Towards retrieval of vertical cloud profiles from synergistic use of multi-angle, mult-ispectral, multi-polarimetric and multi-resolution observations of the 3MI and MetImage.

The Multi-Viewing Multi-Channel Multi-Polarisation Imager (3MI) on the METOP-SG A platform is a 2D wide field of view radiometer dedicated to aerosol and cloud observation for atmospheric composition, air quality, numerical weather prediction and climate monitoring. Leveraging on three missions of its precursor instrument POLDER, the 3MI will provide multi-spectral (from 410 to 2130 nm), multi-polarisation (-60°, 0°, and +60°), and multi-angular (10 to 14 views) observation of the Earth reflectance. Although primarily dedicated to aerosol monitoring, the multiangle and polarisation 3MI observation will provide advanced capabilities for monitoring of cloud properties and water vapour from an operational meteorological mission. In particular, 3MI will allow for detailed observation of cloud parameters that are crucial in understanding the complex interactions between aerosols and clouds, sources of large discrepancies among climate models.

Among other objectives, 3MI will enable better characterisation of cloud microphysics (phase, particle size distribution) and provide access to cloud vertical extent (geometrical thickness), all of which being provided in near-real time by so-called Day-1 algorithm.

In addition of the EUMETSAT official Day-1 clouds product, an optimal-estimation-based algorithm that makes use of the high information content of 3MI observations is being developped to retrieve vertical profile of cloud properties (LWC, extinction coefficient). While the retrieval is performed assuming an idealized but physically based distribution of LWC/IWC and extinction coefficient, the major challenge in this approach remains the computational burden involved by the iterative optimization.

To partly overcome this problem, we take advantage of the higher spatial resolution provided by the MetImage, also aboard METOP-SG A, and propose a synergistic approach based on statistical learning to improve the a priori and initial state vectors used by the optimal-estimation-based algorithm.

We will describe here the status and recent progress made for the development of the research cloud products leveraging the synergy of 3MI and MetImage observation.