Poster + Paper
20 November 2024 Towards an optimal estimation retrieval of cirrus cloud optical and microphysical properties using hyperspectral shortwave instruments and a fast radiative transfer algorithm
Jeffrey C. Mast, Yolanda Shea, Xu Liu
Author Affiliations +
Conference Poster
Abstract
Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral shortwave instruments produce high-resolution and information-dense spectra, thus offering the opportunity to reduce uncertainties in retrieval products. We are in the process of developing a retrieval that uses the very fast Principal Component Radiative Transfer Model in the solar spectral region (PCRTM-Solar) in the forward model calculations. This retrieval will use measured reflectances from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder) instruments. In this manuscript we present progress towards a reference retrieval employing a widely used, verified, accurate, yet computationally slower radiative transfer modeling technique. The reference retrieval, while too slow for using the complete hyperspectral measurement, will allow us to study the behavior of retrieval products and help us verify results from our in-development fast retrieval. Both retrievals will use the optimal estimation retrieval framework. In this manuscript we present results from an uncertainty analysis considering three uncertainty sources for a cirrus cloud retrieval in the form of error covariance matrices: reflectance uncertainty due to water vapor, the reflectance uncertainty due to ice crystal scattering assumptions, and the instrument measurement uncertainty. Results show that the uncertainty due to habit selection is the largest, while that due to water vapor is at most 0.6% relative to channel reflectance. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jeffrey C. Mast, Yolanda Shea, and Xu Liu "Towards an optimal estimation retrieval of cirrus cloud optical and microphysical properties using hyperspectral shortwave instruments and a fast radiative transfer algorithm", Proc. SPIE 13193, Remote Sensing of Clouds and the Atmosphere XXIX, 131930M (20 November 2024); https://doi.org/10.1117/12.3030955
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KEYWORDS
Clouds

Ice

Reflectivity

Radiative transfer

Hyperspectral sensing

Inverse problems

Passive remote sensing

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