北京香山\

应用葵花8号卫星资料,结合NCEP FNL再分析、GNSS遥感水汽、风廓线雷达、全国智能网格实况融合分析资料,对2017年7月14日和2018年8月7日沈阳两次暴雨过程（分别简称过程Ⅰ和过程Ⅱ）中对流云特征进行了比较分析,重点探讨了对流云的触发维持机制与影响降水特征差异的因素。结果表明：（1）两次过程分别为局地突发暴雨和区域性极端暴雨,沈阳市区暴雨均由两个对流云团引发,对流云团合并使得降水持续。过程Ⅱ云团合并发生在其移动方向的后侧,具有后向传播特征,合并云团沿其长轴方向移动影响沈阳市,使降水时间延长。（2）在降水前至降水初期,过程Ⅰ对流云顶和水汽层顶快速上升且云顶迅速超过水汽层顶,而过程Ⅱ亮温下降缓慢。短时强降水发生前红外和水汽亮温同步快速降至-60℃,可作为提前预判对流云团产生短时强降水的参考指标。10 min雨量大于10 mm的对流云云顶集中分布在红外亮温低于-55℃、亮温差为-5~0℃的范围。（3）两次过程中,沈阳市分别位于东北冷涡后部和副热带高压北缘。过程Ⅰ,探空曲线呈“X”型,CAPE高达2 584 J·kg^-1,造成对流云深厚,云底以下干层导致雨滴蒸发,使降水强度减弱,该过程高强度降水仅发生在对流云团合并加强阶段。过程Ⅱ,云底到地面湿层明显,保证了雨滴降至地面,产生相同量级降水的云团的TBB比过程Ⅰ高。（4）强降水发生前,地面风场存在明显辐合,当大气可降水量2 h内跃增8 mm时,站点出现强降水;局地水汽跃增可能是低空西南气流偏南分量增大或偏北冷空气侵入到暖湿空气中所致。

     

Construction and assessment of a logistic regression model applied to short-term forecasting of thunderstorms in León (Spain)

Forecasting thunderstorms is one of the most difficult tasks in weather prediction due to the scarce knowledge on how to characterise the mechanisms taking part in the formation of thunderstorms. New tools based upon the objective recognition of appropriate conceptual models have been developed in the last years. A good example of this are the statistical models, based on variables that characterise the preconvective atmospheric conditions.This paper presents the results obtained by applying a short-term forecast model to thunderstorms during the summer periods in León (Spain). This model makes use of the logistic function as a binary forecasting technique determining storm/no storm. The logistic function used was a combination of 15 previously selected meteorological variables. The model has been constructed with the data collected on 152 occasions, and it has been verified on 77 other occasions.The skill scores obtained show that the use of statistical models combining the data, provided by variables characterizing the preconvective conditions and the triggering mechanisms, represent an important step in the difficult task of short-term thunderstorm forecasting.     

Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting

A neuroid BP-type three-layer mapping model is used for monthly rainfall forecasting in terms of 1946-1985 Nanjing monthly precipitation records as basic sequences and the model has the form i × j ＝ 8 × 3, K = 1; by steadily modifying the weighing coefficient, long-range monthly forecasts for January to December, 1986 are constructed and 1986 month-to-month predictions are made based on, say, the January measurement for February rainfall and so on, with mean absolute error reaching 6,07 and 5,73 mm, respectively. Also, with a different monthly initial value for June through September, 1994, neuroid forecasting is done, indicating the same result of the drought in Nanjing dur-ing the summer, an outcome that is in sharp agreement with the observation.     

变形场锋生对0108登陆台风温带变性和暴雨形成作用的诊断分析

2013年10月6—7日台风“菲特”和台风“丹娜丝”影响中国东部,造成区域性大暴雨。利用客观分离方法及数值模拟两种方法辨别两个台风在不同阶段各自的作用。结果表明,对于10月6日区域性大暴雨,客观分离结果和数值模拟结果一致,被识别为主要由台风“菲特”造成的降水。对于10月7日江苏东南部、浙江东北部的区域性大暴雨,客观分离结果表明,两个台风都起到了关键性作用;模式模拟结果表明,强降水与台风“菲特”有直接关联,而与台风“丹娜丝”作用不大。锋生函数也表明,冷空气对强降水也起到了一定的作用。

     

