Advances in research products from combined CALIPSO-CloudSat observations within the A-Train : aerosol and elevated cloud optical depth direct retrieval over ocean, water clouds and land and first seasonal analyses

The aerosol radiative forcing is still one of the main uncertainty in climate change. The aerosol optical thickness and aerosol type identification are key properties we need to retrieve accurately to increase our scientific understanding of this forcing. So far, despite the increased sophistication and realism of the aerosol retrieval algorithms, discrepancies still exist between retrievals of aerosol optical depth even over ocean regions. These discrepancies are due to different assumptions in the cloud clearing algorithms, the aerosol models used and different parameterizations of ocean surface reflectance. We will show how we did overcome such issues using a fusion of different A-Train observations (CALIPSO/CLOUDSAT/AMSR-E/MODIS) over dense targets (liquid water clouds, ocean and land surface). This is made possible over the ocean through the use of combined high resolution lidar/radar measurements (both vertical and horizontal), and over land from cloud and land reflectance deduced from CLOUDSAT signal, MODIS radiance and lidar multiwavelength/polarized channels. An analysis of aerosol type can be provided over the ocean.

We will present a general overview of the recent advancement we made on those direct optical thickness retrieval of optically thin atmospheric features (aerosol, cirrus clouds), and show first results on the seasonal variability observed over the globe, as compared to well established climatologies from MODIS and PARASOL.

One direct consequence of this work is a better understanding of the impact of microphysical properties assumptions in radiometric retrievals and will lead to a better estimation of aerosol and cloud radiative forcing trends due to anthropogenic activities.

Data fusion between this direct optical thickness retrieval and collocated passive remote sensing measurements (e.g., PARASOL, MODIS, OMI and GLORY) could enable in the future an AERONET-like of retrieval concept from A-train measurements.