三类航危天气预报技术及业务系统研究
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摘要
从上世纪20年代起,航空气象保障就逐渐得到国内外航空公司和气象科技工作者的广泛关注,这主要是因为天气是影响航空安全、正常与效率的最重要因素之一,给航空企业和飞行员提供及时、准确的天气预报,进而提升飞行的安全性是航空气象服务的职责和任务。航空工业的迅猛发展,对航空气象服务提出了更新更高的要求。在诸多的天气因素中,雷暴被称为夏季飞行安全的杀手；雾造成的低能见度常造成机场关闭；风是对飞行影响最大的气象要素,大风可影响飞机正常起降并损坏机场设施。因此,关于这三类典型航空危险性天气的短期预报技术进行深入研究,对于进一步提高航空气象预报业务能力具有重要的现实意义。本论文首先分析了近30年我国三类航危天气的时空分布特征并总结其主要天气型,为了解其气候概率、发生机制以及提高预报水平提供有效的支持；然后通过深入分析三类航危天气的主要物理因子,采用事件概率回归和BP神经网络两种方法建立了三类航危天气的动力-统计预报模型,并对模型的预报性能进行了检验和比较；最后基于软件开发技术构建了能够用于航空气象服务的三类航危天气短期预报业务化系统,目前已投入准业务化运行。主要研究结果如下：
1、在极端天气气候事件增多的大背景下,研究表明,我国大陆雷暴、雾霾和大风存在不同的时空变化特征。我国雷暴高发区主要位于东南沿海、华南、西南以及青藏高原东部地区,近30年来雷暴日数经历了两个相对多发期和一个相对少发期。雾在我国南方和沿海地区频发,多雾地区主要分布在辽东半岛、山东半岛沿海、福建西北及沿海、江浙沿海、四川盆地以及云南西南部,长江以南地区的雾日数明显多于长江以北地区。近30年来大雾日数整体呈现出减少的趋势,而轻雾日数呈增加趋势。霾日数具有东多西少的空间分布特征,东部地区的霾集中在三个多发区,分别为长江中下游、华北和华南地区；2001年以后霾日数呈现出增加的趋势。大风主要出现在北方地区,频发区主要位于新疆北部、内蒙古中西部、青藏高原东部以及东部沿海区域。
2、通过统计分析,筛选出了不同预报对象的主要影响物理因子。雷暴预报方程中贡献大的因子反映了水汽含量、动力条件和不稳定能量。雾霾预报方程中的因子主要有水汽含量、全风速、垂直速度和稳定度指数等。大风预报方程中的因子主要有模式输出的U、V分量及对流层中下层的全风速,其次为垂直速度,槽的强度、温度和涡度平流也在方程中占较大比重。
3、针对全国不同站点,分别建立了基于线性回归理论的三类航危天气事件概率回归预报模型,并对其预报性能进行了检验。对于三类天气,TS评分值在不同区域和不同预报时效均高于气候概率值。雷暴在华南、东部沿海和东北地区预报效果相比其他地区更好。对水平能见度小于10km的雾霾天气,预报评分的空间分布随季节变化而不同,预报效果较好的站点在春季主要分布在西北东南部、华北南部、黄淮东部和江淮中东部；夏季主要分布在两大区域,分别是东北地区南部、华北地区和江南中西部、华南中西部地区；秋冬季分布在东北地区南部、华北地区、黄淮地区以及江淮东部；且冬季在东南沿海、华南预报效果好于秋季。对水平能见度小于1km的雾,预报评分和其气候概率空间分布有较好的一致性,预报评分在东北地区南部、黄淮、江南东部以及西南地区较高。大风天气的预报评分在北方地区整体较高,秋季在东南沿海具有较高的评分。
4、针对全国不同站点,分别建立了基于非线性动力系统的三类航危天气BP神经网络预报模型,检验了其预报性能并与事件概率回归预报模型进行对比。整体来看,由于输入端预报因子相同,两种预报模型试预报评分的空间分布基本一致。对于雷暴天气,随着预报时效的延长,预报评分明显减小,夜晚的评分较白天偏低,且神经网络预报模型效果明显好于事件概率回归预报模型,在白天的预报时段内更为明显。雾霾神经网络模型对水平能见度小于1km雾的预报好于事件概率回归预报模型。对于大风天气,事件概率回归模型预报效果更好。个例预报分析表明两种预报模型对于三类天气的区域性特征的预报与实况都有较好的一致性。
5、三类航危天气短期业务预报系统的建立及准业务化运行。针对当前航空气象业务保障中短期预报任务特点和预报需求,综合利用数值预报产品释用技术和软件开发技术,开发了三类航危天气短期业务化预报系统。系统设计共分为五个模块,分别是预报模型构建模块、数据采集入库模块、基本要素和特征物理量计算入库模块、预报数据自动生成模块和预报信息显示模块。本系统实现了数据自动下载入库、单要素或多要素、单一时间或历史上某一时间段的自动预报、地理信息数据接入、预报结果图形化显示等多个功能。本预报系统具有建模方法先进、客观化与自动化程度高、操作简便等优点,可以辅助预报员高效客观地完成三类航危天气短期预报的制作。目前本系统已集成到航空气象灾害预报支持服务系统进行准业务化运行。
Aviation meteorological assurances have attracted wide attention from the airlines and meteorological scientists and technology workers around the world since1920's. This is mainly because that weather is one of the most important factors which affect aviation safety, normal and efficiency. It's the aviation meteorological service's responsibility and task to provide timely and accurate weather forecast for airlines and pilots to enhance flight safety. With the rapid development of the aviation industry, a newer and higher requirement for aviation weather service is needed. Thunderstorms are known as the killer of summer flight in many weather factors. Low visibility caused by fog often results in the closure of the airports. The bump caused by vertical wind shear is the most dangerous mereorological factors which affect flight safety. Gale not only affects the aircraft's normal taking off and landing but also damages airport facilities. Therefore, the in-depth study