Including the horizontal observation error correlation in the ensemble Kalman filter: idealized experiments with NICAM-LETKF

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES Monthly Weather Review Pub Date : 2023-11-14 DOI:10.1175/mwr-d-23-0053.1
Koji Terasaki, Takemasa Miyoshi
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Abstract

Abstract Densely-observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSE) using the non-hydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires six times more computations than the case with the diagonal observation error covariance matrix.
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集成卡尔曼滤波中包含水平观测误差相关性:NICAM-LETKF的理想实验
雷达和卫星等密集观测遥感数据通常包含显著的空间误差相关性。在数据同化中,通常假设观测误差协方差矩阵为对角线,对密集数据进行稀疏化或空间平均,以弥补忽略观测误差空间相关性的不足。但从理论上讲,在数据同化中加入空间观测误差相关可以更好地利用密集数据。利用非流体静力二十面体大气模式(NICAM)和局部集合变换卡尔曼滤波(LETKF)进行了完美模式观测系统模拟实验(OSSE),以评估同化水平密集和误差相关观测的影响。观测误差协方差矩阵的条件数(定义为最大与最小特征值之比)对LETKF计算的数值稳定性至关重要。大的条件数给正确计算集合变换矩阵带来了困难。通过修正来减少条件数对稳定计算是有效的。结果表明,纳入水平观测误差协方差矩阵可提高分析精度,但计算量是采用对角线观测误差协方差矩阵的6倍。
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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