Retrieval of Vertical Physical Properties of Water Clouds using Multisource Satellite Observations
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更新:2026-08-31 18:26:20 浏览:0次
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摘要
Vertical profiles of cloud microphysical properties, particularly cloud effective radius (CER) and liquid water content (LWC), are essential for understanding cloud evolution, radiative effects, and climate model parameterizations. Active radar provides accurate vertical information but has limited coverage, whereas passive satellites offer broad coverage without direct profiling capability. To bridge this gap, this study develops a physically based framework for retrieving liquid-phase stratiform cloud profiles by integrating LES simulations, CloudSat observations, and POLDER/Parasol multi-angle polarimetric measurements. EOF analysis shows that the first three modes explain over 90% of LES-derived profile variability, dominated by monotonic and triangular structures. Accordingly, a simplified cloud profile parameterization model (CPM) with eight physical parameters is proposed, and a cloud profile reconstruction model (CPRM) is validated using CloudSat and aircraft in-situ data. Radiative-transfer experiments indicate that optical thickness, cloud-top CER, and geometric thickness can be reliably constrained from passive observations, while turning-point parameters are more difficult to retrieve. Global CloudSat statistics show that increase-then-decrease and monotonically decreasing profiles account for 90.1% of cases, with TP_CER and TP_NCOT as key descriptors of triangular structures. Combining multiple linear regression and random forest models enables the first retrieval of TP_CER and TP_NCOT from POLDER-3 observations, with good consistency against collocated CloudSat measurements. The framework is further extended to Himawari-8 multi-channel observations for high-spatiotemporal-resolution retrieval of cloud-top structural parameters. Overall, these studies establish a pathway from active-radar-derived profile knowledge to passive-satellite retrieval of cloud vertical structure, supporting global cloud profile characterization and improved understanding of aerosol–cloud–precipitation interactions and cloud radiative effects.
稿件作者
Huazhe Shang
Aerospace Research Information Institute, Chinese Academy of Sciences
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