数字滤波弱约束四维变分同化在台风初始化中的应用Ⅰ.个例试验

传统四维变分(4D-Var)同化台风初始化中,bogus资料的同化将不可避免引起初始时刻风场和质量场之间的非平衡,在预报时段产生高频振荡噪音,进而影响数值模拟效果.如何有效滤除bogus资料引入产生的高频噪音非常重要.本文为第Ⅰ部分,基于数字滤波弱约束4D-Var方法,开展台风初始化个例试验研究.2010年Chaba台风两个不同时刻数值试验结果表明:bogus资料4D-Var同化台风初始化中,呈现出与暴雨个例明显不同的3 h地面气压倾向特征,不同数字滤波权重试验在初始时刻差异小,在预报1—4 h差异明显;实施数字滤波弱约束使得初始时刻700 hPa垂直速度场和850 hPa散度场在台风区域减小,bogus资料为引起高频噪音的一个重要因素;数字滤波权重选择是影响台风同化和数值模拟效果的重要因素,合理的数字滤波权重选择试验可明显改善台风路径和强度预报,相对于台风强度预报,路径预报改善更加明显;同时,某些个例中,数字滤波权重的选择直接决定着台风数值模拟的成功与否.不同台风个例相对最佳数字滤波权重并不相同,如何选择数字滤波权重非常重要.第Ⅱ部分中将针对bogus资料同化台风初始化,提出一种数字滤波权重优选方案.     

多参数背景场误差模型在散射计资料台风风场反演中的应用

利用散射计资料反演海面风场时, 台风区域普遍存在降雨使得风场反演误差很大, 引入降雨地球物理模型函数 (GMF+Rain) 及多解方案 (MSS), 结合二维变分(2DVAR) 模糊去除思想风速反演误差很大程度减小, 但风向反演误差仍有待进一步改善, 如何进一步减小风向反演误差有待进一步研究. 文章介绍了2DVAR模糊去 除方法的基本思想, 针对背景场误差较大时, 2DVAR模糊去除风向误差较大, 引入包含若干参数的背景场误差模型. 基于台风个例数值试验结果, 着重从理论分析角度讨论各参数关于2DVAR模糊去除效果的敏感性, 进而提出最优参数设置方案以改善风向模糊去除效果. 2006年“摩羯”台风QuikSCAT数据风场反演数值试验结果结合理论分析表明: 引入多参数误差模型, 通过设置粗糙误差概率等于0, 2DVAR风向模糊去除效果明显改善; 同时, 背景场的影响可通过增大背景场误差方差, 减小背景场误差相关尺度和减小粗糙误差概率而减小, 进而减小在背景场误差较大情况下的风向反演误差. 关键词： 台风风场反演 二维变分 多参数误差模型 散射计资料     

模式误差弱约束四维变分同化研究

经典四维变分同化(four dimensional variational data assimilation,简称4dvar)中认为模式是完美的, 而实际数值模式中存在的模式误差将对变分同化效果产生直接影响, 弱约束4dvar的提出为4dvar中考虑模式误差的影响提供了一种可行途径. 研究了基于模式误差控制变量的弱约束4dvar基本理论, 并在一维浅水方程组中对其同化效果及估计模式误差的有效性进行相关数值实验研究. 结果表明,在模式误差不可忽略的情况下,模式误差弱约束4dvar在整个区间 (包括同化区间和预报区间)上预报误差均比传统4dvar小, 在模式误差相对较大时改进效果更明显,具有明显优越性; 并且,模式误差弱约束4dvar能有效估计模式误差大小及分布形式.     

变分同化结合广义变分最佳分析对微波散射计资料进行海面风场反演

针对微波散射计资料的海面风场反演,本文首先利用QuikSCAT卫星散射计的近实时资料,借助荷兰皇家气象学会的SeaWinds散射计数据处理模式,在不同风速和海域的情况下,采用多解方案结合二维变分同化方法进行风向的模糊去除,达到消除解的不唯一性的目的,且对4种反演结果（低速风、中速风、高速风和近海岸风）与美国国家海洋和大气管理局卫星资料中心的近实时风场数据的对比分析,说明了该技术的有效性.在此基础上,对反演得到的中速风场利用广义变分最佳分析方法进行了调整,调整后的风场结构有了进一步的改善.此方法可为我国散射计资料反演海面风场的研究提供新的思路和方法.     

Application of Tikhonov regularization method to wind retrieval from scatterometer data Ⅱ: cyclone wind retrieval with consideration of rain

According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function (called GMF+Rain). The GMF+Rain model which is based on the NASA scatterometer-2 (NSCAT2) GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme (MSS), the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF+Rain model, but the wind direction obtained with the two-dimensional variational (2DVAR) ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR.     

变分同化结合广义变分最佳分析对微波散射计资料进行海面风场反演

针对微波散射计资料的海面风场反演,本文首先利用QuikSCAT卫星散射计的近实时资料,借助荷兰皇家气象学会的SeaWinds散射计数据处理模式,在不同风速和海域的情况下,采用多解方案结合二维变分同化方法进行风向的模糊去除,达到消除解的不唯一性的目的,且对4种反演结果（低速风、中速风、高速风和近海岸风）与美国国家海洋和大气管理局卫星资料中心的近实时风场数据的对比分析,说明了该技术的有效性.在此基础上,对反演得到的中速风场利用广义变分最佳分析方法进行了调整,调整后的风场结构有了进一步的改善.此方法可为我国散射计资料反演海面风场的研究提供新的思路和方法. 关键词： 多解方案 二维变分同化 广义变分最佳分析 正则化方法     

考虑替代模型不确定性的结构动力特性全局敏感性分析

结构参数的不确定性必然导致其动力特性具有不确定性,全局敏感性分析是定量各不确定参数动力特性影响大小的有效手段.但是全局敏感性分析具有计算花费高的问题,为此采用快速Gauss(高斯)过程模型来降低计算成本.此外,该文采用的是全局Gauss过程模型而不是它的均值函数来进行全局敏感性分析,考虑了替代模型不确定性,给出了敏感性指标的分布.该方法的可靠性通过具有解析敏感性指标值的测试函数得到验证.最后,将该方法用于安庆铁路长江大桥动力特性的敏感性分析.     

基于降雨率的GMF+RAIN模型构建及在台风风场反演中的应用

首先选取2006年QuikSCAT散射计扫描的3次台风个例("珊珊"、"摩羯"及"象神"),分析其获取的台风区域雷达后向散射截面值(σ^o)在降雨影响下的分布特征.然后依据大气辐射传输理论,考虑降雨对雷达后向散射截面值的影响,引进两个不同形式的降雨辐射传输模型(简称为SY和AMSR模型),并将这两个降雨辐射传输模型分别与NSCAT2模型函数相结合,构建了适合降雨情况的地球物理模型函数(简称为GMF+RAIN).最后,在GMF+RAIN模型的基础上,采用二维变分结合多解方案     

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

The present work reports the development of nonlinear time series prediction method of genetic algorithm(GA) with singular spectrum analysis(SSA) for forecasting the surface wind of a point station in the South China Sea(SCS) with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days(longer for some point station) in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.     

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I. Sensitivity analysis and simulation experiments

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section (NRCS) or the method of wind retrieval? It is valuable to research. First, depending on SDP2.0, the simulated `true' NRCS is calculated from the simulated 'true' wind through the geophysical model function NSCAT2. The simulated background field is configured by adding a noise to the simulated 'true' wind with the non-divergence constraint. Also, the simulated 'measured' NRCS is formed by adding a noise to the simulated 'true' NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational (2DVAR) ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data.