Continued Development of the GOES-R AWG Fog/Low Cloud Products CIMSS/SSEC Contributors: - Corey Calvert, Shane Hubbard and Scott Lindstrom NOAA/University Collaboration Project Partners: - Michael Pavolonis (NOAA/NESDIS/Center for Satellite Applications and Research Advanced Satellite Products Branch) Funding: $?????K Progress to Date: - Reviewed feedback from the GOES-R Proving Ground to further refine the GOES-R FLS algorithm in preparation for transition to NESDIS operations - Developed experimental methodology to up-scale the spatial resolution of the GOES-R FLS products using high resolution surface elevation data and polar orbiting satellite data Education Proving Ground – small but strong: Our current plan is to engage six dedicated teachers; three teams of two colleagues working in the same school system, one team from California, one team from Wisconsin and team from Florida. This core group of six teachers would work with CIMSS until and after the 2015 launch date. Our long term goal is to have teachers attend the launch, working in collaboration with Prof Rusher, CIMSS scientists and Dr. Nina Jackson. Intended Project Outcomes - Transition the GOES-R FLS products to NESDIS operations by August 2016 - Continue work to up-scale the spatial resolution of the GOES FLS products

Up-scaling GOES Spatial Resolution High resolution Low resolution Fog Fog Fog Due to the lack of spatial resolution, detailed detection of small-scale fog is difficult using GOES Surface elevation data (0.5 km) can be used to create a ‘valleyness’ metric (right) to identify valleys where fog commonly occurs This ‘valleyness’ metric, along with LEO data (e.g., MODIS/SNPP) can be used to up-convert the low-resolution GOES IFR probability product High resolution Blue/green indicates mountains Red/orange indicates valleys

Up-scaling GOES Spatial Resolution The high resolution ‘valleyness’ metric, along with SNPP data, is used to focus sub-pixel satellite signals from GOES to where fog is most likely present This methodology will allow the GOES-R FLS products to more accurately detect small-scale areas of valley fog This should also help future fog alerting capabilities as fog starts to form before becoming more widespread Original GOES SNPP RGB Modified GOES