温州地区一次暴雨过程分析及数值模拟

针对WRF中尺度数值模式较少应用在地形复杂地区,而在浙江省南部山区的温州地区则更缺乏实际应用的案例,利用WRF模式,对2010年5月13～14日温州地区出现的一次暴雨过程进行数值模拟,并利用模式输出的高分辨率资料进行诊断分析.通过分析模拟结果,验证了WRF模式较好地模拟了此次降水过程的时空分布特征,但仍存在着部分区域降水量偏高,暴雨落区偏差等误差.研究结果表明,暴雨中心的强上升运动及低层辐合、高层辐散的配置有利于中尺度对流系统的发生发展,高低空急流耦合是此次暴雨爆发的重要机制.暴雨区域高湿高能环境的维持,保证了暴雨过程的能量输送,也是此次暴雨能够长时间地持续发生的必要条件之一.降水量最大区域的中心与对流层中的最大垂直速度中心都出现在同一区域,而在垂直速度最大区域的两侧都存在有一个垂直的次级环流,这些有利条件对维持强烈的上升运动起关键作用.     

温州地区一次暴雨过程分析及数值模拟

针对WRF中尺度数值模式较少应用在地形复杂地区,而在浙江省南部山区的温州地区则更缺乏实际应用的案例,利用WRF模式,对2010年5月13～14日温州地区出现的一次暴雨过程进行数值模拟,并利用模式输出的高分辨率资料进行诊断分析.通过分析模拟结果,验证了WRF模式较好地模拟了此次降水过程的时空分布特征,但仍存在着部分区域降水量偏高,暴雨落区偏差等误差.研究结果表明,暴雨中心的强上升运动及低层辐合、高层辐散的配置有利于中尺度对流系统的发生发展,高低空急流耦合是此次暴雨爆发的重要机制.暴雨区域高湿高能环境的维持,保证了暴雨过程的能量输送,也是此次暴雨能够长时间地持续发生的必要条件之一.降水量最大区域的中心与对流层中的最大垂直速度中心都出现在同一区域,而在垂直速度最大区域的两侧都存在有一个垂直的次级环流,这些有利条件对维持强烈的上升运动起关键作用.     

南风气流对四川盆地西部突发性暴雨影响的数值试验

用η模式对１９９５年８月２４日四川盆地西部的突发性暴雨，进行了数值模拟和南风减速的数值试验。由模拟和试验比较分析得出以下几点认识：１．对流层低层的云南、贵州到四川南部南风气流的加大，对四川盆地西部的暴雨区，特别是大暴雨区的扩大和强度的增强起着重要作用。２．由南风气流的加强造成暴雨加强的动、热力机制主要是，使暴雨区及其附近区域上空，对流层的中、低层流场的辐合、上升运动加强；对流层的低层形成水汽通量的辐合区。３．对流层低层南风气流的加强，可影响对流层中层的暖湿程度减弱，从而加强了对流层低－中层内的对流性不稳定     

暴雨内涝危险性情景模拟方法研究——以上海中心城区为例

本文采用地理信息系统技术（GIS）与水文/水动力学相结合的方法,使用修正的SCS模型进行产流模拟,利用局部等体积法和水动力模型进行汇流模拟,建立了基于情景的城市暴雨内涝危险性模拟工具,并对相同雨强情景下不同汇流模型的模拟结果与精度进行了对比分析。结果表明：（1）局部等体积法计算简单,对降雨的时程分配较为敏感,峰前历时和雨强对结果影响显著,较适合于雨型确定的城市暴雨内涝危险性快速模拟;水动力模型计算复杂,与整个降雨历时存在明显关系,模拟精度较高。（2）利用台风“麦莎”带来强降雨导致的积水实测数据,对模拟结果进行对比和验证,表明本文建立的水动力模型法模拟结果与实测降雨积水更为接近,更适合上海中心城区暴雨内涝危险性情景模拟。     

湖南“6.19＂暴雨过程的中尺度特征分析

针对2010年6月19～20日湖南一次大范围暴雨过程,利用常规观测资料、FY-2E卫星TBB资料、NCEP1°×1°格点资料及WRF模式输出的高分辨率资料,采用数值模拟和诊断分析相结合的方法,综合分析了此次暴雨过程.结果表明：东移短波槽及低层低涡是此次暴雨的主要影响系统;卫星资料分析显示,α中尺度对流云团前部不断有β中尺度扰动分裂和发展;WRF模式模拟结果表明,此次大范围暴雨过程是多尺度系统影响的结果,在有利的大尺度环流背景下,激发了中小尺度对流系统的活动,导致强降水带中出现了多个中尺度扰动和与此对应的中尺度雨团有组织的活动,是暴雨发生的主要原因;中尺度雨团对应着强上升气流柱、正涡度柱和低层强辐合、高层强辐散的散度结构.     

