各向异性背景场误差协方差构建及在“凡亚比”台风的应用

陈耀登; 陈晓梦; 闵锦忠; 邢建勇; Wang Hongli

doi:10.3969/j.issn.0253-4193.2016.09.004

各向异性背景场误差协方差构建及在“凡亚比”台风的应用

doi: 10.3969/j.issn.0253-4193.2016.09.004

1.
南京信息工程大学气象灾害教育部重点实验室气候与环境变化国际合作联合实验室气象灾害预报预警与评估协同创新中心, 江苏南京 210044
2.
福建省气象服务中心, 福建福州 350001
3.
国家海洋环境预报中心国家海洋局海洋灾害预报技术研究重点实验室, 北京 100081
4.
Global Systems Division, NOAA/Earth System Research Laboratory, Colorado, USA

基金项目: 国家重点基础研究发展计划（2013CB430102）；公益性行业（气象）科研专项（GYHY201506002）；国家自然科学基金项目（41675102）；中国气象局“气象资料质量控制及多源数据融合与再分析”项目。

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出版历程
- 收稿日期: 2015-10-16
- 修回日期: 2016-06-08

Anisotropic background error covariance modelling and its application in Typhoon Fanapi

1.
Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Fujian Meteorological Service Center, Fuzhou 350001, China
3.
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
4.
Global Systems Division, NOAA/Earth System Research Laboratory, Colorado, USA

摘要

摘要: 利用相临过去时段预报结果中同一时刻不同时效的模式预报场差异，计算预报误差协方差，并基于集合-变分混合同化系统将其与静态背景场误差协方差结合，从而在同化系统中构建了具有各向异性和一定流依赖特征的背景场误差协方差。单点观测理想试验显示本方案改善了静态模型化背景场误差协方差的各向同性和流依赖性问题。“凡亚比”台风的一系列同化及模拟试验表明，从台风路径、强度等方面本文方案的效果都要优于三维变分法。本文方案在不需要集合预报，计算量与三维变分法相当的情况下，给同化系统引入了各向异性、一定流依赖特征的背景误差协方差，因此本方案适于在计算资源较为紧缺情况下，对时效要求较高的预报业务中应用。
- 资料同化 /
- 混合同化 /
- 背景场误差协方差 /
- 各向异性 /
- 台风
Abstract: Based on ensemble-variational hybrid data assimilation system, the anisotropic and some flow-dependent background error covariance was introduced into data assimilation systems by combining historical forecast error covariance with the static background error covariance. The historical forecast error covariance was calculated from the forecasts of difference between the different forecasts respectively valid at the same time. Single observation experiments demonstrate that the background error covariance modeled by the new method has the anisotropic and some flow-dependent information. A series of assimilation and simulation experiments for typhoon Fanapi show that the track, minimum sea level pressure and wind speed using the method were better than that of 3DVar. The historical forecast error covariance not need ensemble forecasts and the anisotropic and some flow-dependent information are taken into account in the data assimilation system, then the cost of the calculation is similar to that of 3DVar, so the method would be beneficial to some operational centers and research communities with limited computational resources.
- data assimilation /
- hybrid assimilation /
- background error covariance /
- anisotropic /
- typhoon

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参考文献(14)

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