Adaptive Sky: Observing Clouds Using Multi-Instrument, Multi-Platform Sensor Webs

At present there exists a large suite of spaceborne and in-situ assets operated by NASA, NOAA, and other organizations, that provide independent sensing of the Earth's atmosphere, oceans, and land surfaces. As the number of these assets grows, there is an increasing need for methods that combine these observations to provide a more complete and coherent picture of important geophysical processes. As part of a project supported through NASA's Earth Science Technology Office (ESTO), we have developed techniques that address this challenge by dynamically combining information from multiple sensors on different platforms to form sensor webs, which can respond quickly to short-lived events and provide rich, multi-modal observations of objects, such as clouds, that are evolving in both space and time. Techniques were adapted from the fields of computational data mining, computer vision, and machine learning that allow correspondence to be automatically established among various sets of observations. Two science scenarios were chosen to steer the development of the project: (1) matchups between the morning and afternoon constellations of the NASA Earth Observing System (EOS) satellites, including the MODIS, MISR, AIRS, CloudSat, and CALIPSO instruments, and (2) correspondences between satellite and ground-based cloud images. The EOS matchup scenario provided improved satellite-derived information about cloud formation and development, along with important algorithm intercomparison information. The second scenario yielded new perspectives related to the three-dimensional structure and development of clouds. This work was performed at the Jet Propulsion Laboratory, California Institute of Technology under contract with the National Aeronautics and Space Administration.