融合法及其在数据同化中的应用研究
Blending method and its application in data assimilation
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摘要: 根据预报值具有最小方差这一要求,详细推导了融合法在观测数据为一维、多维和维数不同的情况下的具体同化表达形式,同时还给出了不同情况下与同化表达式相对应的预报误差公式.利用这些公式,可以用融合法处理常见的海洋观测数据的同化问题.在陆架海模式HAMSOM基础上,以4月份的渤海海表温度为例,我们验证了同化公式的正确性,并给出了同化后较好的同化结果。最后将融合法的同化结果与卡尔曼滤波同化结果进行了对比.比较表明,融合法使用起来更简单,且能有效地处理常见的海洋观测数据.Abstract: The different scheme of blending method to meet the requirement of the smallest forecast covariance is developed according to the different dimension of observed data.The formula of computing the forecast covariance is obtained too.Employing the scheme, the observed data of different types, are assimilated in real and then verified with the SST data in April in the Bohai Sea.The result of twin experiments indicates that the new scheme can give attention to both the observations and the model simulations, and that the data after assimilation are close to the observations.The result obtained by blending method is compared with that by Kalman filter, and which shows that the blending method is simpler than the Kalman filter in practical use and is more efficient than Kalman filter.
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Key words:
- blending method /
- data assimilation /
- Bohai Sea SST
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