隧道围岩变形监测反演分析及稳定性研究

针对有限元方法模拟隧道开挖过程受参数取值影响导致精度降低问题，提出利用BP神经网络和数值模拟方法来对隧道围岩力学参数进行反演分析。对比实测值与模拟值，证明了反演参数的可靠性，并得到隧道的最大最小主应力和位移变化值；利用强度折减法，通过数值模拟计算相应的拱顶沉降值，通过离散点拟合来求得隧道围岩的自稳系数，分析隧道围岩的稳定性。     

利用GPS水汽资料对重庆一次暴雨过程的综合分析

为了更好地预报重庆地区暴雨发生的时间和落区,利用重庆地区基于GPS得到的GPS-PWV(Precipitable Water Vapor)资料结合WRF数值模拟对2012年一次暴雨过程进行综合分析,分析此次过程中重庆地区GPS-PWV的变化特征、不稳定能量以及动力抬升条件与降水的关系。研究结果表明:此次过程在降水之前有18小时到35小时的水汽聚集过程,在接近水汽通道的迎风坡面降水量转化率较高。综合分析不稳定能量和水汽通量散度的变化结合GPS-PWV提供的水汽场能够更好地预报降水发生的时间和落区。     

成都“2008.9.24”暴雨的中尺度数值模拟

为了更好的研究四川成都地区2008年9月23日～25日强降水天气的物理机制,利用中尺度天气预报模式(Weather Research Forecast,WRF)模拟了此次过程,并结合FY-2号C星云图资料,对过程的发生、发展过程进行分析后发现,此次暴雨过程为低层弱低压辐合,同时台风"黑格比"登陆后带来大量水汽,并借助西伸的西太平洋副高源源不断的向四川盆地输送,另外存在来自南方孟加拉湾水汽的持续供应,对流云团形成于地面低压与西伸副高的交界处,并沿着副高边缘由西南向东北移动,雨带分布在副高边缘气压梯度大的地方,模拟结果与观测事实基本相符。利用WRF模式模拟暴雨产生的环流形势,配合卫星云图,能进一步揭示暴雨产生的物理机制,可作为深化暴雨研究的有效手段。     

基于WRF模式的陕西两次区域性秋季暴雨的数值模拟

2005年陕西秋淋天气明显且偏晚,通过WRF模式对秋淋期间的两次区域性暴雨模拟结果显示,暴雨雨带走向、强降雨中心位置以及强降水出现的时间段等都与实况基本吻合,预报时效可达36-48小时;模式能成功地模拟出暴雨的主要影响系统和不同时段的风场演变变化.分析表明,利用该模式可以对不同类型暴雨进行机理分析和研究,能够作为未来客观预报陕西转折性天气和暴雨天气一种新的技术手段和工具.     

FY-3A卫星MWHS资料同化及四川盆地暴雨数值模拟分析

为研究中国FY-3A(风云三号A卫星)资料在数值模式中对暴雨模拟的作用,以2012年07月03日发生在四川盆地的一次暴雨过程为个例。利用WRF(V3.3)模式和三维变分同化系统WRF-3DVAR,对FY-3A的MWHS(微波湿度计)资料进行同化试验研究。研究结果表明:同化MWHS资料后,相比控制试验,(1)同化方案A在降水区出现+3K的正增量中心和+10%的正增量湿中心,改善了大气能量场和湿度场信息;(2)同化方案A提高了降水区垂直速度和涡度场的数值和分布位置,并较好的刻画出风场的辐合辐散区域和水汽通量高值中心,准确模拟出水汽通量的中心位置;(3)对此次暴雨个例试验研究,在中东部、东北部主降水区,同化方案A模拟降水区分布形式较好,尤其是降水强度得到较大提高降水值达230mm,非常接近实况降水值(227mm),同化MWHS资料可以较好提高WRF模式降水预报能力。     