on the short-term forecast for three kinds of dangerous weather has important practical significance for improving aviation weather forecasting operational capacity. In this paper,the temporal and spatial distribution of three typical aviation dangerous weather for nearly30years were analyzed and their main weather patterns were summarized which can provide effective supports to understand the climatic probability of three typical aviation dangerous weather, pathogenesis and to improve the forecast level;after that analyzed the main physical factors of the three typical aviation dangerous weather and establish the dynamic-statical model using the event probability regression and BP neural network, in addition, the forecasting performancies of two model were tested. at last, the short-term forecasting operational system of three typical aviation dangerous weather which can be used for aviation meteorological services was built basing on software development techniques. The system is being used for quasi-operational running.The main conclusions are as follows:
1. The results show that there were different spatial and temporal variations of thunderstorms, fog-haze and gale in the past three decades in mainland China in the context of global extreme weather events' increasing. Thunderstorms are more frequent in the southeast coast of China, southwest and eastern Tibetan Plateau region. In the past30years the annual thunderstorm days showed two periods of higher value and a lower period. Fog occurs frequently in south China and the coastal areas such as Liaodong Peninsula, Shandong Peninsula, Fujian, Jiangsu and Zhejiang coastal region, Sichuan Basin and the southwest of Yunnan. The number of foggy days in the south of the Yangtze River region is more significantly than the northern part of the Yangtze River, foggy days in the past30years showed a decreasing trend, howerver the number of mist days showed an increasing trend. Spatial distribution of Haze day showed more in east China and less in west. In east China area, there are three more frequent centers as follows:the middle-lower reaches of the Yangtze River, northern and southern China. Haze days showed an increase trend after2001. Gale mainly occurred in the northern China; such as northern Xinjiang, central and western Inner Mongolia, eastern Tibetan Plateau and the eastern coastal region.
2. The main physical factors of the different forecasting objects had been selected by using statistical analysis. The top factors in the thunderstorm forecasting equations are water vapor content, dynamic conditions and unstable energy. The top factors in the fog-haze forecasting equations are water vapor content, wind speed and stability index. The top factors in the gale forecasting equations are U, V component of the model output, the middle and lower troposphere wind speed calculated by meridinal wind and zonal wind. In addition, the strength of the trough, temperature and vorticity advection also account for a larger proportion.
