The Cooperative Institute for Research in the Atmosphere (CIRA) in Fort Collins, Colorado, together with the NOAA/NESDIS RAMM Branch is developing and distributing the Low-Cloud/Fog Product.
The Low-Cloud/Fog product is created at CIRA, and is sent to NWS Central Headquarters, and then distributed to the WFO’s as a product on their AWIPS.
The size of one east or west Low-Cloud/Fog image is 20 MB, with updates available every 30 minutes.
The Low-Cloud/Fog satellite imagery product, developed at CIRA, is demonstrated on the RAMSDIS Online web page for both GOES-West and GOES-East. The product displays standard GOES Imager data in a unique way using a simple algorithm, producing a similar product day or night except for the white/black appearance for thin cirrus during the day/night. Inputs are the 3.9um (shortwave) and 10.7 um (longwave) infrared window bands from the GOES Imager.
The Low-Cloud/Fog product demonstrates a unique kind of imagery that is already available, but is under-utilized, as well as a continuing product in the GOES-R era. GOES-R will feature the Advanced Baseline Imager (ABI) sensor which will be able to produce both a higher spatial resolution (2 km) and higher temporal resolution (5 min) version of the Low-Cloud/Fog product.
Here is a brief description of how the Low-Cloud/Fog product is created from GOES Imager data:
The Low-Cloud/Fog product can be computed from two (longwave and shortwave) infrared window bands available on nearly all operational geostationary and polar-orbiting satellites, such as GOES, MODIS, etc…
A simple algorithm is used to compute the reflected (only) component of the shortwave (3.9 µm) infrared window band, which normally consists of both emitted and reflected energies. The emitted component is removed through the use of the longwave (10.7 µm) infrared window band, by computing the shortwave-equivalent radiance from the longwave radiance through simple Planck function relationships. (The behind-the-scenes name for the Low-Cloud/Fog product is the “Shortwave Albedo” product, indicating that the image is really the reflected component or albedo that remains after the emitted component has been subtracted.)
The reflected component of the shortwave infrared window band is the key to the product. In the shortwave portion of the spectrum, ice clouds and snow are not very reflective, unlike water-droplet clouds which are highly reflective. This characteristic leads to an easy way to distinguish between ice and water clouds. Although this distinction is seen in the shortwave infrared window band alone, subtracting the emitted component of the shortwave highlights/enhances the reflected component for the user of this product.
This product has heritage in the “fog” product that was developed after the first appearances of the shortwave infrared window band on geostationary satellites in the 1980s. The fog product however has its best application at night. Another “reflectivity” product for daytime use has also been used to distinguish between ice and water clouds. But the Low-Cloud/Fog product is a more general form of both products, generated by a single formula both day and night, unlike the distinct fog and reflectivity products which are generated differently.
For a detailed description of the Low-Cloud/Fog product, see Kidder et al (2000)
Figures 3 and 4 are further examples of the Low-Cloud/Fog product, but for the eastern U.S. The daytime example in Figure 3 shows low clouds or fog as white in the Great Lakes region, the Texas panhandle, and surrounding the front lying across the Ohio River valley. Thick high clouds are colored brightly. (Color enhancement is used when the radiative/brightness temperature is below -30°C.) Middle-level clouds with mixed phase (ice and water) are medium gray shades. A few surface observations of fog (F) are shown along the Gulf Coast, otherwise most of the low clouds are probably stratus and not necessarily fog (on the ground).
Kidder, S.Q., D.W. Hillger, A.J. Mostek, and K.J. Schrab, 2000: Two simple GOES Imager products for improved weather analysis and forecasting. Nat. Wea. Dig., 24(4), (December), 25-30.
The accompanying nighttime example in Figure 4 occurred a few hours earlier than the image in Figure 3. The meteorological situation is similar, with thick high clouds along the front showing up as brightly colored. Low clouds, showing up as white, are in portions of Florida and the Gulf Coast of Texas. Low clouds are also seen in the Great Lakes states and provinces. The main difference between the nighttime and daytime products is the dark appearance of thin cirrus at night, compared to their bright appearance during the day. Cirrus are most apparent surrounding the front in Figure 3.
The Low-Cloud/Fog product provides a simple yet visually powerful display of different types of clouds. The color enhancement clearly shows higher, colder clouds as distinct from lower clouds, which appear white. Low clouds are clearly differentiated from snow, which appears dark due to its low reflectivity in the shortwave region.
The main disadvantage of this product is the fact that low clouds cannot be seen when high clouds obscure the lower clouds below. Especially at night, thin cirrus will appear black and can mask low clouds. Some of this may be alleviated by observing a temporal loop of the Low-Cloud/Fog product, to see through the higher clouds as they move. Product mages are currently available every 30 minutes at 4 km spatial resolution.
The Low-Cloud/Fog product is generated both day and night, but with the differences explained. This also leads to a transition in the product as the day-night terminator passes over the image area. Because of the formula used to compute the “shortwave albedo”, there are situations for which the product goes out of reasonable range (essentially when the equation is close to a divide-by-zero situation), and that occurs at low sun angles. In those cases, the reflected solar energy and the emitted energy from the earth are about equal. The solution again is to observe the Low-Cloud/Fog product over time, with a new image every 30 minutes, and to not rely on a single image for any analysis.
Finally, in discussing the examples shown, we have been careful to use the language “low-cloud/fog” instead of distinguishing between the two. The reason for this is because the simple GOES Low-Cloud/Fog product cannot by itself distinguish between a low cloud and a fog layer (where the latter is simply a low cloud whose base is in contact with the surface). However, there exist promising techniques that attempt to distinguish between the two, but that is beyond the scope of this product. For details on the future of low-cloud/fog product, see Hillger 2008 , where MODIS imagery is used as a proxy for GOES-R ABI, and three-color imagery can provide some hints at the low-cloud/fog differentiation.
Interactions with NWS users via the Proving Ground will assist algorithm developers in improving this product, such as the desired enhancements, product scaling, etc., to best tailor this unique application to the end-user needs. The development of future, improved products will also benefit from user feedback.
Hillger, D.W., 2008: GOES-R Advanced Baseline Imager Color Product Development, J. Atmos. Ocean. Technol. – Atmospheres, 25(6), (June), 853-872.