用地球静止气象卫星云图分析我国几类强雷暴天气

夏季的雷暴、冰雹、大风等强对流天气,尺度小,生命史短,用常规的天气观测资料分析它们的活动存在着一定的困难。然而,地球静止气象卫星(GMS)云图为我们提供了连续观测这些对流活动的一种可能性。本文主要是利用三小时一次的GMS云图资料,分析了1978年6—8月份我国大陆上的六类雷暴天气,阐述了强雷暴活动的云图特征。指出,假若有30分钟一次的GMS云图,则对夏季大陆上强雷暴的分析和监视更为有利。     

Statistical analysis of thunderstorms on the eastern Tibetan Plateau based on modified thunderstorm indices

The Tibetan Plateau, with an average altitude above 4000 m, is the highest and largest plateau in the world. The frequency of thunderstorms in this region is extremely high. Many indices are used in operational forecasting to assess the stability of the atmosphere and predict the probability of severe thunderstorm development. One of the disadvantages of many of these indices is that they are mainly based on observations from plains. However, considering the Plateau's high elevation, most convective parameters cannot be applied directly, or their application is ineffective. The pre-convective environment on thunderstorm days in this region is investigated based on sounding data obtained throughout a five-year period(2006–10).Thunderstorms occur over the Tibetan Plateau under conditions that differ strikingly from those in plains. On this basis,stability indices, such as the Showalter index(including SI and SICCL), and the K index are improved to better assess the thunderstorm environments on the Plateau. Verification parameters, such as the true-skill statistic(TSS) and Heidke skill score(HSS), are adopted to evaluate the optimal thresholds and relative forecast skill for each modified index. Lastly, the modified indices are verified with a two-year independent dataset(2011–12), showing satisfactory results for the modified indices. For determining whether or not a thunderstorm day is likely to occur, we recommend the modified SICCLindex.     

Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms

Thunderstorms are responsible for remarkable devastation when accompanied with lightning flashes, high wind gusts, torrential rain, hail and tornadoes. Weather hazards due to thunderstorms of such severe measure take place every year over Kolkata (22°32′N, 88°20′E), India during the pre-monsoon season (April–May). Prediction of severe thunderstorms is extremely important to cope with the devastations. However, forecasting severe thunderstorms is very difficult because the weather system is confined within a very small spatial-temporal scale. The network of observation systems is not adequate to detect such high frequency small scale weather. The purpose of the present study is to bring in the concept of Intuitionistic fuzzy logic as a decision — making technique to assess the predictability of severe thunderstorms over Kolkata in the premonsoon season. Different measures of entropies are used to extract the route of fuzziness. The intuitionistic fuzzy logic is implemented with ten years (1997–2006) observation of the occurrence/nonoccurrence of severe thunderstorms to assess the predictability. The result reveals that two consecutive severe thunderstorm days are highly probable after two consecutive non-thunderstorm days whereas the probability of severe thunderstorm is very less after three consecutive non-thunderstorm days during the pre-monsoon season over Kolkata. The result is compared with the box-and-whisker plot and validated with four years (2007–2010) observations of India Meteorological Department (IMD).     

Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm

Thunderstorms are perennial features of India. However, the severe thunderstorms of pre — monsoon season (April–May) over Kolkata (22°32′N, 88°20′E) are of great concern for imparting devastating effect on life and property on the ground and aviation aloft. The study is thus, focused on developing one hidden layer neural network model with variable learning rate back propagation algorithm to forecast such thunderstorms. Convective available potential energy (CAPE) and convective inhibition energy (CIN) are selected as the input parameters of the model after the estimation of various skill scores like, Probability of Detection (POD), False Alarm Ratio (FAR), Heidke Skill Score (HSS) and Odds Ratio Skill Score (Yule’s Q) on different stability indices. During training the model, the squared error for forecasting severe thunderstorms is observed to be 0.0022 when the values of CIN within the range of 0 to 140 J kg^?1 is taken as the input whereas the error is observed to be 0.0114 while the values of CAPE within the range of 2000 to 7000 J kg^?1 is considered as the input. The values of CIN and CAPE at twelve to six hours prior to the occurrence of severe thunderstorms are considered in this study. During validation of the model, the percentage of prediction error with the values of CIN as input is observed to be 0.042% and that with CAPE as input is 0.162%. The values of CIN within the range of 0–140 J kg^?1 are observed to be more persistent in forecasting severe thunderstorms over Kolkata than the values of CAPE within the range of 2000–7000 J kg^?1.     

