A dynamic global cloud layer for virtual globes

Abstract

We describe a technique to merge multiple environmental satellite data sets for an hourly updated, near real-time global depiction of cloud cover for virtual globe applications. A global thermal infrared composite obtained from merged geostationary- (GEO) and low-Earth-orbiting (LEO) satellite data is processed to depict clear and cloudy areas in a visually intuitive fashion. This GEO-plus-LEO imagery merging is complicated by the fact that each individual satellite observes a single ‘snapshot’ of the cloud patterns, each taken at different times, whereas the underlying clouds themselves are constantly moving and evolving. For the cloudy areas, the brightness and transparency are approximated based upon the cloud top temperature relative to the local radiometric surface temperatures (corrected for surface emissivity variations) at the time of the satellite observation. The technique clearly defines and represents mid- to high-level clouds over both land and ocean. Due to their proximity to the Earth's surface, low-level clouds such as stratocumulus and stratus clouds will be poorly represented with the current technique, since warmer temperatures in this case do not correspond to higher cloud transparency. Overcoming this problem requires the introduction of multispectral channel combinations.

Acknowledgements

The first two authors would like to acknowledge the support of the Google Earth GIS team at Google, Inc. The MODIS data are provided courtesy of the National Aeronautics and Space Administration (NASA) Near Realtime Processing Effort (NRTPE). We would like to acknowledge Dr Ben Ruston of the Naval Research Laboratory for the use of the surface emissivity database and for advice and guidance on its use in this application.

Notes

Current affiliation: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.