Effects of Flow Dependency Introduced by Background Error in Frequent and Dense Assimilation of Radial Winds Using Observation Error Correlated in Time and Space
Effects of Flow Dependency Introduced by Background Error in Frequent and Dense Assimilation of Radial Winds Using Observation Error Correlated in Time and Space
复制标题
利用时空相关观测误差对径向风频繁密集同化中背景误差引入的流相关性影响
DOI：
10.1175/mwr-d-21-0121.1
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发表时间：
2022
影响因子：
3.2
通讯作者：
Takuya Kawabata
中科院分区：
文献类型：
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作者：
Tadashi Fujita;Hiromu Seko;Takuya Kawabata
我们研究了高密度、高频率观测资料同化中气流依赖性的影响。本文利用一个区域混合四维变分资料同化（4D-Var）方案和一个与气流相关的背景误差协方差，同化了多普勒雷达的径向风。为了能够以10 min的间隔同化5 km × 5. 625 °的单元平均径向风，对观测误差的时空相关性进行了统计诊断，并将其纳入到混合四维变分中。空间相关宽度大于仪器误差的预期，表明表示误差的传播也被认为是导致时间相关性的贡献，其宽度被诊断为随预测时间增加。背景误差协方差在将观测信息纳入分析中也具有重要作用。单次观测试验表明，混合4D-Var的气流相关背景误差相关比受气候背景误差协方差限制的4D-Var具有更多的小尺度结构，主要集中在同化窗口的前半部分。这表明混合4D-Var的更高潜力允许增量中的更多更高波数分量。一个案例研究表明，混合4D-Var更好地利用了密集和频繁的观测，反映了更详细的表示流在整个同化窗口，导致有希望的结果在预测。灵敏度实验也表明，使用最优观测误差相关是重要的。它建议，流量相关的背景误差成为必要的，以有效地使用高分辨率，高频率的观测。
We investigated the effect of flow dependency in the assimilation of high-density, high-frequency observations. Radial winds from a Doppler radar are assimilated using a regional hybrid four-dimensional variational data assimilation (4D-Var) scheme with a flow-dependent background error covariance. To consistently assimilate 5 km × 5.625° cell-averaged radial winds at an interval of 10 min, the spatial and temporal correlations of the observation error are statistically diagnosed to be incorporated into the hybrid 4D-Var. The spatial correlation width is larger than that expected from instrument error, suggesting a contribution from representation error whose propagation is also considered to lead to temporal correlation, the width of which is diagnosed to increase with forecast time. The background error covariance also has an important role in incorporating observational information into the analysis. Single observation experiments show that the hybrid 4D-Var has more small-scale structure in its flow-dependent background error correlation than the 4D-Var limited from the climatological background error covariance mainly in the former part of the assimilation window. This suggests the higher potential of the hybrid 4D-Var to allow more higher-wavenumber components in the increment. A case study shows that the hybrid 4D-Var makes better use of the dense and frequent observations, reflecting more detailed representation of flow throughout the assimilation window, leading to promising results in the forecast. Sensitivity experiments also show that it is important to use the optimal observation error correlation. It is suggested that the flow-dependent background error becomes necessary to effectively use high-resolution, high-frequency observations.