92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012
A Quantitative Fog/Low Stratus Detection Algorithm for GOES-R
Hall E (New Orleans Convention Center )
Corey G. Calvert, CIMSS/Univ. of Wisconsin, Madison, WI; and M. J. Pavolonis

The GOES-R fog/low stratus (FLS) algorithm was created to quantitatively identify clouds that produce Marginal Visual Flight Rules (MVFR) conditions, defined as having a cloud ceiling below 3000 ft above ground level, in the absence of overlapping water or ice clouds during both day and night. The FLS detection algorithm utilizes textural and spectral information, as well as modeled relative humidity (RH) and the difference between the cloud radiative temperature and modeled surface temperature. At night, the algorithm utilizes modeled RH and surface temperature data and the 3.9 and 11 μm channels to detect MVFR conditions. FLS detection during the day is determined using modeled RH and surface temperature data and the 0.65, 3.9, and 11 μm channels. Nighttime LUTs were created for modeled RH data as well as combined 3.9 μm pseudo-emissivity and surface temperature bias data from both fog and non-fog water clouds determined by surface observations and the GOES-R cloud type algorithm. Daytime LUTs were created for modeled RH data as well as combined 3.9 μm reflectance, a 3x3 pixel 0.65 μm reflectance spatial uniformity metric and the surface temperature bias for the same types of water clouds determined by surface observations and the GOES-R cloud type algorithm. A na´ve Bayes probabilistic model was used to combine the information from the LUTs to produce a quantitative output providing the probability that FLS is present for a given pixel.

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