Special Issue "Radiative Transfer Models of Atmospheric and Cloud Properties"
Deadline for manuscript submissions: closed (15 June 2020) | Viewed by 20668
Interests: atmospheric radiative transfer; satellite; airborne and ground-based remote sensing; retrieval of atmospheric and surface properties; electromagnetic scattering theory; cirrus; operational satellite data assimilation; numerical methods; big data; machine learning techniques
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Versatile state-of-the-art atmospheric radiative transfer models are of paramount importance in the research of the effect of atmospheric greenhouse gases, clouds, and aerosols in climate simulations. These radiative transfer models must make use of the latest available information on gaseous absorption properties as well as on the optical properties of cloud and aerosol particles. Numerical weather prediction and climate models require accurate and fast radiative transfer codes for the simulation of vertical profiles of atmospheric heating/cooling rates. Another area with ever increasing demand on the radiative transfer codes is the assimilation of satellite data with new missions like the polar-orbiting IASI-NG, and the geostationary MTG-IRS, to be launched in a couple of years. Future missions will further extend the range of the electromagnetic spectrum that is monitored from space. The sub-millimetre and far-infrared part of the spectrum promise more information on cirrus clouds in particular. Fast radiative transfer models are only beginning to make use of dimensionality reduction and machine learning techniques. This is equally true for the treatment of the inverse problem in remote sensing and the analysis of information content. Clouds remain difficult to model. Studies that consider the importance of 3D effects, polarization, and cloud overlap, as well as schemes that can model these effects fast while remaining sufficiently accurate, are therefore also welcome.
Dr. Stephan Havemann
Manuscript Submission Information
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- Radiative transfer
- Atmospheric greenhouse gases and climate
- 3D cloudy radiative transfer
- Approximate treatments of clouds including overlap
- Polarized radiative transfer of clouds and surfaces
- Fast forward models
- Dimensionality reduction
- Machine learning
- Satellite, airborne, and ground-based remote sensing
- Inverse problems in remote sensing
- New satellite missions