To appreciate the radiative impact of clouds in the dynamics of the global atmosphere, it is important to deploy from space, from aircraft, or from ground, instruments able to describe the cloud layering and to document the cloud characteristics (namely liquid and/or ice water content, and the effective particle radius).

In the framework of Earth CARE (ESA), that plans to associate a cloud radar and a lidar on the same space platform, RALI (RAdar-LIdar) airborne system, developed at IPSL (France), is an interesting demonstrator. RALI combines the 95 GHz cloud radar of the CETP and the 0.5 µm wavelength backscattering lidar of the Service d'Aéronomie.

The first tests of RALI were successfully accomplished during the last CARL 2000 and CARL 2001 field projects (in November 2000 and in March 2001, in France), where both instruments were mounted aboard the French ARAT aircraft. Interesting cases have been studied, particularly a case from CARL2000 where a supercooled layer has been detected in an ice stratus.

In order to derive the radiative and microphysical properties of clouds, a synergetic algorithm has been developed. It combines the backscatter coefficient, b_a, from the lidar and the apparent reflectivity, Z_a, from the radar to infer properties of the particle size distribution. The principle of this algorithm is to apply in parallel the Hitschfeld-Bordan algorithm to the radar and the Klett algorithm to the lidar. Taken separately, these two algorithms are unstable, but by considering a mutual constraint, it is shown that a stable solution can be established.

This solution formulates the retrieval of attenuation parameters of lidar and radar, which allow us, by combining retrieved reflectivity of the radar and backscattering coefficient of the lidar, to access microphysical and radiative parameters of clouds. This algorithm allows also to retrieve the variable N₀^* parameter, which is a normalization parameter of the particle size distribution.

This synergetic algorithm has been tested with simulated cases, and results of the algorithm applied on real data are validated by microphysical in-situ measurements.