An application of the threshold autoregression procedure to climate analysis and forecasting

In this paper a nonlinear method of time series analysis-threshold autoregressive (TAR) model in discrete time is used. The TAR procedure consists of four parts: model building, statistical identification, parameter estimation and forecasting.The object of this study is to estimate monthly total precipitation of Shanghai and Beijing by using open loop TAR model. We can see that the trend of forecasting is in agreement with observations.     

1973—2012年承德市雷暴气候特征分析

基于1973—2012年承德市9个地面气象观测站逐日雷暴观测资料,利用线性分析、突变分析、小波分析等统计方法,对承德市雷暴日数变化特征进行分析。结果表明:承德市雷暴日数呈现出由西向东递减的空间分布特征,大部分地区处于"中雷区",丰宁、滦平、承德市达到"多雷区"级别;全市年平均雷暴日数为39.5d,最多年为55.3d,而最少年为27.6d;1973—2012年承德市雷暴日数呈现显著减少趋势,平均10a减少2d左右,并于2006年发生突变;承德市雷暴发生存在明显季节差异,高发期主要集中在夏季,呈单峰型分布,7月雷暴日出现最多;初雷日一般出现在4月上旬,近40a呈显著推迟趋势,终雷日一般出现在10月中旬;雷暴日数存在明显的年际变化,且呈现出4~6a周期振荡。     

Modeling the electric structures of two thunderstorms and their contributions to the global circuit

This study examines the electricity in two thunderstorms, typical for their respective locales (the Great Plains and the New Mexico mountains), by modeling them as a set of steady-state horizontal layers of external currents. The model electric sources, corresponding to the charge separation processes in the thundercloud, are embedded in an exponential conducting atmosphere. The source parameters are determined by fitting the model electric field to measured profiles. The resulting currents to the ionosphere (i.e., the Wilson current) from the two storms are 0.53 A and 0.16 A, while the calculated electrical energies of the storms are 2.3 × 10¹⁰ J and 2.8 × 10⁹ J, respectively. The more vigorous storm is estimated to transfer 16 000 C in the global circuit during 8.5 h of its lifetime, while the weaker mountain storm transferred about 1200 C in its entire 2-h lifetime. Removal of the screening charge layer from above the updraft region in one modeled storm leads to only a small increase in the net Wilson current of less than 3%, while it provides a substantial local disturbance of the electric field. Overall, the model findings indicate that differences in the Wilson currents and electrical energies of the two storms result from differences in their internal dynamical and electrical structures as well as their geographical locations.     

Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms

The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April?CMay) over Kolkata (22°32??N, 88°20??E), India. The pre-monsoon thunderstorms during 1997?C2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12?h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.     

新津机场雷暴的特征

利用1986～1996年新津机场雷暴观测资料，分析了该区域雷暴天气发生的时间和空间分布特征。在此基础上结合典型的天气个例，用天气动力学方法，讨论了新津机场雷暴天气的物理成因。结果显示：新津机场雷暴集中在4～9月，以热雷暴为主。高温、高湿、不稳定能量的大气和动力触发是其形成的重要条件。     

新津机场雷暴的特征

利用1986～1996年新津机场雷暴观测资料,分析了该区域雷暴天气发生的时间和空间分布特征.在此基础上结合典型的天气个例,用天气动力学方法,讨论了新津机场雷暴天气的物理成因.结果显示:新津机场雷暴集中在4～9月,以热雷暴为主.高温、高湿、不稳定能量的大气和动力触发是其形成的重要条件.     

Morphological classification pertaining to validate the climatology and category of thunderstorms over Kolkata,India

As the world's highest and largest plateau, the Qinghai–Xizang Plateau has experienced a greater warming than the Northern Hemisphere and global averages. This warming has been reported to exhibit an elevation-dependent pattern. However, the finding involved plenty of uncertainties caused by the spatially limited datasets and complex topography. Here, we explored an approach integrating satellite-derived LST data and ground records to generate a spatially continuous air temperature dataset for the plateau grasslands from 2003 to 2012, and then examined influences of elevation/topography on temperature change trends. The derived temperature dataset was validated to be closely correlated with field-station records. Based on the derived spatially continuous temperature datasets, we found an opposite change trend of annually average temperature between Qinghai and Xizang Province. The contrasted trend was obvious in daytime and more so in summer season. By analyzing the temperature trend in relation to elevation, we found an enhanced temperature change trend in higher elevation than in lower elevation for autumn nights and winter temperatures, while the temperature change trends for other seasons were more evident in lower elevation areas. The varying temperature change trends as regulated by elevation implies that temperate grasslands have experienced a more rapid temperature change than alpine grasslands during the past decade.     