3. Established the probability regression forecasting models basing on linear regression theory for three typical dangerous weather and the models's forecasting performances were tested.the TS score is higher than the climate probability value in different regions and different prediction time. The heidike score of thunderstorms forecast was higher in south China, the eastern coast and northeast. The sites which have good prediction effect on haze and mist that horizontal visibility is less than10km haze are mainly distributed in the southeast part of northwest China, the northern part of north China, the eastern part of Huanghuai, and the middle and eastern past of jiang-huai River in spring. In summer, there are two regions, one is southern part of northeast China and north China, another is the middle and western part of south of the Yangtze River. In autumn and winter, the sites having good forecast performance distribute in the southern part of northeast China, north China,Huang-huai area and eastern part of Jiang-huai and better in winter than autumn in the southeast coast and south China. The spatial distribution of fog's score demonstrates good consistency with the spatial distribution of the number of foggy days.The score is higher in northeast China,huang-huai,eastern part of south of the Yangtze River and southwest region. The gale's forecast performance shows better overall in north China and the score is higher in the southeast coastal in autumn。
4. Established the BP neural network forecasting models basing on nonlinear dynamic system. for three typical dangerous weather.the models's forecasting performances were tested and compared with the REEP forecasting models.Overall, because of the same input factors of the two models, the spatial distribution of two prediction model scores is basically same. For thunderstorm, With the extension of perdition time the score significantly reduced fast, the score at night is lower than that during the day, and the neural network prediction model is obviously better than regression estimation of event probabilities, which is more obvious in the day time forecast period. BP Neural Network model is better for fog which horizontal visibility less than lkm. The regression estimation of event probabilities showed better performance on gale forecasting. The cases forecast results analysis indicates the regional characteristics of the three types of weather forecasts have more consistent with observations.
5. Developed short-term forecasting system and achieved a quasi-operational running. According to characteristics of short-term forecasting task for the security of aviation weather services and forecast demand, Depending on the interpolation and explanation method of numerical forecast products and software development technology, the operational forecasting system was developed. The system is divided into five modules including forecasting model construction, data acquisition and storage, basic and characteristic physical element's computing, automatic generation of forecast data, geographic information data access and forecast information displaying. The system has many advantages such as modeling approach reasonably, operating easily and having a higher degree of objection and automation etc. It can help weather forecasters to make the forecasts of three types of dangerous weather efficiently. The system has been integrated into the aviation weather hazard forecasting support system for a quasi-operational running.
1、在极端天气气候事件增多的大背景下,研究表明,我国大陆雷暴、雾霾和大风存在不同的时空变化特征。我国雷暴高发区主要位于东南沿海、华南、西南以及青藏高原东部地区,近30年来雷暴日数经历了两个相对多发期和一个相对少发期。雾在我国南方和沿海地区频发,多雾地区主要分布在辽东半岛、山东半岛沿海、福建西北及沿海、江浙沿海、四川盆地以及云南西南部,长江以南地区的雾日数明显多于长江以北地区。近30年来大雾日数整体呈现出减少的趋势,而轻雾日数呈增加趋势。霾日数具有东多西少的空间分布特征,东部地区的霾集中在三个多发区,分别为长江中下游、华北和华南地区；2001年以后霾日数呈现出增加的趋势。大风主要出现在北方地区,频发区主要位于新疆北部、内蒙古中西部、青藏高原东部以及东部沿海区域。
2、通过统计分析,筛选出了不同预报对象的主要影响物理因子。雷暴预报方程中贡献大的因子反映了水汽含量、动力条件和不稳定能量。雾霾预报方程中的因子主要有水汽含量、全风速、垂直速度和稳定度指数等。大风预报方程中的因子主要有模式输出的U、V分量及对流层中下层的全风速,其次为垂直速度,槽的强度、温度和涡度平流也在方程中占较大比重。
