11A.5 Fire Detection with GOES-16: Year One

Thursday, 11 January 2018: 11:30 AM
410 (Hilton) (Austin, Texas)
Christopher C. Schmidt, Univ. of Wisconsin/CIMSS, Madison, WI

The location and intensity of fires greatly affects our health and safety. In recent years, geostationary detection of fires has gained traction, particularly in smoke and aerosol modeling but also for monitoring of active wildfires as algorithms and sensors have improved. The Wildfire Automated Biomass Burning Algorithm (WFABBA), which became an operational product from NOAA/NESDIS (National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service) in 2002, provides that information to us today. The WFABBA produces fire location and characterization data for all data received from GOES-13/-14/-15, Meteosat Second Generation, COMS, the formerly operational MTSAT series, and the Advanced Himawari Imager (AHI) on Himawari-8. As the WFABBA adapted to the GOES-R series imagers, the Fire Detection and Characterization Algorithm FDCA) allows for excellent continuity as we transition to the new generation of geostationary imagers represented by ABI and its fraternal twin, AHI. Data began streaming from GOES-16 in January 2017, leading shortly thereafter to the validation process for the Level 2 products such as the FDCA. This new platform has led to new insights and revealed new challenges presented by the new sensors. Case studies using GOES-16 ABI data will be used to illustrate validation progress to date as well as potential directions for further development of geostationary fire detection algorithms.
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