the cold front identified with our method are more reasonable. This objective technique is also applied to a nationwide cold wave event over China. Results show that the horizontal extent and movement of the cold front are in good agreement with the related circulation and the associated cold weather. The proposed method and results in this study may shed light on the rapid identification of cold fronts in operational weather analysis and facilitate further research on the long-term activity characteristics of continental cold fronts.     

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。     

The synoptic-scale surface wind climate regimes of the Mediterranean Sea according to the cluster analysis of ERA-40 wind fields

In 2008, Burlando et al. proposed a two-stage clustering technique for the classification of mesoscale wind regimes. The first stage of this classification scheme is based on a hierarchical cluster analysis, according to Ward’s minimum variance technique applied to an Euclidean distance, for a first-guess subdivision of the events into groups. In the second stage, a partitional k-means clustering for the optimal reassignment of the events among clusters is performed. Following this methodology, in the present paper, the synoptic-scale wind fields over the Mediterranean Sea have been analysed in order to check the suitability of this technique to a higher-dimensional phase space. The study is based on a 30-year-long data set of wind speed and direction at 10 m above sea level obtained from the reanalysis of the European Centre for Medium-range Weather Forecasts. The cluster analysis has been performed on wind speeds only, while wind directions have been used to show the existence, in the spatial structure of the wind climate regimes, of particular regions which correspond to the main topographic and/or thermal forcing of the Mediterranean. These regions are the peaks of the probability density function in the climatological phase space of wind speed patterns recognised by the clustering algorithm. The final classification has been able to identify, for instance, the surface circulation patterns corresponding to Mistral events in the western Mediterranean sub-basin and Etesian winds in the eastern sub-basin.     

雷达定量估测降水的亮带自动消除改进方法

层状云降水中,0 ℃层融化效应会引起雷达反射率因子局部增大,若不进行订正,则会高估雷达估测的降水.本文提出一种基于新一代天气雷达反射率因子垂直廓线的0 ℃层亮带自动识别与订正算法,以减小因亮带造成的降水高估.本研究首先对降水类型进行分类,在SHY95的基础上增加了垂直方向的反射率因子三维特征,避免亮带的反射率因子高值区被误识别为对流云区;其次,在层状云区识别出一个可能的亮带影响区,在其中查找亮带,采用旋转坐标系法精确的识别亮带的顶、底高度;最后,利用最小二乘法拟合亮带上、下层的斜率,平滑垂直廓线(VPR,Vertical Profile of Reflectivity)的显著突出部分.将该方法应用于北京地区2010—2011年10次包含亮带的降水过程,得到的亮带订正后的均方根误差E_RMS、平均绝对误差E_RMA、平均相对误差B_RM值较初值均有显著减小(分别减小1.538 mm,0.417和0.468).结果表明,该方法能够有效地识别与订正亮带,使得定量测量降水精度有所提高.     

X波段双偏振雷达相态识别与拼图的关键技术

X波段双偏振雷达具有时空分辨率高、易于布网的特点,但散射特性差异和衰减影响使现有S波段雷达的相态识别和拼图算法不适用于X波段双偏振雷达。该文针对X波段相态识别及拼图产品的关键技术开展研究,提出基于准垂直剖面的融化层识别方法、基于数据质量的置信度阈值调整方法、基于统计的隶属函数参数改进方法和基于衰减程度的拼图融合方法。通过对比改进后可有效提升水凝物相态识别结果的可靠性和多雷达拼图结果的合理性。在2016年汛期北京典型个例中,融合后的X波段雷达网与当地S波段业务雷达相比能够提供更精细的回波结构和水凝物相态分布,有效缓解S波段雷达在近处探测能力降低的问题,识别的降雹区与地面观测相符。     

北京地区强对流天气展望预报方法研究

该文依据1983～1992年6～8月间276次强对流天气过程，研究北京地区强对流天气的形成条件及其展望预报方法。通过500 hPa逐日分型，将影响北京地区的大尺度环流型划分为5种类型。在各型中随机抽取出现和不出现强对流天气样本共298个，分别作合成分析，研究各环流型出现和不出现强对流天气的合成形势及差异。结果表明，出现和不出现强天气的环流系统特征、结构及物理量分布都有明显不同，从而概括出概念模式，导出各环流型预报着眼点。在此基础上，从各型计算的数十个物理量参数中，筛选出最佳预报因子，采用判断树预报流程，逐     

