Daily Archives: March 28, 2013

Fog Detection over Snow

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GOES-R IFR Probabilities computed from GOES-East (Upper Left), GOES Brightness Temperature Difference (10.7 µm- 3.9 µm ) (Upper Right), Suomi/NPP 1.61 µm Reflectivity (Lower Left), Suomi/NPP Visible Imagery (0.64 µm) (Lower Right)

Snow cover in Spring promotes the development of advection fogs when relatively moist air moves over the snowpack and is cooled to its dewpoint, saturating.  But snowcover is also white when viewed from Satellite, and its presence makes the detection of fog areas difficult.  There are several products that can be used to distinguish between white snow and white clouds, and products that can be used to refine further the difference between stratus decks and fogbanks.

In the imagery above, Suomi/NPP 1-km resolution visible (0.64 µm) data in the bottom right figure show a large region of both clouds and snow over the North Dakota and surrounding US States and Canadian Provinces.  It is very difficult to find the cloud edges that are there.  The 1.61 µm imagery from Suomi/NPP is very helpful in screening out imagery of snow on the ground.  Water clouds are far more effective at reflecting radiation at wavelengths around 1.61 µm than snow or ice (snow and ice both strongly absorb radiation at that wavelength).  Thus, in the bottom left figure, the 1.61 µm reflectivity detected by Suomi/NPP, areas of snow appear dark and areas of water-based clouds are white.  This tells you where water clouds exist, but nothing about how high above the surface those water clouds are.  The brightness temperature difference product from GOES (10.7 µm – 3.9 µm) also highlights in dark regions of water-based clouds, but as with the 1.61 µm reflectivity does not give information about cloud bases.

The GOES-R Fog/Low Stratus IFR Probability neatly distinguished between the stratus deck over western Minnesota (that is not accompanied by IFR conditions at the surface) from the cloudy region over central and northern North Dakota that is accompanied by IFR conditions at the surface, as shown in the plotted observations.  (Thanks to Chad Gravelle for noticing this case today!)

One difficulty in Fog Detection

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Toggle between Suomi/NPP Day/Night Band (i.e., Night-Time Visible Imagery, 0.70 µm) and Brightness Temperature Difference field (10.8 µm- 3.74 µm) at 0734 UTC on 28 March 2013

The image toggle above shows an area of stratus over central Missouri and surrounding states.  The stratus shows up in both the Night-time visible Day/Night band from Suomi/NPP (March 28th is one day past the Full Moon, so there is plenty of lunar illumination, and indeed lunar shadows from the higher cirrus clouds over Illinois, Kentucky and Tennessee are apparent).  The brightness temperature difference field crisply highlights the region of lower, water-based clouds.  That difference field arises from the differences in emissivity properties of the water-based clouds:  they emit nearly as a blackbody around 11 µm, and not as a blackbody at 3.9 µm.

A key question for this scene is:  is this cloud that is depicted stratus at mid-levels, or is it fog?  From the top (that is, as the satellite views it), a stratus deck will look very much like a fog bank.  The satellite gives little information, however, on how thick the cloud is, or on how close to the ground it sits.  A satellite-only fog detection algorithm, therefore, will include many false positives.

MODIS-based IFR probabilities, 0811 UTC on 28 March 2013

IFR probabilities include data about the surface that are incorporated into the Rapid Refresh Model.   This fused product clarifies where the brightness temperature difference product is detecting mid-level stratus versus low-level fog.  In this case over Missouri, IFR probabilities are very low throughout the scene because saturation at low levels in the Rapid Refresh is not occurring, and therefore IFR probabilities are low.

By blending information about the top of the cloud (the brightness temperature difference product) with information about the bottom of the cloud (the Rapid Refresh model data), a more accurate depiction of the horizontal extent of IFR conditions is achieved.