Retrieving 3D microphysical properties of shallow cumulus clouds from A-Train observations using an advanced particle flow approach and a radiative transfer emulator
Abstract
Passive satellite observations play an important role in retrieving cloud optical depth and effective radius, which are fundamental cloud properties for understanding Earths radiation budget. In the past few decades, one-dimensional (1D) plane-parallel clouds have been assumed in most retrieval methods, even though it is well known that three-dimensional (3D) radiative effects are not negligible, particularly in cumulus cloud fields. The slow movement in relaxing such a strong 1D assumption from existing retrieval methods is due to a lack of 3D cloud field information, a lack of a rigorous framework to incorporate 3D radiative effects into retrieval, and to some extent, computational considerations. We will introduce a new method for retrieving 3D fields of cloud water content and effective radius, using A-Train cloud radar, lidar, and shortwave radiation measurements. Our method is built on a particle flow approach, iteratively moving ensemble members from the prior to the posterior probability density function, to provide robust retrieval uncertainty. The method also incorporates 3D effects by capitalizing on the recently developed radiative transfer emulator. We will highlight examples of cumulus clouds over the Southeast Atlantic Ocean, which have been difficult to retrieve but are important for studying cloud transitions and aerosol-cloud interactions.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.A54D..01C