Inhomogeneous and fractional cloud parameter retrieval using neural network. First comparisons with MODIS products

Improvements of radiometric observational capabilities enable the development of new approaches for cloud parameter retrieval. In this study, we investigate the possibility of retrieving cloud parameter of inhomogeneous cloud and fractional clouds by using neural network techniques. The inverse model is defined, at the scale of retrieval, with 6 cloud parameters: the mean optical depth, the mean droplet effective radius, the fractional cloud cover, the optical thickness inhomogeneity, the effective radius inhomogeneity and the cloud top temperature. The retrieval procedure includes two separate steps: the first one is relative to the interpolation and the thermal contribution correction for the near-infrared wavelength. The second step concerns the cloud parameter retrieval. The input vector of the retrieval procedure uses multispectral information and different spatial resolution The whole procedure was tested on different types of synthetic inhomogeneous and fractional clouds. All the cloud parameters of these types of clouds can be retrieved with reasonable accuracy. Following these studies, we applied this procedure to real measurements provided by MODIS on Terra and compared results with MODIS products.