中国北方典型农牧交错区的土壤风蚀危险度研究

土壤风蚀及其引发的土地退化、沙化和沙尘暴是中国北方严重的环境问题之一。通过定量评估中国北方典型农牧交错地区土壤风蚀危险程度,可以为区域生态环境的保护和生态修复提供科学支撑。本文基于文献调研、兼顾数据的可获得性,建立了包括风场强度、植被覆盖率、地形起伏度、土壤干燥度等因子在内的风蚀危险程度评价指标体系。同时,依据遥感参数反演和地面气象观测数据,在地理信息系统技术支持下,形成了上述因子的空间分布数据。另外,利用层次分析方法,构建土壤风蚀危险度评价模型,得到研究区土壤风蚀危险度的空间分布。最后,结合研究区土地利用数据,探讨了风蚀危险度空间分布格局的自然环境和土地利用背景。研究表明:研究区土壤风蚀极险型区域面积为1.47×10⁴km²,强险型区域面积为6.09×10⁴km²,危险型区域面积为3.47×10⁴km²,轻险型区域面积为3.45×10⁴km²,无险型区域面积为2.19×10⁴km²。本区土壤风蚀危险度呈现出从东南到西北逐渐增强的趋势,这与区域的植被、气候,以及土地利用的空间格局具有内在的有机联系。     

An Elliptical Wind Field Model of Typhoons

1　Introduction　Thestormsurgeisoneofthemostimportantphe nomenathatendangerthecoastalengineeringfacili ties .Everyyearthereareabout 1 2tropicalcyclonesmakinglandfallatthemainlandofChinafromMaytoOctober (MuandTu ,2 0 0 0 ) .Whentheastronomictideishigh ,the…     

中国风能资源空间分布的估算

风能是一种清洁的可再生能源,是太阳能的一种转化形式,但风能开发利用的成本比太阳能开发利用的成本要低,它是可再生能源中最具开发前景的一种能源。科学、准确地估算我国风能潜力及其空间分布是国家对风能资源开发中一项极其重要的基础性工作。本文利用全国395个气象站10年、每日4次的气象观测数据,计算了每个气象站所在地区常年有效风能密度和有效风时数,在此基础上,通过空间内插,形成全国范围的风能密度和有效风时数分布数据。结果显示,在全国范围内:有效风能密度大于150W/m²、100～150W/m²、50～100W/m²、小于50W/m²的区域面积占全国国土总面积的百分比分别为2.51%、16.45%、53.39%和27.65%;有效风时数大于5 000h、4 000～5 000h、2 000～4 000h、小于2 000h的区域面积占国土总面积的百分比分别为5.28%、22.19%、53.54%和18.98%。用风能密度和有效风时数两个指标分别表达风能资源潜力虽然存在局部差异,但在总体态势上基本一致,二者之间的相关系数,...     

Trends of sea surface wind energy over the South China Sea

Studies on climate change typically consider temperature and precipitation over extended periods but less so the wind.We used the Cross-Calibrated Multi-Platform(CCMP)24-year wind field data set to investigate the trends of wind energy over the South China Sea during 1988-2011.The results reveal a clear trend of increase in wind power density for each of three base statistics(i.e.,mean,90 th percentile and 99 th percentile)in all seasons and for annual means.The trends of wind power density showed obvious temporal and spatial variations.The magnitude of the trends was greatest in winter,intermediate in spring,and smallest in summer and autumn.A greater trend of increase was found in the northern areas of the South China Sea than in southern parts.The magnitude of the annual and seasonal trends over the South China Sea was larger in extreme high events(i.e.,90~(th) and 99~(th) percentiles)compared to the mean conditions.Sea surface temperature showed a negative correlation with the variability of wind power density over the majority of the South China Sea in all seasons and annual means,except for winter(41.7%).     

Study of the Wind Conditions in the South China Sea and Its Adjacent Sea Area

An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,wind climate,wind energy assess-ment,and extreme wind speed in the South China Sea(SCS)are investigated in detail in this study.The data are obtained from the China Meteorological Administration,the European Centre for Medium-range Weather Forecasts,and the National Centers for Envi-ronmental Prediction.The offshore wind energy potential is analyzed at five sites near the coast.The spatial and monthly frequencies of tropical cyclones for different intensity categories are analyzed.The extreme wind speed is fitted by five distribution models,and the generalized extreme value(GEV)distribution is selected as the most suitable function according to the goodness of fit.The spa-tial distributions of extreme wind speeds in the SCS are plotted on the basis of the GEV distribution and ERA5 data sets.The influ-ences of the distribution models and data sets on the calculated results are discussed.Moreover,the monthly extreme wind speed and comparison with the results of previous studies are analyzed.This study provides a reference for the design of wind turbines.     

