Probabilistic Detection of Low Cloud Ceilings and Cloud Phase Determination
Using the GOES-R Approach: Algorithm Description and Comparison to FRAMICE Observations
Michael Pavolonis (NOAA/NESDIS)
Corey Calvert (UW- CIMSS)
The Advanced Baseline Imager (ABI) on board the next generation of Geostationary
Operational Environmental Satellites (GOES-R) will provide spectral, spatial, and
temporal capabilities that significantly exceed those offered by the current GOES
Imager. These new instrument capabilities, in tandem with other data sets (e.g.
NWP), can be used to generate a variety of products that are capable of
automatically detecting and characterizing hazards such as fog/low cloud. Using the
Moderate Resolution Imaging Spectroradiometer (MODIS) and the current GOES
imager as proxies for the GOES-R ABI, it will be shown that quantitative products,
such as the probability of Instrument Flight Rules (IFR) and cloud thermodynamic
phase can be generated and are useful for real-time fog/low cloud hazard detection
and assessment. This talk will describe the approach, developed by NOAA’s GOES-R
Algorithm Working Group (AWG), for detecting low cloud ceilings and determining
cloud phase using a combination of satellite, NWP, and other ancillary data. In
addition, preliminary results from comparisons to FRAM-ICE observations will be
shown.