On the retrieval of cloud and precipitation properties from space (Invited Presentation)

Clouds play an important role in the hydrologic cycle, influence global energy balance, and represent a significant yet poorly understood component of global climate change. As a result, quantitative global observations of liquid and ice cloud microphysical and radiative properties continue to be a focus of a growing number of satellite-based sensors each having an associated suite of retrieval algorithms. While a number of these algorithms have successfully been applied to map clouds, many can only be applied under specific conditions (e.g. during the daytime) or over a limited dynamic range (e.g. optically thin cirrus) often leading to unphysical discontinuities when one seeks to compile a complete picture of the global distribution of clouds. Furthermore, discrepancies exist between products of different algorithms when they are applied to the same scene by virtue of differences in the information provided by distinct combinations of measurements.

With the near emergence of spaceborne cloud radar (CloudSat) and backscattering lidar (CALIPSO) systems, expected in early 2005, and with potential for combining these observations with more conventional satellite observations, the promise of new ways of observing clouds is fast approaching. In preparation for understanding how the data from these new sensors might be used optimally with these more conventional radiometric data, it is important to deliberate on the respective information content contained in the measurements of the different sensors. This presentation reviews the problem of cloud microphysical property retrievals using conventional satellite radiance observations and demonstrates how the concept of information content can be used as a metric for evaluating the utility of a set of observations in a retrieval problem. This approach will then be used to demonstrate how the subset of wavelengths that provide the optimal amount of information for global microphysical property retrievals from the MODIS instrument might be selected. Discussion on how these types of observation might be merged with the observations from the active sensors to provide improved cloud and precipitation will also be presented.