基于集合卡尔曼变换的目标观测敏感区识别系统优化及影响试验

在基于集合卡尔曼变换(Ensemble Transform Kalman Filter,ETKF)方法的适应性观测系统的基础上,考虑湿度因子作用并增加对流层低层的大气运动信息,发展了更加适用于我国中尺度高影响天气系统敏感区识别的优化方案。针对环北京夏季暴雨和冬季降雪的高影响天气个例,分别设计4组试验进行观测敏感区识别试验,考察了优化方案目标观测敏感区识别质量,并对分析和预报结果进行了评估。结果表明:优化方案的目标观测敏感区识别效果最佳,对环北京夏季暴雨和冬季降雪天气的目标观测敏感区质量有明显改善,湿度因子可使最强观测敏感区更加集中,对夏季降水敏感区的影响比冬季降雪天气更加明显。低层大气信息的引入对最强观测敏感区的准确识别也具有重要的积极作用。目标观测敏感区的目标资料对分析和短期预报质量具有明显的正贡献。

Optimization and influence experiment to identify sensitive areas for target observations on ETKF method

The adaptive observation system based on ETKF(Ensemble Transform Kalman Filter) has been applied to identify sensitive observation area in Jianghuai heavy rain, typhoon, freezing rain disaster and etc.This paper considers the function of humidity factor, increases the information of atmosphere in low troposphere, and develops an optimization scheme to make the system more suitable for high impact weather in China.Selecting summer heavy rain and winter snow around Beijing as high impact weather examples, four groups of test schemes are designed for identifying observation sensitive area.This paper investigates the quality of observation sensitive area and evaluates the results of analysis and forecast.Results show that the optimized adaptive observation system is the best scheme, which has significantly improved the quality of observation sensitive region.The humidity factor can make the strongest observation sensitive area more concentrative, which is more obvious in the heavy rain than in the snow.The information of low-level atmosphere is helpful in identification of the strongest observation sensitive area.Target observations in the sensitive area have positive contribution for the quality of analysis and short-term forecast.