逐步回归双重分析及其在长期天气预报中的应用

本文针对多元分析和周期分析各自存在的缺陷,提出了一种新的统计预报方法——逐步回归双重分析方法,它利用逐步回归技术将因子筛选和周期分析有机结合,并反映在同一预报方程中。 本文以金华市5月份降水量长期预报为例,进行了初步的分析和尝试,表明该方法与多元分析和周期分析比较,其拟合误差与预报误差均较小,预报效果稳定,对长期天气预报有较好的实用价值。     

Measurements by airplane of electric charge passing vertically through thunderstorms to ground

Summary The electric current passing vertically through thunderstorms has been measured above the thunderheads with apparatus mounted on a B-29 airplane. The direction of the resulting current, in all cases, was such as to maintain the negative charge found over the fair-weather region of the earth. The average magnitude of the current per storm was one ampere, an amount required of the average storm if the fair-weather charge is to be maintained through thunderstorm activity. Other results obtained are summarized at the end of the article.

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Zusammenfassung Der elektrische Strom, der vertikal durch Gewitter fließt, wurde oberhalb der Gewitterwolken mit Apparaten gemessen, die auf einem B-29-Flugzeug montiert waren. Der resultierende Strom war in allen Fällen so gerichtet, daß er die negative Ladung über dem Schönwettergebiet der Erde zu erhalten sucht. Die mittlere Stromstärke pro Gewitter betrug 1 Amp., welcher Durchschnittsbetrag gerade erforderlich ist, um die Schönwetterladung durch Gewittertätigkeit aufrechtzuerhalten. Weitere Ergebnisse sind am Ende des Aufsatzes zusammengefaßt.

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Résumé On a mesuré à l'aide d'appareils montés sur un avion B-29 le courant électrique vertical issu des nuages orageux, au-dessus de ceux-ci. La direction du courant résultant était toujours telle qu'il tendait à maintenir la charge négative au-dessus de la région de beau temps de la terre. L'intensité moyenne par orage était de un ampère, valeur précisément nécessaire pour maintenir la charge de beau temps par l'activité orageuse. D'autres résultats sont résumés à la fin de l'article.

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With 3 figures.

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Dedicated to Professor Dr.H. Benndorf on the occasion of his eightieth birthday.     

气象资料分析服务子系统

气象情报业务系统是省气象台的主要业务系统之一。该系统首次实现了实时与历史气象资料的查询自动化。气温等气象资料分析服务子系统是该系统的主要构成之一。本系统提供了气温以及大风日数、日照时数、日照百分率、积温、积雪深度、高低温日数、积雪深度等各项气象资料的查询、检索、排序、极值查找、等值线分析、曲线分析等功能 ,同时还提供了图形分布显示、图形放大、切换、打印和查询结果保存等功能 ,操作界面友好、使用便捷。1　系统的主要功能1 1　平均气温查询功能可以查询任意时段内日平均气温实况 ,日历史平均气温 ,日平均气温距…     

Radar observations of precipitation production in thunderstorms

Abstract

Precipitation production is investigated for 9 intense thunderstorms that developed over the Lowveld in South Africa. A C‐band radar is used to observe the 3‐dimensional reflectivity pattern. Using an empirical relation between reflectivity factor and precipitation content and integrating over the storm volume provides an estimate of the total precipitation content aloft. Likewise, an area integration of the instantaneous rain rate at cloud base yields an estimate of the rate of total outflow. At their maturing stage, the storms had precipitation contents of 0.2 to 5.0 Tg and rainfall rates of about 0.3 to 2.0 Gg s^?1. The total accumulation of rain at the ground ranged from 1 to 10 Tg. The characteristic storm updraft, defined as the ratio of the area‐averaged rainfall rate to the volume‐averaged precipitation content, was about 5 ms^?1 for all storms. The time evolution of integral storm parameters is also presented and related to the overall storm development. The precipitation production values observed in the Lowveld storms compares well with previous estimates reported for large thunderstorms observed in Alberta and New England.