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Near-cloud aerosol retrieval and three-dimensional radiative transfer using machine learning




Yang, Chen-Kuang, author
Chiu, Christine, advisor
Kummerow, Christian D., committee member
Miller, Steven D., committee member
Ebert-Uphoff, Imme, committee member

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According to the most recent report of the Intergovernmental Panel on Climate Change, aerosols remain one of the largest sources of uncertainty in estimating and interpreting the Earth's changing energy budget. To reduce the uncertainty, an advanced understanding of aerosol optical properties and aerosol-cloud interaction is needed, which has largely relied on (but is not limited to) passive satellite observations. Current aerosol retrieval methods require a separation between cloud-free and cloudy regions, but this separation is often ambiguous. Three-dimensional (3D) cloud radiative effects can extend beyond the physical boundaries and enhance the reflectance in adjacent cloud-free regions as far as 10 km from clouds. Aerosol optical properties cannot be accurately retrieved without considering the 3D cloud radiative effect in this so-called "twilight" or "transition" zone, which denotes the area between cloud-free and cloudy regions. Indeed, most contemporary retrievals discard these regions, making it impossible to estimate the aerosol radiative effects in this zone. To help break the deadlock, 3D cloud radiative effects must be incorporated into the retrieval methods, and two approaches are proposed in this work, both leveraging machine learning techniques. The first approach accounts for 3D cloud radiative effects by building a 3D shortwave radiative transfer emulator as the forward model for the retrieval methods. Our emulator was trained by cumulus scenes generated from large eddy simulations and radiation fields calculated from 3D radiative transfer, to predict downward and upward flux profiles at a 500 m horizontal resolution and 30 m vertical resolution. From a case drawn from the testing dataset, our emulator captures the spatial pattern of the surface downwelling flux (e.g., shadows and illuminations), and the associated PDF has a remarkable similarity to the synthetic truth. In addition, compared to 1D calculation, our 3D emulator improves the root-mean-square-error by a factor of 6. For the flux and heating rate profiles, our emulator is much superior to the 1D calculation for the cloudy column. The application of this 3D radiative transfer emulator to numerical weather modeling or large-eddy simulations type of model is beyond the scope of the current work to develop an aerosol retrieval algorithm, but the possibility exists to do so. While the promising results from the emulator make it possible to conduct 3D RT retrieval methods, this approach still faces ambiguity in separating cloud-free and cloudy pixels. Here, we present a new retrieval algorithm for aerosol optical depth (AOD) in the vicinity of clouds which contains two unique features. First, it does not require pre-separation of aerosols and clouds. Second, it incorporates 3D radiative effects, allowing us to provide accurate aerosol retrievals near clouds. The AOD retrieval uncertainty of this method in the cloud-free region is (0.0004 ± 4% AOD), which is much better than the (0.03 ± 5% AOD) retrieval uncertainty in NASA Aerosol Robotic Network (AERONET). This method shows skill of predicting AOD over the near-cloud regions, and its validity was confirmed by using one of the explainable artificial intelligence methods to demonstrate that the model's decisions are supported by radiative transfer theory. Finally, a case study using MODIS observations shed light on how this new method can be applied to real world observation, possibly leading to new scientific insight on aerosol structure and aerosol-cloud interaction.


2021 Fall.
Includes bibliographical references.

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machine learning
aerosol remote sensing
rhree-dimensional radiative transfer


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