GOES_R_CIMSS_Overview_FLS_2014

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Continued Development of the GOESR 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
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 lowresolution 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
SNPP RGB
Original GOES
Modified GOES
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