1. A Probabilistic Nighttime Fog/Low Stratus Detection Algorithm
Corey G Calvert and Michael J Pavolonis*
Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin
*NOAA/NESDIS/Center for Satellite Applications and Research
Advanced Satellite Product Branch, Madison Wisconsin 
Fog Detection Approach
Reducing Noise from the Final Product
Small-Scale Detection Capabilities
The Basics
The aviation-based level of cloud ceiling under which pilots must fly using instrument flight rules (IFR) is approximately 300m. Therefore, the goal for the GOES-R fog detection algorithm is to create a fog mask to detect liquid stratus clouds with bases lower than 300m.
The GOES-R fog detection algorithm builds off the widely-used 3.9, 11m channel combination for nighttime detection of fog.
Strategy
Nighttime fog is typically characterized by the following traits:
Cloud top is close to ground so the difference between cloud temperature and surface temperature is typically small
Relatively high spectral emissivity signal (3.9,11m) at night
Fog detection is based on finding small differences between the radiometrically-derived and NWP surface temperature along with strong signals from the 3.9m pseudo-emissivity.
Rather than using specific thresholds, training data were used to create look-up tables (LUTs) that assign a probability a pixel returning certain spectral information is fog.
Cloud objects are created to group neighboring pixels with similar radiometric signals. This allows pixels within an object that have a stronger signal (usually at the center) to represent the entire object, which is useful for small-scale fog events (see far right).
Algorithm
The GOES-R fog detection algorithm can be broken down into the following four steps:
Due to the spatial resolution (4km) of current GOES satellites, detecting small-scale fog events (e.g., within river valleys) is difficult.
The use of cloud objects can enhance the detection ability by allowing pixels with a stronger radiometric signal to represent pixels within the object that may not have otherwise been classified as fog.
This helps areas such as fog edges where the signal may not be strong enough by itself to be classified as fog, but has pixels in the same object with a stronger signal that is more representative.
Using the cloud objects can help to restore detail that may be missed without increasing the spatial resolution of the instrument.
The images below depict a valley fog event over Pennsylvania and upstate New York on September 17, The upper left image is the first daylight image (11:45 UTC) showing the fog within the valleys. The upper right image is the high resolution (1 km) MODIS fog/stratus product at 7:38 UTC. The lower left image is the heritage fog algorithm at 7:45 UTC displaying fog where the BTD is below -2 K. The lower right image is the corresponding GOES-R fog product creating cloud objects using the 3.9m pseudo-emissivity. The improved spatial resolution of the GOES-R ABI will greatly enhance the future GOES fog product.
MODIS Fog/Stratus Product
In the upper left false color image, the white/red crosses represent surface observations meeting the no fog/fog criteria, respectively, for non ice clouds (given by GOES-R cloud type product). The light blue/magenta crosses represent surface observations meeting the same criteria respectively under multi-layer or ice clouds (areas excluded from the GOES-R fog detection algorithm). The upper right image is the GOES-R cloud type product.
The heritage fog algorithm (bottom left image) flags pixels with a m BTD less than -2 K. In the presence of convective clouds and non-fog water clouds this algorithm has the tendency to return ‘noisy’ pixels that are usually false alarms.
The GOES-R algorithm (bottom right image) screens these areas out using the cloud type information along with 3.9 m pseudo-emissivity data. It also uses cloud objects to group neighboring pixels with similar radiometric signals. Using a minimum object size of 3 pixels, it can significantly reduce noise in the final product.
Use the ABI cloud phase output to identify non-ice cloud pixels
Determine the fog probability for each pixel using pre-determined LUTs
Group pixels into cloud objects
Eliminate artifacts by removing any cloud objects consisting of 3 pixels or less and check spectral and spatial object metrics
Look-up tables were created using surface temperature bias along with the 3.9m pseudo-emissivity as predictors. “Truth” was gleaned from surface observations.
The 3.9 m clear-sky surface emissivity was also used to separate pixels with different surface types (e.g., desert and forest) which might lead to unrepresentative fog probability calculations.
Below is the fog LUT for the 3.9m pseudo-emissivity and surface temperature bias for pixels with a clear-sky surface emissivity between 0.90 and 0.95.
Pixels with small surface temperature biases and low 3.9 m pseudo-emissivity will be given a higher probability of containing fog.
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