This presentation will be about the development of a methodology to get a better retrieval of cloud properties by using a synergy of active/passive remote sensing instruments and radiative transfer models for a better understanding of the processes involved in the interaction between clouds and solar/thermal radiation. As a start, this presentation will focus only on liquid water clouds.
A combination of active and passive remote sensing observations is used to determine the cloud properties and cloud radiative forcing. Measurements from ground-based active remote sensing instruments (lidar and radar) are derived to get the vertical distribution of cloud properties. Upward radiation fluxes at TOA are measured using passive space-borne instruments like SEVIRI imager (onboard MeteoSat). To retrieve the downward surface radiation fluxes, ground-based measurements from passive radiation sensors (by instance from CESAR observatory, Cabauw, NL) are used. Both SW and LW domains are considered.
In synergy, we use ECSIM (EarthCARE Simulator) as radiative transfer model. ECSIM is a tool in development, which simulates the parameters measured by the instruments onboard the future ESA-JAXA joint-mission EarthCARE (Earth, Cloud, Aerosol and Radiation Experiment), which is due to be launched around 2017. The radiative transfer calculations in LW and SW domains can be done in two ways (1D- and 3D- modes); they differ basically by the algorithm approach (the 1D-mode works on vertical column approximations while the 3D-mode is based on Monte Carlo simulations) and time computation. This simulator is used to validate the accuracy of the retrieval of the parameters of liquid water clouds that were detected by the lidar and radar. A special focus will be done on the accuracy of the retrieval of the Liquid Water Path and effective radius.
The closure of the radiation scheme at surface and TOA is used to validate the retrieval of cloud microphysical properties in an effort to understand better the interaction between radiation and cloud properties at local scale and with the goal to apply and generalize the methodology at other ground-based stations, depending on their meteorological situation.
References: Wild M., D. Folini, C. Schär, N. Loeb, E. G. Dutton, G. König-Langlo (2013), The global energy balance from a surface perspective, Clim. Dyn., 40, 3107-3134, doi:10.1007/s00382-012-1569-8.