This paper presents advancements in the -IASI/f2n radiative transfer model, developed under two Italian Space Agency projects to support Italy's contribution to ESA's Earth Explorer 9 mission. The model simulates spectrally resolved radiance and its analytical derivatives across 5{3000 cm1, addressing key challenges in cloud and aerosol scattering, atmospheric inhomogeneity, and resolution scalability. A novel parameterization represents cloud and aerosol scattering via an apparent optical thickness, signicantly accelerating calculations for high-resolution simulations. Additionally, the model introduces an innovative treatment of atmospheric layer inhomogeneity, improving realism in atmospheric representation. Enhanced scalability allows the model to adapt to various instrument resolutions and applications, making it a exible tool for remote sensing studies involving clouds, atmospheric composition, and climate. These innovations enable accurate, ecient simulations tailored to diverse observational scenarios. The implications for hyperspectral infrared data analysis and remote sensing applications will be discussed.
Advancements in sigma-IASI/f2n: efficient radiative transfer simulations for current and future cloud, atmosphere, and climate remote sensing infrared missions
Masiello, Guido;Liuzzi, Giuliano;Serio, Carmine;Donat, Federico;Pasquariello, Pamela;Giosa, Rocco;D'Emilio, Marco;Inglese, Raffaele;Venafra, Sara
2025-01-01
Abstract
This paper presents advancements in the -IASI/f2n radiative transfer model, developed under two Italian Space Agency projects to support Italy's contribution to ESA's Earth Explorer 9 mission. The model simulates spectrally resolved radiance and its analytical derivatives across 5{3000 cm1, addressing key challenges in cloud and aerosol scattering, atmospheric inhomogeneity, and resolution scalability. A novel parameterization represents cloud and aerosol scattering via an apparent optical thickness, signicantly accelerating calculations for high-resolution simulations. Additionally, the model introduces an innovative treatment of atmospheric layer inhomogeneity, improving realism in atmospheric representation. Enhanced scalability allows the model to adapt to various instrument resolutions and applications, making it a exible tool for remote sensing studies involving clouds, atmospheric composition, and climate. These innovations enable accurate, ecient simulations tailored to diverse observational scenarios. The implications for hyperspectral infrared data analysis and remote sensing applications will be discussed.| File | Dimensione | Formato | |
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