Abstract
A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms. Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations, which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction (XCO2). Based on the radiance measurements in the 0.76 μm O2A band, 1.61 μm (weak), and 2.06 μm (strong) CO2 bands, the current combination of the A-Band Preprocessor (ABP) algorithm and Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) algorithm passes around 20%–25% of all soundings, which means that some contaminated scenes also pass the screening process. In this work, three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask. The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016. The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask, especially in summer and fall. In addition, analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network (TCCON) sites.
摘 要
本文优化了二氧化碳测量卫星OCO-2现在使用的ABP以及IDP云层筛选算法, 通过调整算法中的阈值组, 提升卫星反演XCO2数据质量. 这两种云层筛选算法是为了满足对OCO-2/GOSAT卫星数据进行快速、 准确的有云或无云判定的需求而提出的. 其基本原理为, 云与气溶胶的散射作用使得卫星测量得到的光谱与晴空条件下辐射传输模型模拟的光谱数据存在明显差异. 现有的阈值组较为宽松, 使得数据通过率较MODIS全球云覆盖率偏高, 说明一部分有云场景的数据会影响反演的精确度. 因此, 实验在欧洲、日本两个区域, 对2016年的OCO-2、 MODIS以及TCCON数据进行分析, 以MODIS云层数据为云场景判定真值, 通过逐步调整阈值组的方式, 使OCO-2卫星数据总体通过率接近当地MODIS云层覆盖率的月均值, 并据此分析最优阈值组的季度分布特征. 数据表明, 更严格的阈值选择提升了筛选结果与MODIS云层数据的匹配度, 降低了与地基站点TCCON的平均差值与标准差 (3.23±2.25 ppm 到 2.11±1.76 ppm). 该研究结果说明, 使用随区域情况变化的阈值组可以提升OCO-2云层筛选算法对云场景判定的能力, 降低卫星数据与TCCON地基数据之间的偏差值.
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Acknowledgements
This work was supported by the National Key Research Program of China (Grant No. 2016YFC0200900), the National Natural Science Foundation of China (NSFC) (Grant No. 41775023), the Excellent Young Scientists Program of the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR19D050001), the Fundamental Research Funds for the Central Universities, and the State Key Laboratory of Modern Optical Instrumentation Innovation Program. The authors declare no conflicts of interest regarding the publication of this paper.
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Article Highlights:
• A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for OCO-2 cloud screening algorithms.
• The optimized scheme reduces the difference between TCCON XCO retrievals and OCO-2 measurements from 3.23 ± 2.25 ppm to 2.11 ± 1.76 ppm.
• Adjustment is applied according to average monthly clear-sky fractions, which helps incorporate seasonal variation in Europe and Japan.
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Chen, S., Wang, S., Su, L. et al. Optimization of the OCO-2 Cloud Screening Algorithm and Evaluation against MODIS and TCCON Measurements over Land Surfaces in Europe and Japan. Adv. Atmos. Sci. 37, 387–398 (2020). https://doi.org/10.1007/s00376-020-9160-4
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DOI: https://doi.org/10.1007/s00376-020-9160-4