| 变分伴随数据同化在海表面温度预报中的应用研究 |
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| 引用本文： | 马继瑞,韩桂军,李冬.变分伴随数据同化在海表面温度预报中的应用研究[J].海洋学报,2002,24(5):1-7. |
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| 作者姓名： | 马继瑞 韩桂军 李冬 |
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| 作者单位： | 1.国家海洋信息中心, 天津, 300171 |
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| 基金项目： | 国家自然科学基金项目 (49876 0 0 1，4 0 0 0 6 0 0 1)，国家海洋局青年基金 (9930 6 ) |
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| 摘 要： | 将变分伴随数据同化技术应用于海表面温度(SST)数值预报.采用中国近海海表面温度短期数值预报模式,将船舶测报海表面温度同化到该模型中,对SST初始场进行优化.文中给出了中国近海SST数值预报同化模型5d试报结果与观测值的比较,整个区域的均绝差由同化前的2.71℃降至0.87℃,即变分伴随数据同化对改进SST数值预报的效果是比较明显的,表明它可成为SST数值预报初始化的新方法.
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| 关 键 词： | 变分伴随同化 SST模型 数值预报 中国近海 |
| 文章编号： | 0253-4193(2002)05-0001-07 |
| 收稿时间： | 2001/6/28 0:00:00 |
| 修稿时间： | 2001年6月28日 |
A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature |
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| MA Ji-rui,HAN Gui-jun and LI Dong.A study on the application of variational adjoint data assimilation for numerical prediction of sea surface temperature[J].Acta Oceanologica Sinica (in Chinese),2002,24(5):1-7. |
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| Authors: | MA Ji-rui HAN Gui-jun and LI Dong |
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| Institution: | 1.National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, China2.National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, China;Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China |
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| Abstract: | The variational adjoint method is used for assimilating the observed data into the sea surface temperature (SST) numerical models.The frame of SST model adopted here is based on "the short-term forecasting model of offshore SST in China seas".The experiment of SST hindcasting (5 d) is conducted when the initial field of SST is estimated by assimilating the SST ship report data into the model.The result with the absolute mean error decreased from 2.71 to 0.87 in the whole sea area shows that the variational adjoint calculations have successfully improved the accuracy of SST hindcasting.This experiment will guide a novel way towards the initialization for operational forecasting of SST. |
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| Keywords: | variational adjoint data assimilation SST model numerical prediction China seas |
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