Abstract
This study explores the structures of the correlations between infrared (IR) brightness temperatures (BTs) from the three water vapor channels of the Advanced Baseline Imager (ABI) onboard the GOES-16 satellite and the atmospheric state. Ensemble-based data assimilation techniques such as the ensemble Kalman filter (EnKF) rely on correlations to propagate innovations of BTs to increments of model state variables. Because the three water vapor channels are sensitive to moisture in different layers of the troposphere, the heights of the strongest correlations between these channels and moisture in clear-sky regions are closely related to the peaks of their respective weighting functions. In cloudy regions, the strongest correlations appear at the cloud tops of deep clouds, and ice hydrometeors generally have stronger correlations with BT than liquid hydrometeors. The magnitudes of the correlations decrease from the peak value in a column with both vertical and horizontal distance. Just how the correlations decrease depend on both the cloud scenes and the cloud structures, as well as the model variables. Horizontal correlations between BTs and moisture, as well as hydrometeors, in fully cloudy regions decrease to almost 0 at about 30 km. The horizontal correlations with atmospheric state variables in clear-sky regions are broader, maintaining non-zero values out to ∼100 km. The results in this study provide information on the proper choice of cut-off radii in horizontal and vertical localization schemes for the assimilation of BTs. They also provide insights on the most efficient and effective use of the different water vapor channels.
摘要
本文研究了GOES-16卫星搭载的先进基线成像仪(ABI)的三个水汽通道的红外亮温与大气状态量的相关性。集合资料同化方法,如集合卡尔曼滤波(EnKF),使用相关性和协方差将红外亮温的观测增量转换为模式变量的更新向量。因为这三个水汽通道对对流层不同高度的水汽敏感度不同,因此,在晴空区域下,这三个水汽通道和水汽的相关性最强的高度和各个通道各自的权重函数密切相关。在云区,最强的相关性通常出现在深对流云的云顶,并且冰粒子和亮温的相关性强于水粒子。随着垂直和水平距离的增加,相关性也逐渐减弱。相关性减弱的幅度和距离取决于是否有云出现和云的结构,亮温和不同的大气状态量的相关性减弱的特征也不同。在云区,亮温和水汽以及水成物粒子的相关性在大约30 km距离处减弱为0。在晴空区域,相关性的水平尺度更大,在100 km距离处依旧有着一定的相关性。本文的结果对同化红外亮温时协方差矩阵的垂直和水平局地化,有重要参考价值,也为更有效地同化不同的红外水汽通道提供了重要信息。
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Acknowledgements
This article is dedicated to Dr. Fuqing ZHANG, who was a talented mentor, a wonderful colleague, and a very good friend. The authors would like to thank Dr. Yinghui LU of Nanjing University for his help on the CRTM model, and a reviewer who helped us to improve the clarity of this paper. This work is supported by the NASA under awards NNX15AQ51G and 80NSSC19K0728, the ONR under award N000141812517, and the NOAA Office of Weather and Air Quality under award NA18OAR4590369. The numerical experiments were performed on the Stampede 2 supercomputer of the Texas Advanced Computing Center (TACC) through the Extreme Science and Engineering Discovery Environment (XSEDE) program of the National Science Foundation (NSF).
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Article Highlights
• Different infrared channels are correlated with different layers of the atmosphere
• Cloud-affected brightness temperatures have different horizontal and vertical correlation structures compared with clear-sky ones
• The horizontal and vertical structures of the correlations imply localizations that facilitate the assimilation of multiple channels
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Zhang, Y., Clothiaux, E.E. & Stensrud, D.J. Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales. Adv. Atmos. Sci. 39, 714–732 (2022). https://doi.org/10.1007/s00376-021-0352-3
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DOI: https://doi.org/10.1007/s00376-021-0352-3