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.     

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.     

数字滤波弱约束四维变分同化在台风初始化中的应用(Ⅱ):数字滤波权重选择

数字滤波权重的选择是影响台风路径和强度预报的重要因素,某些个例中直接决定着台风数值模拟的成功与否,合适的数字滤波权重非常重要.本文利用2010年3个台风7个不同初始时刻个例,开展数字滤波弱约束四维变分同化(4D-Var)台风初始化试验,分析不同数字滤波权重对台风路径和强度预报的影响;基于上述台风路径和强度预报后验统计信息,以及台风中心区域800 km范围和整个模拟区域3 h平均地面气压倾向信息,提出一种bogus资料同化台风初始化中数字滤波权重优选方案,并进行理论解释说明;同时,指出bogus资料同化台风初始化中,常规数字滤波权重为1000更合理,明显有别于常规天气和暴雨天气权重设置.