Aerosols, clouds, and their interaction contribute the largest uncertainty to estimates and interpretations of the energy budget of Earth-atmosphere system. Over the last several decades, radiation measurements from satellites, aircraft and the ground have been successfully employed for characterizing their radiative properties. However, some challenges still remain for delivering climate-quality aerosol and cloud products. One of them involves the development of reliable and accurate procedures for inversion of the observations.
We solicit articles that emphasize the various aspects of numerical inversion. The contributions are expected to address such important attributes of inversion as multi-source data inversion, construction of novel a priori information, inverse modeling, information content assessment, retrieval error estimations, retrieval acceleration, joint inversion of aerosol/gas/cloud properties, and data assimilation.
We encourage explorations of new retrieval concept and new or improved products for existing and next generation satellite missions and ground-based networks. These products include (but not limited to) the types, composition and vertical profiles of aerosols, surface particulate matter and speciation, trace gas abundance, cloud microphysical properties, and land and ocean reflection. Development of forward radiative transfer models for complex media and particle light scattering models to improve remote sensing inversion are also welcome.
Aerosols, clouds, and their interaction contribute the largest uncertainty to estimates and interpretations of the energy budget of Earth-atmosphere system. Over the last several decades, radiation measurements from satellites, aircraft and the ground have been successfully employed for characterizing their radiative properties. However, some challenges still remain for delivering climate-quality aerosol and cloud products. One of them involves the development of reliable and accurate procedures for inversion of the observations.
We solicit articles that emphasize the various aspects of numerical inversion. The contributions are expected to address such important attributes of inversion as multi-source data inversion, construction of novel a priori information, inverse modeling, information content assessment, retrieval error estimations, retrieval acceleration, joint inversion of aerosol/gas/cloud properties, and data assimilation.
We encourage explorations of new retrieval concept and new or improved products for existing and next generation satellite missions and ground-based networks. These products include (but not limited to) the types, composition and vertical profiles of aerosols, surface particulate matter and speciation, trace gas abundance, cloud microphysical properties, and land and ocean reflection. Development of forward radiative transfer models for complex media and particle light scattering models to improve remote sensing inversion are also welcome.