集合卡尔曼滤波在流域水文模型流量预报中的应用

针对流域降雨入渗过程,引入集合卡尔曼滤波(EnKF)理论,视整个边坡流域为一个随机动态系统,将边坡流域流量观测值作为系统的输出,用集合卡尔曼滤波模型来描述系统的状态;结合流域流量计算方法,实现水文模型参数的随机动态估计,在有效获得待估参数的同时还给出估计值的不确定性.通过数值算例表明,集合卡尔曼滤波可以有效地对含噪声的量测数据进行处理,能够跟踪水文模型的动态变化.相对于常用最优化算法,集合卡尔曼滤波同时给出反演结果和先验知识的后验分布,显示出更好的实时性和可靠性.     

金沙江下游多种面雨量集成预报方法的对比分析

集成方法有利于提高降水要素预报的准确性和可预报性。本文基于格点实况资料和智能网格预报、西南区域数值预报、ECMWF模式预报、GRAPES模式预报产品,以面雨量为研究对象,采用多元回归法、BP神经网络法、评分权重法、加权集成预报法和算术平均法,得到集成面雨量预报,再运用平均绝对误差、模糊评分、正确率、TS评分、偏差分析等方法,对2020年4—10月金沙江下游面雨量预报效果进行对比分析。结果表明：多元回归集成法和BP神经网络法的预报效果总体上优于其他几种集成方法。在考虑流域面雨量的预报量级时,下游可以采用预报量级较小的模式和集成方法。集成后偏差百分比均有降低,且多元回归法和BP神经网络法对预报量级较小的模式有矫正作用。在面雨量有无、小雨和中雨预报中,多元回归法集成效果较好,在大雨量级预报中,BP神经网络法集成效果较好。这些结论可为流域面雨量预报提供参考借鉴。     

东江流域雨水集中期气候特征及2006年降水成因分析

利用湘东南东江流域1959～2006年共48年的历史资料,采用统计分析方法,对东江流域历年雨水集中期的气候规律以及2006年雨水集中期的特征及成因进行了分析。分析结果表明:东江流域历年的雨水集中期多出现在6月和8月;东江流域雨水集中期的出现,受台风的影响较大。2006年由于04号台风"碧利斯"的影响导致了本年的雨水集中期出现在7月中旬,其特征是降水强度强、强降水范围广、过程降雨量大,而大尺度引导气流、南海季风对"碧利斯"的西行路径及降水强度影响较大。     

Local and landscape effects on spatial patterns of mangrove forest during wetter and drier periods: Moreton Bay,Southeast Queensland,Australia

Land use/cover and mangrove spatial changes were assessed for ten sites and their sub-catchments in Southeast Queensland, Australia. Two time periods were involved: 1972–1990, a period of relatively high rainfall, and 1990–2004, which was significantly drier. Aerial photographs and Landsat satellite imagery were used to map the inter-tidal wetlands and classify the land use/cover in the sub-catchments. A Maximum Likelihood Classification was used to map three types of land cover: agriculture, built-up and plantation forest. Mangroves (mainly Avicennia marina) were the focus as they have been recorded over recent decades encroaching into salt marsh. The Mangrove-Salt marsh Interface (MSI) Index was developed to quantify the relative opportunity for mangroves to expand into salt marshes, based on the shared boundary between them. The index showed a consistent relationship with mangrove expansion and change. To address problems of high dimensionality and multi-collinearity of predictor variables, a Partial Least Squares Regression (PLSR) model was used. A key finding of this research was that the contribution of environmental variables to spatial changes in the mangroves was altered following a reduction in rainfall. For example, agriculture had more influence on mangrove expansion and change during the wet period than during the dry period.     

冲绳海槽北段的重磁场特征及地质意义

１９９２年之前,国内对冲绳海槽的调查研究主要集中在海槽的中部和南部,而对其北段的调查研究工作却很少。根据实测的重磁异常,较深入地分析了海槽北段的地球物理场特征,构造活动地壳结构及应力状态,结果表明冲绳海槽北段同样具有强烈的地壳构造活动。     

TRMM海表降水率同化在热带气旋Danas(2001) 数值模拟中的应用研究

研究了TRMM/TMI海表降水率资料的四维变分同化在热带气旋(TC)数值模拟中的作用.使用中尺度气象模式MM5设计了若干数值试验模拟了TC Danas(2001)由热带低压初生到台风生成的发展过程.在满足MM5模式动力约束的前提下,将TRMM海表降水率资料直接同化进入较高分辨率(18 km)的模式初始场.结果表明,使用MM5模式的4D-VAR同化系统直接同化TRMM/TMI海表降水率资料是可行的.这种做法提高了TRMM资料的利用率,不仅在模式初始场中加入了更多实测信息,而且避免了两次同化(1DVAR+4DVAR)可能引起的误差.直接同化TRMM资料通过调整气压、温度、湿度等要素初始场,改善了模式对热带气旋结构(如暖心、涡度、散度)的描述和降水的模拟.在此基础上,同化不仅改进了对Danas强度的模拟,而且成功地模拟了热带气旋环境场的演变过程,因而改进了路径的模拟.     

