The toggle above, of Brightness Temperature Difference and GOES-R IFR Probabilities, shows how the traditional method of fog/low stratus detection — brightness temperature difference fields — can overpredict where fog and low stratus are actually observed. There are two main reasons for this: First, in regions of dry soil (over the Desert Southwest, for example), emissivity differences in soil can trigger a large difference in satellite-perceived brightness temperature at 10.7µm and 3.9µm that leads to a fog-like signal; second, in regions of water-based clouds, the signal is for the cloud top only. The satellite signal gives little information about the thickness of the cloud or of the cloud base. In the example above, mid-level stratus over central Kansas southward through central Oklahoma into central Texas yield a brightness temperature difference signal similar to regions of low clouds over the northern Texas panhandle. Compare the observations at Dalhart TX (KDHT) and Alva, OK (KAVK), for example.
The fused product (GOES-R IFR Probabilities) yields a statistically superior picture of the region of low stratus and fog because of the use of Rapid Refresh Data. These model data include the effects of surface-based observations and can therefore screen regions where low clouds are not actually present. GOES-R IFR Probabilities therefore give a better estimate of exactly where the low clouds present a hazard to — for example — aviation.