| Understanding Earth's climate is a complex undertaking, and requires the development of accurate simulations that combine all the natural elements that can affect climate. The data that goes into these models needs to be as precise as possible, and collecting reliable data is an important and sometimes difficult step in this research. Clouds, in particular tropical thin cirrus clouds, exert a major influence on climate through cloud radiative forcing (CRF). These clouds, with their variable height, optical depth and particle distribution, make collecting accurate data difficult. Investigation by remote sensing techniques, in particular by LIDARs alone or in combination with other instruments, has become a popular way of retrieving cirrus cloud physical and microphysical properties such as particle concentration (N), characteristic diameter (D), ice water content (IWC) and ice water path (IWP).; The aim of this study is to document the properties of tropical thin cirrus using combinations of data collected from various remote sensing systems. A new method for determining cloud optical depths and a new parameterization that treats multiple scattering effects in the lidar equation are introduced. A novel “inverse” model applied to LIDAR measurements produces profiles for IWC and N. A simple analytical method developed for the combination of RADAR and LIDAR system is also presented. This combination of data provides vertical profiles for D, N, and IWC. One key finding derived from the measured data is the observational relationship between optical depth and IWP. Also, the active-passive combinations of LIDAR and IR radiometer, and LIDAR and MODIS Airborne Sensor are explored.; The results obtained with these new analysis tools are used to study the radiative budget of tropical thin cirrus. It is shown how the thin cirrus radiatively heat the atmosphere both within the layer of cloudiness as well as within the atmosphere below. A convenient parameterization of the LW and SW CRF as function of the IWP is tested against measured data. |
