Although the study of atmospheric radiative transfer is the main interest in the Stephens Research group, this interest extends significantly into how radiative transfer processes affect the climatic evolution. This is a major application of radiative transfer modeling since radiative transfer processes enter so prominently into the genesis of climatic systems. That this is indeed the case is attested to by the fact that solar radiation near the top of atmosphere is partially transformed into other forms of energy that drive the general circulation. This conversion of energy is greatly affected by clouds that reflect much solar radiation back into space and simultaneously are good absorbers of IR. The episodic occurrence of clouds may be considered as a type of forcing that redistributes energy in time and space.

To obtain at least a rudimentary understanding of how such forcing can affect climatic evolution, we have employed the relatively simple Lorentz model as a investigative tool. This model exhibits internal instability that causes the computer result to be quite unpredictable. This is desirable in as much as it captures a dominant characteristic of the real climate.

• The Lorentz Model

CloudSat is a satellite program designed to provide new observations of clouds and aerosols. The program will contribute in specific ways to these major climate change thrusts. (i) CloudSat will fulfill critical observational gaps that have been identified by various sectors of the climate community. (ii) CloudSat will provide a significant advance to our current understanding of a number of cloud and aerosol-related processes vital for understanding climate change

Further Infomration is available from the CloudSat Home Page.

This research is an exploratory effort that seeks to test the realism of clouds predicted in the forecasts of the European Centre for Medium Range Weather Forecasting (ECMWF). This is an important, challenging class of problem, and might be considered as an important "next step" in the assimilation of cloud satellite data in global models.

This project impacts the public good and is of special interest because it lays the groundwork for introducing a significantly higher level of skill to existing forecasting models. By contrasting model predictions with real measurements, model shortcomings may be identified and addressed. The synergy of models with measurements is considered to be an essential paradigm shift toward the improvement of forecasting products.

While the technology exists to further this effort, the possibilities have remained largely unexplored. The study places further emphasis on the need to include active remote sensing instrumentation on the space platform, and addresses the role of CloudSat in this capacity.