Cloud parameter retrieval from combined remote sensing observations

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 gr ound, instruments able to describe the cloud layering and to document the c loud characteristics (namely liquid and/or ice water content, and the effec tive particle radius). Combining passive and active remote sensing techniqu es, microwave or VIS/IR, is a possible way to achieve this goal. A statisti cal knowledge of particle spectra drawn from microphysical data base is nev ertheless indispensable to build the inverse model and algorithms needed to retrieve the cloud parameters from remote sensing observations. The presen t paper covers three subjects: Techniques to analyse particle spectra from cloud databases: What are the k ey parameters to characterise a particle spectrum? What are their statistic s? How do they vary with temperature? Building of the inverse model: How do the parameters which define the respo nse of remote sensing instruments (radar reflectivity Z, radar specific att enuation K, lidar backscattering coefficient beta, lidar extinction coeffic ient alpha) relate to the cloud parameters interesting to evaluate cloud, r adiative properties (liquid water content LWC, ice water content IWC, effec tive radius of particles r(e)). Algorithm retrieval: What are the uncertainties in the retrievals of radar or lidar alone? What do combined observations of radar and lidar bring? Wha t kind of combined algorithm can we consider to improve the retrieval? Results of the combining algorithm applied tot data sets are then presented . (C) 2000 Elsevier Science Ltd. All rights reserved.