3、针对全国不同站点,分别建立了基于线性回归理论的三类航危天气事件概率回归预报模型,并对其预报性能进行了检验。对于三类天气,TS评分值在不同区域和不同预报时效均高于气候概率值。雷暴在华南、东部沿海和东北地区预报效果相比其他地区更好。对水平能见度小于10km的雾霾天气,预报评分的空间分布随季节变化而不同,预报效果较好的站点在春季主要分布在西北东南部、华北南部、黄淮东部和江淮中东部；夏季主要分布在两大区域,分别是东北地区南部、华北地区和江南中西部、华南中西部地区；秋冬季分布在东北地区南部、华北地区、黄淮地区以及江淮东部；且冬季在东南沿海、华南预报效果好于秋季。对水平能见度小于1km的雾,预报评分和其气候概率空间分布有较好的一致性,预报评分在东北地区南部、黄淮、江南东部以及西南地区较高。大风天气的预报评分在北方地区整体较高,秋季在东南沿海具有较高的评分。
4、针对全国不同站点,分别建立了基于非线性动力系统的三类航危天气BP神经网络预报模型,检验了其预报性能并与事件概率回归预报模型进行对比。整体来看,由于输入端预报因子相同,两种预报模型试预报评分的空间分布基本一致。对于雷暴天气,随着预报时效的延长,预报评分明显减小,夜晚的评分较白天偏低,且神经网络预报模型效果明显好于事件概率回归预报模型,在白天的预报时段内更为明显。雾霾神经网络模型对水平能见度小于1km雾的预报好于事件概率回归预报模型。对于大风天气,事件概率回归模型预报效果更好。个例预报分析表明两种预报模型对于三类天气的区域性特征的预报与实况都有较好的一致性。
5、三类航危天气短期业务预报系统的建立及准业务化运行。针对当前航空气象业务保障中短期预报任务特点和预报需求,综合利用数值预报产品释用技术和软件开发技术,开发了三类航危天气短期业务化预报系统。系统设计共分为五个模块,分别是预报模型构建模块、数据采集入库模块、基本要素和特征物理量计算入库模块、预报数据自动生成模块和预报信息显示模块。本系统实现了数据自动下载入库、单要素或多要素、单一时间或历史上某一时间段的自动预报、地理信息数据接入、预报结果图形化显示等多个功能。本预报系统具有建模方法先进、客观化与自动化程度高、操作简便等优点,可以辅助预报员高效客观地完成三类航危天气短期预报的制作。目前本系统已集成到航空气象灾害预报支持服务系统进行准业务化运行。
Aviation meteorological assurances have attracted wide attention from the airlines and meteorological scientists and technology workers around the world since1920's. This is mainly because that weather is one of the most important factors which affect aviation safety, normal and efficiency. It's the aviation meteorological service's responsibility and task to provide timely and accurate weather forecast for airlines and pilots to enhance flight safety. With the rapid development of the aviation industry, a newer and higher requirement for aviation weather service is needed. Thunderstorms are known as the killer of summer flight in many weather factors. Low visibility caused by fog often results in the closure of the airports. The bump caused by vertical wind shear is the most dangerous mereorological factors which affect flight safety. Gale not only affects the aircraft's normal taking off and landing but also damages airport facilities. Therefore, the in-depth study on the short-term forecast for three kinds of dangerous weather has important practical significance for improving aviation weather forecasting operational capacity. In this paper,the temporal and spatial distribution of three typical aviation dangerous weather for nearly30years were analyzed and their main weather patterns were summarized which can provide effective supports to understand the climatic probability of three typical aviation dangerous weather, pathogenesis and to improve the forecast level;after that analyzed the main physical factors of the three typical aviation dangerous weather and establish the dynamic-statical model using the event probability regression and BP neural network, in addition, the forecasting performancies of two model were tested. at last, the short-term forecasting operational system of three typical aviation dangerous weather which can be used for aviation meteorological services was built basing on software development techniques. The system is being used for quasi-operational running.The main conclusions are as follows:
1. The results show that there were different spatial and temporal variations of thunderstorms, fog-haze and gale in the past three decades in mainland China in the context of global extreme weather events' increasing. Thunderstorms are more frequent in the southeast coast of China, southwest and eastern Tibetan Plateau region. In the past30years the annual thunderstorm days showed two periods of higher value and a lower period. Fog occurs frequently in south China and the coastal areas such as Liaodong Peninsula, Shandong Peninsula, Fujian, Jiangsu and Zhejiang coastal region, Sichuan Basin and the southwest of Yunnan. The number of foggy days in the south of the Yangtze River region is more significantly than the northern part of the Yangtze River, foggy days in the past30years showed a decreasing trend, howerver the number of mist days showed an increasing trend. Spatial distribution of Haze day showed more in east China and less in west. In east China area, there are three more frequent centers as follows:the middle-lower reaches of the Yangtze River, northern and southern China. Haze days showed an increase trend after2001. Gale mainly occurred in the northern China; such as northern Xinjiang, central and western Inner Mongolia, eastern Tibetan Plateau and the eastern coastal region.
