Abstract
Cloud top pressure (CTP) is one of the critical cloud properties that significantly affects the radiative effect of clouds. Multi-angle polarized sensors can employ polarized bands (490 nm) or O2 A-bands (763 and 765 nm) to retrieve the CTP. However, the CTP retrieved by the two methods shows inconsistent results in certain cases, and large uncertainties in low and thin cloud retrievals, which may lead to challenges in subsequent applications. This study proposes a synergistic algorithm that considers both O2 A-bands and polarized bands using a random forest (RF) model. LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP. Additionally, through analysis, we proposed that the polarized signal becomes saturated as the cloud optical thickness (COT) increases, necessitating a particular treatment for cases where COT < 10 to improve the algorithm’s stability. The synergistic method was then applied to the directional polarized camera (DPC) and Polarized and Directionality of the Earth’s Reflectance (POLDER) measurements for evaluation, and the resulting retrieval accuracy of the POLDER-based measurements (RMSEPOLDER = 205.176 hPa, RMSEDPC = 171.141 hPa, R2POLDER = 0.636, R2DPC = 0.663, respectively) were higher than that of the MODIS and POLDER Rayleigh pressure measurements. The synergistic algorithm also showed good performance with the application of DPC data. This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing, Atmospheric Radiation, and Updating Energy (CARE) platform.
摘 要
云顶压强是影响云辐射效应的关键云参数之一, 准确探测或反演云顶压强是开展数值天气预报与气候模式模拟的前提条件. 当前星载多角度偏振传感器可以利用490nm处的偏振特性或O2-A带(763和765nm)的吸收效应反演云顶压强. 但是, 由于不同反演方法在某些情况下存在的局限性, 会导致两种反演方法获得的云顶压强反演结果存在差异. 尤其在低薄云的反演中二者均存在较大不确定性, 可能会导致后续的应用中出现误报. 本研究提出了一个同时考虑O2-A带和多角度偏振信息的联合反演算法, 利用高精度激光雷达CALIOP数据作为真值, 结合多通道偏振和非偏振反射率信息构建随机森林模型. 并通过分析发现, 随着云光学厚度(COT)的增加, 偏振反射率趋于饱和的特性. 因此通过大量的模拟, 对光学薄云(COT<10)的偏振反射率立了拟合关系, 以提高算法的稳定性. 将协同方法应用于POLDER和DPC数据, 与CALIOP数据的对比中RMSEEPOLDER=205.176 hPa、 RMSEDPC=171.141 hPa、 R2POLDER=0.636、 R2DPC=0.663, RMSE和R2均高于MODIS和POLDER官方产品. 验证结果表明, 该算法在POLDER和DPC数据的应用均表现出了良好的性能. 该算法有望在遥感、 大气辐射和更新能源云应用(CARE)平台中作为云遥感算法, 为大气相关领域提供数据支持.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 42025504; No.41905023); National Natural Science Youth Science Foundation (Grant No. 41701406); Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant No.: 2021122).
The authors would like to thank the Anhui Institute of Optics and Fine Mechanics and the Satellite Application Center for Ecology and Environment for providing the DPC data, NASA and ICARE for freely providing the MODIS, and CALIOP and POLDER3 for data provided online.
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Article Highlights
• The synergistic use of measurements in polarized and O2 A-bands provides more information for CTP retrieval than a single retrieval method.
• Taking high-precision LiDAR data as the true value, a CTP retrieval algorithm was developed based on machine learning.
• The new algorithm can be applied to DPC and POLDER data, and the accuracy is improved compared with previous POLDER CTP products.
This paper is a contribution to the special issue on Cloud–Aerosol–Radiation–Precipitation Interaction: Progress and Challenges.
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Wei, L., Shang, H., Xu, J. et al. Cloud Top Pressure Retrieval Using Polarized and Oxygen A-band Measurements from GF5 and PARASOL Satellites. Adv. Atmos. Sci. 41, 680–700 (2024). https://doi.org/10.1007/s00376-023-2382-5
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DOI: https://doi.org/10.1007/s00376-023-2382-5