Project Lead: Oleg Dubovik
Both climate models and remote sensing algorithms are attempting to provide accurate modeling of microphysical and optical properties of atmospheric aerosol. At the same time, the focus of these algorithms have differences. Climate models focus on realizing the most accurate chemical transport of aerosols by accounting for chemical and physical atmospheric processes, while RS is focused on developing the most accurate models of aerosol optical properties possible to produce results that are consistent with radiometric observations. Therefore, there are some differences in representation of aerosol properties by climate models and remote sensing. For instance, remote sensing techniques are more accurate than climate models at modeling aerosol optical properties and radiation effects, but satellites in low Earth orbits only provide daily ‘snapshots’ (at best) at any given location. Climate models can provide the spatial and temporal variability of aerosol components (or species) between the snapshots.
The objective of HAMR is to harmonize aerosol representations connecting climate models and remote sensing. Several complimentary positive outcomes can be expected from these efforts. First, climate models can certainly benefit from the experience accumulated by remote sensing for improving optical properties of aerosols. Second, the refining of climate models relies heavily on aerosol assimilation models; therefore, aligning climate models with remote sensing may certainly affect very positively the efficiency of aerosol assimilation models. Third, the harmonization of climate models and remote sensing can significantly improve the efficiency of remote sensing approaches that attempt to use climate models data as a priori constraints. Indeed, the aerosol properties are very complex and practically no single remote sensing approach has sufficient information content to reliably retrieve the full state vector of aerosols that are generally considered by climate models. Correspondingly, climate model data is one of most evident sources of a priori information for remote sensing techniques. Thus, HAMR is expected to significantly improve the consistency of climate models and remote sensing and notably enhance the efficiency and accuracy of both aerosol assimilation models and remote sensing.
We seek discussion and collaborations in the following areas:
+ How can we facilitate comparisons of modeled aerosol species to remote sensing of aerosol type?
+ How can remote sensing observations be configured for easy assimilation by climate models?
+ How can climate model output of aerosol species and/or types be implemented as a viable constraint for remote sensing retrievals?