The formation of wind curl in the marine atmosphere boundary layer over the East China Sea Kuroshio in spring

Various data are used to investigate the characteristics of the surface wind field and rainfall on the East China Sea Kuroshio (ESK) in March and April, 2011. In March, the wind speed maximum shows over the ESK front (ESKF) in the 10 meter wind field, which agrees with the thermal wind effect. A wind curl center is generated on the warm flank of the ESKF. The winds are much weaker in April, so is the wind curl. A rainband exists over the ESKF in both the months. The Weather Research and Forecasting (WRF) model is used for further researches. The winds on the top of the marine atmosphere boundary layer (MABL) indicate that in March, a positive wind curl is generated in the whole MABL over the warm flank of the ESKF. The thermal wind effect forced by the strong SST gradient overlying the background wind leads to strong surface northeasterly winds on the ESKF, and a positive shearing vorticity is created over the warm flank of the ESKF to generate wind curl. In the smoothed sea surface temperature experiment, the presence of the ESKF is responsible for the strong northeast winds in the ESKF, and essential for the distribution of the rainfall centers in March, which confirms the mechanism above. The same simulation is made for April, 2011, and the responses from the MABL become weak. The low background wind speed weakens the effect of the thermal wind, thus no strong Ekman pumping is helpful for precipitation. There is no big difference in rainfall between the control run and the smooth SST run. Decomposition of the wind vector shows that local wind acceleration induced by the thermal wind effect along with the variations in wind direction is responsible for the pronounced wind curl/divergence over the ESKF.     

Comparison of two wind algorithms of ENVISAT ASAR at high wind

Two wind algorithms of ENVISAT advanced synthetic aperture radar (ASAR), i. e. CMOD4 model from the European Space Agency (ESA) and CMOD IFR2 model from Quilfen et al., are compared in this paper. The wind direction is estimated from orientation of low and linear signatures in the ASAR imagery. The wind direction has inherently a 180° ambiguity since only a single ASAR image is used. The 180° ambiguity is eliminated by using the buoy data from the NOAA (National Oceanic and Atmospheric Administration) buoys moored in the Pacific. Wind speed is obtained with the two wind algorithms using both estimated wind direction and normalized radar cross section (NRCS). The retrieved wind results agree well with the data from Quikscat. The root mean square error (RMSE) of wind direction is 2.80? The RMSEs of wind speed from CMOD4 model and CMOD_IFR2 model are 1.09 m/s and 0.60 m/s, respectively. The results indicate that the CMOD_IFR2 model is slight better than CMOD4 model at high wind.     

Industrial arrangement of large-scale, non-grid-connected wind power industrial zones in coastal areas of China

Constructing an industrial system for a large-scale,non-grid-connected wind power industry is a key step towards the diverse utilization of wind power.However,wind power exploitation is not only a technical challenge but an industrial problem as well.The objective of this study is to introduce a concept of large-scale,non-grid-connected wind power(LSNGCWP) industrial zones and establish an evaluation model to assess their industrial arrangement.The data of wind energy,industry,nature resources and socio-economy were collected in this study.Using spatial overlay analysis of geographic information system,this study proposes a spatial arrangement of the LSNGCWP indus-trial zones in the coastal areas of China,which could be summarized as the ’one line and three circles’ structure,which will contribute to the optimization of the industrial structure,advance the wind power technology,coordinate the multi-industrial cooperation,and upgrade the industrial transformation of China’s coastal areas.     

2011年7月陕西一次区域性暴雨过程分析

为了探索陕西区域性暴雨发生发展的机制,提高暴雨预报准确率,利用实况高空观测、自动站观测资料和NCEP1°×1°格点资料,采用天气学诊断方法,对2011年7月28日陕西区域性暴雨过程进行分析。结果表明:500hPa西风槽、西太平洋副热带高压、700hPa及其以下的切变是这次暴雨的主要影响系统,低层大风速带和南海台风的远距离作用是暴雨增幅的重要因子;暴雨区具有强的能量锋和对流不稳定,降水产生在850hPa温湿能等值线密集区偏高值一侧;水汽主要来源于孟加拉湾和南海台风外围;辐合、辐散中心下移是强降水即将发生的一个信号,850hPa以下出现比湿猛增现象对位于秦岭以北的渭河流域暴雨预报有一定指导意义。     

Wind energy input in coastal seas east of China

This study investigates the wind energy input, an important source of mechanical energy, in the coastal seas east of China. Using the wind field from the high-resolution sea surface meteorology dataset in the Bohai Sea, Yellow Sea, and East China Sea, we studied the wind energy input through surface ageostrophic currents and surface waves. Using a simple analytical formula for the Ekman Spiral with timedependent wind, the wind energy input through ageostrophic currents was estimated at ～22 GW averaged from 1960 to 2007, and through use of an empirical formula, the wind energy input through surface waves was estimated at ～169 GW. We also examined the seasonal variation and long-term tendency of mechanical energy from wind stress, and found that the wind energy input to the East China Sea decreased before the 1980s, and then subsequently increased, which is contrary to what has been found for the Bohai Sea and Yellow Sea. More complicated physical processes and varying diffusivity need to be taken into account in future studies.