Estimation of instrument cloud base conditions at night using GOES and surface temperature data

Low ceilings and visibilities caused by fog and stratus clouds are one of the most significant weather-related factors in aircraft accidents and operational flight delays. Detection of low clouds or fog at night can be accomplished using an image derived from the temperature difference between the short wave (3.9 micron) and long wave (10.7 micron) infrared (IR) imager channels on the Geostationary Operational Environmental Satellites (GOES), sometimes referred to as the “fog product.” One weakness of the product, however, is that it cannot easily distinguish between clouds that cause very low ceilings and visibilities at the surface, versus higher-based stratus, stratocumulus, and altostratus that do not represent significant hazards to aviation. Empirical rules have been developed to help forecasters make such assessments from the satellite imagery, but they require some experience for successful diagnosis of fog without the use of surface data. A more quantitative product is desired that would provide an estimate of the likelihood of low ceilings or visibilities that can result in hazardous flight conditions. At the National Weather Service’s Pacific Coastal Forecast Systems workshop held in July, 1998 in Silver Spring, Maryland, there was a proposal to produce an experimental, enhanced GOES fog product that would provide probabilities of certain critical ceilings and visibilities for aviation users.

Data from the summer of 1997 has been collected and analyzed, and a good correlation was found between the GOES longwave infrared minus surface temperature difference, and instrument cloud base conditions (<1000 ft). An experimental prototype product has been developed that can be automatically generated, and will soon be available on an Internet Web site. Examples of the product will be shown, along with some preliminary verification, and a discussion of known product weaknesses. Based on preliminary results, this appears to be a promising approach to providing aviation forecasters with an additional satellite-based guidance product.