Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast
Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast
复制标题
POD-4DEnVar 方法卫星和雷达组合资料同化对降雨预报的评估
DOI：
10.3390/app10165493
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发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Mingyang Zhang
中科院分区：
文献类型：
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作者：
Jingnan Wang;Lifeng Zhang;Jiping Guan;Mingyang Zhang
卫星和雷达观测代表了两种根本不同的遥感观测类型，为数值天气预报（NWP）提供独立的信息。由于先前已经研究了对改善预报的个人影响，因此将这两种数据资源结合起来可能会提高天气预报的性能。采用基于正交分解（POD）的集合四维变分（4DVAR）同化方法（简称POD-4DEnVar），对卫星辐射、雷达径向速度和反射率进行了同步同化。对2016年和2017年6 - 7月发生的连续强降雨过程进行了影响评价。结果表明，POD-4DEnVar同化卫星和雷达资料的组合具有改善天气预报的潜力。对22次预报的平均结果表明，尽管预报结果对不同的变量敏感，但一般来说同化对不同气压水平的预报效果都有改善。当进行同化时，降水技能分数通常会增加。一个案例研究也检查，以找出预测改善的贡献。POD-4DEnVar同化的累积降水量演变对降水强度和降水分布有较好的预报效果。这些改善是由于当地的湿度，温度和风场的变化。另外，雷达资料同化直接改变了初始的雨水和云水条件。两个试验都能模拟出强降水区的强降水，但同化过程加快了旋转，加强了暴雨的初始强度。一般来说，卫星和雷达资料的联合同化比没有资料同化的降水预报效果更好。
Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.
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DOI：
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发表时间：
2006
期刊：
--
影响因子：
--
作者：
F. Weng;Yong Han;P. V. Delst;Q. Liu;T. Kleespies;B. Yan;J. Marshall
通讯作者：
F. Weng;Yong Han;P. V. Delst;Q. Liu;T. Kleespies;B. Yan;J. Marshall
影响因子：
3.1
作者：
Jidong Gao;D. Stensrud
通讯作者：
Jidong Gao;D. Stensrud
影响因子：
3.8
作者：
Daqing Yang;B. Ye;A. Shiklomanov
通讯作者：
Daqing Yang;B. Ye;A. Shiklomanov
DOI：
10.1111/j.1600-0870.2011.00529.x
发表时间：
2011-01
期刊：
Tellus - Series A: Dynamic Meteorology and Oceanography
影响因子：
--
作者：
Tian Xiangjun;Xie Zhenghui;Sun Qin
通讯作者：
Sun Qin
影响因子：
3.2
作者：
Yunji Zhang;D. Stensrud;Fuqing Zhang
通讯作者：
Yunji Zhang;D. Stensrud;Fuqing Zhang