A multi-model ensemble approach for assessment of climate change impact on surface winds in France

Statistical downscaling of 14 coupled atmosphere-ocean general circulation models (AOGCM) is presented to assess potential changes of the 10 m wind speeds in France. First, a statistical downscaling method is introduced to estimate daily mean 10 m wind speed at specific sites using general circulation model output. Daily 850 hPa wind field has been selected as the large scale circulation predictor. The method is based on a classification of the daily wind fields into a few synoptic weather types and multiple linear regressions. Years are divided into an extended winter season from October to March and an extended summer season from April to September, and the procedure is conducted separately for each season. ERA40 reanalysis and observed station data have been used to build and validate the downscaling algorithm over France for the period 1974–2002. The method is then applied to 14 AOGCMs of the coupled model intercomparison project phase 3 (CMIP3) multi-model dataset. Three time periods are focused on: a historical period (1971–2000) from the climate of the twentieth century experiment and two climate projection periods (2046–2065 and 2081–2100) from the IPCC SRES A1B experiment. Evolution of the 10 m wind speed in France and associated uncertainties are discussed. Significant changes are depicted, in particular a decrease of the wind speed in the Mediterranean area. Sources of those changes are investigated by quantifying the effects of changes in the weather type occurrences, and modifications of the distribution of the days within the weather types.     

An assessment of the Jenkinson and Collison synoptic classification to a continental mid-latitude location

A weather-type catalogue based on the Jenkinson and Collison method was developed for an area in south-west Russia for the period 1961–2010. Gridded sea level pressure data was obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. The resulting catalogue was analysed for frequency of individual types and groups of weather types to characterise long-term atmospheric circulation in this region. Overall, the most frequent type is anticyclonic (A) (23.3 %) followed by cyclonic (C) (11.9 %); however, there are some key seasonal patterns with westerly circulation being significantly more common in winter than summer. The utility of this synoptic classification is evaluated by modelling daily rainfall amounts. A low level of error is found using a simple model based on the prevailing weather type. Finally, characteristics of the circulation classification are compared to those for the original JC British Isles catalogue and a much more equal distribution of flow types is seen in the former classification.     

基于数据挖掘处理的影响辽宁高速公路冬季交通事故主要气象要素分析

利用2014—2016年辽宁省冬季高速公路事故记录,将多要素逐日气象观测数据与事故数据按照日期进行匹配,分析气象条件引发高速事故的空间分布特征。数据挖掘分析首先利用两步聚类方法确定辽宁地区冬季天气类型数量,再通过K-means方法对气象数据进行聚类处理。采用随机森林方法对不同天气类型搭建高速公路交通事故分类模型,并分析模型中气象要素的特征重要性。结果表明：受气象条件影响的高速事故数量辽南地区最多,其次是辽西地区,辽东、辽北地区占比较低。辽宁冬季天气可以分为四种类型,根据气象要素数据结构总结出的天气特征分别为：当日出现降水、前一日出现降水、寒冷干燥、潮湿回暖。有明显降水特征天气类型的事故率超过七成,降温、升温天气类型的事故率在两成左右。随机森林方法对前一日出现降水、寒冷干燥两种天气类型的分类精度更高,模型泛化能力也更好。4种天气类型中气象要素的特征重要性有明显差异,最高地温要素在事故高发天气类型中特征重要性排第1位,在潮湿回暖天气类型排第3位,对于冬季辽宁地区高速公路交通安全影响高于其他要素。     

基于雷达回波强度面积谱识别降水云类型

基于谱分析原理提出了雷达回波强度面积谱的概念及算法,利用宁夏银川多普勒天气雷达回波资料,分析了不同性质降水云的雷达回波强度面积谱,并根据不同性质降水云雷达回波强度面积谱特征,提出了基于雷达回波强度面积谱识别降水云类型的方法,利用强回波面积（回波强度不小于40 dBZ的回波面积）占总回波面积百分比和基本降水回波面积（回波强度不小于20 dBZ的回波面积）占总回波面积百分比作为降水云类型判别的主要因子,提炼出基于雷达回波强度面积谱特征参数的层状云、积层混合云、对流云等不同类型降水云的判别指标,建立了基于雷达回波的降水云类型自动判识模型。利用该模型对2016-2017年6次强降水过程进行了降水云类型判别试验,模型准确判别出6次强降水过程中2次为对流云降水、4次为混合云降水,判别结果较好地反映了降水云类型,验证了判识方法的可行性。     

Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data

This paper investigates the potential for developing schemes that classify convective and stratiform precipitation areas using the high infrared spectral resolution of the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). Two different classification schemes were proposed that use the brightness temperature (BT) Τ _10.8 along with the brightness temperature differences (BTDs) Τ _10.8–Τ _12.1, Τ _8.7–Τ _10.8, and Τ _6.2–Τ _10.8 as spectral parameters, which provide information about cloud parameters. The first is a common multispectral thresholding scheme used to partition the space of the spectral cloud parameters and the second is an algorithm based on the probability of convective rain (PCR) for each pixel of the satellite data. Both schemes were calibrated using as a reference convective\stratiform rain classification fields derived from 87 stations in Greece for six rainy days with high convective activity. As a result, one single infrared technique (TB₁₀) and two multidimensional techniques (BTD_all and PCR) were constructed and evaluated against an independent sample of rain gauge data for four daily convective precipitation events. It was found that the introduction of BTDs as additional information to a technique works in improving the discrimination of convective from stratiform rainy pixels compared to the single infrared technique BT₁₀. During the training phase, BTD_all performed slightly better than BT₁₀ while PCR technique outperformed both threshold techniques. All techniques clearly overestimate the convective rain occurrences detected by the rain gauge network. When evaluating against the independent dataset, both threshold techniques exhibited the same performance with that of the dependent dataset whereas the PCR technique showed a notable skill degradation. As a result, BTD_all performed best followed at a short distance by PCR and BT₁₀. These findings showed that it is possible to apply a convective/stratiform rain classification algorithm based on the enhanced infrared spectral resolution of MSG-SEVIRI, for nowcasting or climate purposes, despite the highly variable nature of convective precipitation.     

Changes in atmospheric circulation over Europe detected by objective and subjective methods

Summary Changes in atmospheric circulation over Europe since 1958 were examined using both objective (modes of low-frequency variability and objective classification of circulation types) and subjective (Hess-Brezowsky classification of weather types) methods. The analysis was performed with an emphasis on the differences between the winter (DJF) and summer (JJA) seasons, and between objectively and subjectively based results. Majority of the most important changes in atmospheric circulation are same or similar for the objective and subjective methods: they include the strengthening of the zonal flow in winter since the 1960s to the early 1990s; the increase (decrease) in frequency of anticyclonic (cyclonic) types in winter from the late 1960s to the early 1990s, with a subsequent decline (rise); and the sharp increase in the persistence (measured by the mean residence time) of all groups of circulation types in winter around 1990 and of anticyclonic types in summer during the 1990s. Differences between the findings obtained using the objective and subjective methods may result from the intrinsically different approach to the classification (e.g. the Hess-Brezowsky weather types have a typical duration of at least 3 days while objective types typically last 1–3 days). Generally, changes in atmospheric circulation which have taken place since the 1960s were more pronounced in winter than in summer. The most conspicuous change seems to be the considerable increase in the persistence of circulation types during the 1990s, which may be also reflected in the increase in the occurrence of climatic extremes observed in Europe during recent years.     

CHAC: a weather pattern classification system for regional climate downscaling of daily precipitation

