Principal Investigator (PI): Eric Fetzer, NASA's Jet Propulsion Laboratory (JPL)

The goal of this project is to create a record of all satellite sounder temperature and water vapor observations, matched to simultaneous, collocated imager cloud observations, on platforms launched by NOAA, NASA, and EUMSAT since 1998. The cloud data will also be matched to coincident Global Positioning System (GPS) Radio Occultation (RO) observations (GPS-RO).

The majority of sounder observations in this work will be obtained by the four currently operational hyperspectral infrared sounders and their associated microwave sounders and cloud imagers, beginning in 2002. We will extend the record back to 1998 to include earlier Advanced Microwave Sounding Unit-B (AMSU-B) and Advanced Very High Resolution Radiometer (AVHRR) observations. We will also complete a series of analyses to reconcile similar observations from different sounders on different platforms as a function of cloud state. Our baseline data set will be a record already created from A-Train sensors as part of our MEaSUREs work, "A Multi-Sensor Water Vapor Climate Data Record Using Cloud Classification".

The innovation in this work is the extension of those techniques to all modern hyperspectral sounders, to all NOAA satellites with AMSU-B microwave sounders, and to all coincident, collocated GPS-RO soundings. The resulting data set will extend nearly two decades (1998 to present) and relate all sounder profiles to imager cloud state. The main scientific goal of the proposed work is a reconciled temperature and water vapor record from the many different sounders in the suite of instruments considered in this work, in the context of collocated clouds from imagers. We will work toward this goal in a hierarchy of analyses and data sets. The primary technical goal is a complete data set of satellite sounding profiles combined imager clouds for all sensors, made publicly available along with associated documentation. Data processing will be completed using existing capability with the demonstrated flexibility to run locally, in a high-performance computing environment, or in a commercially available compute cloud. This flexibility offers potential cost savings over traditional in-house processing.