Cloud optical and microphysical properties derived from satellite data

It is now well established that the complex nonlinear interactions and feedbacks generated by clouds modulate the climate response on many spatial and temporal scales. To better assess these processes, both large- and small-scale cloud systems must be observed and their properties quantified. Ideal for such a task is to use data collected by the existing multitude of satellite-based remote sensors. The use of multi-spectral information is motivated by the aforementioned complex nature of clouds, thus the ability of the forward model to properly describe the observing vector in terms of the relevant state vector components. The task of retrieving such a state vector is complicated by the fact that the majority of the sensors are passive; therefore, the problem of penetrating the cloud vertical structure is left to the sole description of an accurate, yet simple, forward model.

Present work focuses on the retrieval problem itself, by seeking better and faster algorithms to deal with nonlinearities of such forward models. In preparation for the next generation of sensors that NPOESS system provides, the retrieval algorithms are tested on the GOES system, by using a 3 channel observing system. The retrieved cloud variables of interest here are cloud top temperature, effective radius and optical depth, as well as liquid water path, emissivity, and cloud top height. These parameters bear relevance on other topics of interest for Navy applications, such as drizzling marine stratocumulus or convective clouds, for which the observing system must be extended beyond NPOESS capabilities. Currently, daytime retrievals demonstrate the feasibility of the problem, while nighttime retrievals are not as promising due to a lesser variability of the forward model description in terms of the state vector components.