秋季巴伦支海海温异常对冬季我国渤海冰情的可能影响

本文利用美国NCEP/NCAR逐月的再分析资料、HadISST海温、中国160台站气温和反映渤海冰情轻重的渤海冰情等级资料,研究了前秋巴伦支海海温异常对后期渤海冰情和东亚冬季风的影响,并对相关的物理过程进行分析。结果表明,前秋巴伦支海关键区海温与该区域海冰密集度呈显著的负相关,且具有较好的持续性,通过调节随后冬季向大气释放的热通量,引起后期环流变化。偏高(偏低)年冬季亚洲纬向环流偏弱(偏强),东亚大槽加深(减弱),东亚冬季风加强(减弱),我国东北、华北及西北地区地区显著偏冷(偏暖),这与冬季渤海海冰异常的强度和范围都偏大(小)及与之相联系的环流异常相一致。进一步的分析揭示了联系上游关键区海温变化与后期东亚地区气候异常的重要途径,前秋巴伦支海海温偏高会导致200 hPa高度场形成一个自西向东的波列形式,在东亚局地Hadley环流异常的作用下,加强了我国北方地区地表的北风异常。因此,前秋巴伦支海海温异常可以作为冬季渤海冰情的预报因子。     

华北地区夏季降雨量与南海海温长期变化的关系

比较了华北地区7个站与17个站1951-1997年夏季（6，7，8月）降雨量与气候随时间的变化特征，并对其成因作了探讨。结果表明，用北京、天津、邢台、烟台、郑州、太原和济南等7个站可代表该地区夏季降雨量与气候的多尺度变化特征，过去47a该地区依次经历了湿凉、湿热、湿凉、干热、湿热几个时期，降雨量的长期变化与南海前冬（1-2月）海温成负相关。前冬南海海温偏高，意味着初夏南海地区大气对流低频振动偏弱，南海夏季风爆发较晚，西南季风较弱，夏季西太平洋副高位置偏南，华北地区大气低层北风加强，华北地区夏季少雨，前冬南海海温偏低时情况则相反，考虑冬季（1-2月）南海南温和7-8月西太平洋副高脊线位置（纬度）的影响用均生函数建模，试验结果与用子波变换重构方法考虑华北地区夏季降雨量的变化趋势比较，二者相吻合，预测试验结果与过去3a的实况基本一致。     

Conversion of kinetic energy from synoptic scale disturbance to low-frequency fluctuation over the Yangtze River valley in the summers of 1997 and 1999

In order to investigate the conversion of kinetic energy from a synoptic scale disturbance （SSD; period≤seven days） to a low-frequency fluctuation （LFF; period〉seven days）, the budget equation of the LFF kinetic energy is derived. The energy conversion is then calculated and analyzed for the summers of 1997 and 1999. The results show that the energy conversion from the SSD to the LFF is obviously enhanced in the middle and lower troposphere during the heavy rainfall, suggesting this to be one of mechanisms inducing the heavy rainfall, although the local LFF kinetic energy may not be enhanced.     

Impact of 4DVAR assimilation of rainfall data on the simulation of mesoscale precipitation systems in a mei-yu heavy rainfall event

The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulated through rainfall assimilation using the PSU/NCAR non-hydrostatic, mesoscale, numerical model （MM5） and its four-dimensional, variational, data assimilation （4DVAR） system. For this case, the improvement of the process via the 4DVAR rainfall assimilation into the simulation of mesoscale precipitation systems is investigated. With the rainfall assimilation, the convection is triggered at the right location and time, and the evolution and spatial distribution of the mesoscale convective systems （MCSs） are also more correctly simulated. Through the interactions between MCSs and the weather systems at different scales, including the low-level jet and mei-yu front, the simulation of the entire mei-yu weather system is significantly improved, both during the data assimilation window and the subsequent 12-h period. The results suggest that the rainfall assimilation first provides positive impact at the convective scale and the influences are then propagated upscale to the meso- and sub-synoptic scales.
Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance.