NOAA cloud product algorithm development for the next generation GOES-R and NPOESS satellite observing systems

August 27, 2009

Andrew Heidinger (SSEC/Univ of Wisconsin)

Hosted by Steve Miller

Abstract

The NOAA fleet of meteorological satellite imagers is undergoing a major transition. The GOES-R Advanced Baseline Imager (ABI) and the NPOESS Visible Infrared Imager Radiometer Suite (VIIRS) will be launched soon and both offer new capabilities over the current operational imager sensors(POES/AVHRR and GOES I-M/NOP). To exploit these new sensors, NOAA/NESDIS and its partners have developed new algorithms that exploit advances in our ability to model cloud-free conditions and to extract information for high spatial resolution fields. This talk will
demonstrate advances in our ability to use infrared imager channels for quantitative cloud remote sensing. The impact of different channel selection on the performance of VIIRS and ABI is explored using these techniques. In addition, the availability of active sensors on satellites has also led to new avenues for the optimization of algorithms employed on passive satellites. An example of the use CALIPSO for Bayesian cloud detection is presented here. Lastly, these same techniques have also paid dividends on the ability to extract useful information from the multiple decades of observations from the current suite of sensors. The talk will present our current activities in generating and analyzing cloud product time series from the POES/AVHRR and GOES-IM sensors.