集合均方根滤波同化地面自动站资料的技术研究

邵长亮; 闵锦忠

doi:10.3878/j.issn.1006-9895.1406.13263

集合均方根滤波同化地面自动站资料的技术研究

doi: 10.3878/j.issn.1006-9895.1406.13263

邵长亮¹,
闵锦忠^2,

1.
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京210044;中国气象局气象探测中心, 北京100081;气象灾害教育部重点实验室, 南京210044
2.
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京210044;气象灾害教育部重点实验室, 南京210044

基金项目: 国家重点基础研究发展计划(973计划)2013CB430102,江苏省普通高校研究生科研创新计划项目KYLX_0824

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出版历程
- 收稿日期: 2013-09-16
- 修回日期: 2014-07-08

A Study of the Assimilation of Surface Automatic Weather Station Data Using the Ensemble Square Root Filter

SHAO Changliang¹,
MIN Jinzhong^2
,

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;CMA Meteorological Observation Centre, Beijing 100081;Key Laboratory of Meteorological Disaster of Ministry of education, Nanjing 210044
2.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;Key Laboratory of Meteorological Disaster of Ministry of education, Nanjing 210044

摘要

摘要: 模式地形与观测站地形高度差异一直是地面资料同化面临的棘手问题,合理的同化方案能够将地面自动站资料有效的同化到中尺度数值模式中。本文首先采用Guo et al.(2002)的方案实现了在WRF模式中应用集合Kalman滤波方法同化地面自动站资料;然后对方案进行调整,对10 m高度风场、2 m高度位温、2 m高度露点和地表气压进行同化。通过均方根误差分析,模拟结果和同化增量分析来确定集合平方根滤波(EnSRF)同化地面自动站资料的有效性,并进行敏感性试验分析检验模式对各要素物理量的响应状况。结果表明:在EnSRF同化系统中应用Guo et al.(2002)的方案将地面自动站资料进行同化到数值模式中,能够部分改善模拟结果;地面观测资料(温度、湿度、风场、地表气压)中各物理量分别同化到数值模式都能影响18小时降水预报,但各物理量所起作用大小不同,其中对结果影响最大的是露点;使用位温、露点分别代替温度、比湿进行同化模拟效果更好,对自动站资料的同化也更加有效。
- 资料同化 /
- 集合Kalman滤波 /
- 自动站资料 /
- 敏感性试验
Abstract: Handling the difference in elevation between a model surface and an observation site is always a challenge in surface data assimilation. However, a reasonable assimilation scheme can efficiently assimilate surface automatic weather station (AWS) data into a mesoscale model. In this paper, surface AWS data are first assimilated into a weather research and forecasting (WRF) model through an ensemble Kalman filter using the Guo et al. (2002) scheme. Then an adjusted scheme is proposed that assimilates 10-m wind observations, 2-m potential temperature, 2-m dew point temperature, and surface pressure. This scheme is then validated by mean square root error analysis, simulated result and assimilation increment analysis, and sensitive experiments to check the assimilation response of each AWS meteorological parameter. Results show that the assimilation of surface AWS data through the ensemble square root filter (EnSRF) using the Guo et al. (2002) scheme can improve the simulation results. The separate assimilation of any element of the surface observation data (temperature, humidity, wind, surface pressure) can affect the forecast of 18 h accumulated rainfall. However, different elements have different impacts, and the one having most influence is the dew point temperature. The use of 2-m potential temperature and 2-m dew point temperature, instead of 2-m temperature and 2-m specific humidity, leads to better simulation results.
- Data Assimilation /
- Ensemble Kalman filter /
- AWS (automatic weather station) data /
- Sensitive experiments

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