A weather pattern clustering method is applied and calibrated to Argentinean daily weather stations in order to predict daily precipitation data. The clustering technique is based on k-means and is applied to a set of 17 atmospheric variables from the ERA-40 reanalysis covering the period 1979–1999. The set of atmospheric variables represent the different components of the atmosphere (dynamical, thermal and moisture). Different sensitivity tests are applied to optimize (1) the number of observations (weather patterns) per cluster, (2) the spatial domain size of the weather pattern around the station and (3) the number of members of the ensembles. All the sensitivity tests are compared using the ROC (Relative Operating Characteristic) Skill Score (RSS) derived from the ROC curve used to assess the performance of a predictive system. First, we found the number of observations per cluster to be optimum for values larger than 39. Second, the spatial domain size (~4° × 4°) was found to be closer to a local scale than to a synoptic scale, certainly due to a dominant role of the moisture components in the optimization of the transfer function. Indeed, when reducing the set of variables to the subset of dynamical variables, the predictive skill of the method is significantly reduced, but at the same time the domain size must be increased. A potential improvement of the method may therefore be to consider different domains for dynamical and non-dynamical variables. Third, the number of members per ensembles of simulations was estimated to be always two to three times larger than the mean number of observations per cluster (meaning that at least all the observed weather patterns are selected by one member). The skill of the statistical method to predict daily precipitation is found to be relatively homogeneous all over the country for different thresholds of precipitation.     

沙尘天气定量分级方法研究与应用

利用已有的关于能见度与沙尘浓度统计反演关系的研究成果,对沙尘天气进行了定量分级研究.利用2004、2005年春季3～5月沙尘天气地面气象观测资料,采用沙尘天气的强度分类与反演的沙尘浓度分级的统计方法,建立了不同沙尘天气(扬沙、沙尘暴和强沙尘暴)对应的沙尘浓度等级,并通过对2006年沙尘天气的实例分析,验证了该种分级方法的可用性.通过由能见度反演的观测结果与沙尘数值预报模式的对比分析,探讨了沙尘数值预报业务模式产品的天气学释用方法,建立了不同强度沙尘天气与沙尘数值模式输出的浓度之间的定量分级关系.     

高温中暑气象等级评定方法

在全球气候变化的大背景下, 高温热浪天气频繁出现, 由其引发的群发性中暑事件不断发生。为了有效预防极端高温天气对人体健康的影响, 利用全国358个重点城市1996-2005年逐日地面气象观测资料, 通过对气象要素与中暑的相关分析, 选择炎热指数为气象指标对引发中暑的气象条件进行了等级划分, 同时考虑不同程度高温天气的持续时间确定了高温中暑气象等级, 分别为可能发生中暑、较易发生中暑、易发生中暑和极易发生中暑4个等级。利用武汉市1994年和1995年7 -8月中暑人数与高温中暑气象等级进行对比分析, 结果表明:两者具有较好的对应关系。     

Anatomy of the synoptic conditions occurring over southern Greece during the second half of the 20th century. Part II. Autumn and spring

Summary ?A new procedure for grouping the circulation types occurring during autumn and spring over an area is introduced in this paper. The procedure combines surface air mass characteristics affecting the area with the prevailing synoptic conditions. Factor and Cluster Analysis are used to derive the circulation types, based on surface meteorological data, as well as on surface pressure grid data. The methods are applied to Athens, Greece, using data covering the period 1954–1999. Eight circulation types for the autumn (September, October, November) and eight for the spring (March, April, May) are derived. Clusters presenting low-pressure systems are found to be generally fast moving, while anticyclones generally remain over the area for more than two days. Finally an interesting seasonal distribution is found from some weather types. Received February 20, 2002; revised December 23, 2002; accepted January 9, 2003 Published online May 26, 2003     

An empirical classification of weather types in the Mediterranean Basin and their interrelation with rainfall

Summary  This paper presents a classification of weather types in the Mediterranean Basin based on cluster analysis of the daily occurrences of several surface pressure centers and the subjective identification of 500 hPa trough axis positions (1992–1996). The procedure results in 20 types that explain 69% of overall pressure center variance and which are consistent with the seasonal succession of regional circulation. The development of weather types in winter is primarily controlled by the eastward propagation of barotropic waves while departures from the zonal flow pattern in summer tend to be linked to blocked stationary pools. H1-types with anticyclonic circulation in the Western Mediterranean and cyclonic flow in the eastern part are well interrelated with zonal and anticyclonic general weather types in Central Europe. H2-types featuring a weak Azores Anticyclone interrelate with a variety of meridional circulation types after the Hess and Brezowski (1969) classification. The 20 types explain rainfall variance in the core Mediterranean regions (as defined by principal components) to a high degree while rainfall variance in marginal regions is influenced by circulation patterns not being typical for the Mediterranean Basin. Received January 29, 1999 Revised March 28, 2000