2. The main physical factors of the different forecasting objects had been selected by using statistical analysis. The top factors in the thunderstorm forecasting equations are water vapor content, dynamic conditions and unstable energy. The top factors in the fog-haze forecasting equations are water vapor content, wind speed and stability index. The top factors in the gale forecasting equations are U, V component of the model output, the middle and lower troposphere wind speed calculated by meridinal wind and zonal wind. In addition, the strength of the trough, temperature and vorticity advection also account for a larger proportion.
3. Established the probability regression forecasting models basing on linear regression theory for three typical dangerous weather and the models's forecasting performances were tested.the TS score is higher than the climate probability value in different regions and different prediction time. The heidike score of thunderstorms forecast was higher in south China, the eastern coast and northeast. The sites which have good prediction effect on haze and mist that horizontal visibility is less than10km haze are mainly distributed in the southeast part of northwest China, the northern part of north China, the eastern part of Huanghuai, and the middle and eastern past of jiang-huai River in spring. In summer, there are two regions, one is southern part of northeast China and north China, another is the middle and western part of south of the Yangtze River. In autumn and winter, the sites having good forecast performance distribute in the southern part of northeast China, north China,Huang-huai area and eastern part of Jiang-huai and better in winter than autumn in the southeast coast and south China. The spatial distribution of fog's score demonstrates good consistency with the spatial distribution of the number of foggy days.The score is higher in northeast China,huang-huai,eastern part of south of the Yangtze River and southwest region. The gale's forecast performance shows better overall in north China and the score is higher in the southeast coastal in autumn。
4. Established the BP neural network forecasting models basing on nonlinear dynamic system. for three typical dangerous weather.the models's forecasting performances were tested and compared with the REEP forecasting models.Overall, because of the same input factors of the two models, the spatial distribution of two prediction model scores is basically same. For thunderstorm, With the extension of perdition time the score significantly reduced fast, the score at night is lower than that during the day, and the neural network prediction model is obviously better than regression estimation of event probabilities, which is more obvious in the day time forecast period. BP Neural Network model is better for fog which horizontal visibility less than lkm. The regression estimation of event probabilities showed better performance on gale forecasting. The cases forecast results analysis indicates the regional characteristics of the three types of weather forecasts have more consistent with observations.
5. Developed short-term forecasting system and achieved a quasi-operational running. According to characteristics of short-term forecasting task for the security of aviation weather services and forecast demand, Depending on the interpolation and explanation method of numerical forecast products and software development technology, the operational forecasting system was developed. The system is divided into five modules including forecasting model construction, data acquisition and storage, basic and characteristic physical element's computing, automatic generation of forecast data, geographic information data access and forecast information displaying. The system has many advantages such as modeling approach reasonably, operating easily and having a higher degree of objection and automation etc. It can help weather forecasters to make the forecasts of three types of dangerous weather efficiently. The system has been integrated into the aviation weather hazard forecasting support system for a quasi-operational running.
引文
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