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Toward Hyperspectral Remote Sensing of Cirrus Clouds and Aerosol Mineral Dust
Abstract
Retrievals utilized in ice cloud and mineral dust aerosol remote sensing contain uncertainties from both forward models and measurements. These uncertainties are present in the retrieval products and propagate to atmospheric and climate models that ingest them. Hyperspectral instruments measure radiances in many narrow and overlapping channels producing highly information dense spectra. Hyperspectral instruments offer one tool for reducing uncertainties in retrieval products. This dissertation lays the foundation for future reflective solar hyperspectral retrievals of cirrus cloud and mineral dust properties. Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-PF) measured radiances are modeled.
Measurement information content (MIC) analysis is used to identify optimal channel subsets for cirrus cloud retrievals of cloud optical depth, effective diameter (Deff), and cloud top pressure (Ptop) over ocean water. Results using 252 simulated cloud/atmosphere cases and clustering analysis in (wavelength, MIC) data space show high density clusters in the water vapor channels centered at 0.978 micrometers, 1.115 micrometers, 1.78 micrometers, 1.86 micrometers, and 1.914 micrometers have high MIC for τcld. A cluster centered at 1.86 shows high MIC for P. Ice absorption channel clusters centered at 1.915 micrometers and approximately 2.00 micrometers contain high MIC for Deff. Cluster centroids are suggested for research retrievals that utilize rigorous radiative transfer models such as the Line-by-Line Radiative Transfer Model plus Discrete Ordinates Radiative Transfer model (LBLRTM+DISORT). Given the tradeoff between accuracy and speed for retrievals, additional channels within the clusters can be used contingent on requirements.
Hyperspectral remote sensing also offers an opportunity to unmix complex spectral signals reflected off aerosol layers to retrieve mineral dust composition, an uncertainty source in climate modeling. Sensitivity studies are conducted to ascertain which CLARREO-PF channels have the greatest sensitivity to target retrieval variables and how the partitioning of iron oxide between hematite and goethite influences the sensitivities. The spectral scope of this project is limited to 460 – 700 nm by the scarcity of published goethite spectral refractive indices. Optical depth sensitivity is found to be the strongest of the retrieval target variables. The partitioning between hematite and goethite has an effect on optical depth, effective diameter, and aerosol layer height sensitivities.
Citation
Mast, Jeffrey Clark (2023). Toward Hyperspectral Remote Sensing of Cirrus Clouds and Aerosol Mineral Dust. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /199989.