The Snow/Cloud Discrimination product, developed at the 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 Red-Green-Blue (RGB or 3-color) combination of images and image products, producing a single daytime-only Snow/Cloud Discrimination product. Inputs are the 3.9 µm (shortwave) window band, the 10.7 µm (longwave) infrared window band, and the 0.7 µm (visible) window band from the GOES Imager. Only the infrared window band is used directly, whereas the visible and shortwave bands are first converted into the Visible Albedo product, and the Shortwave Albedo products, respectively.
The Snow/Cloud Discrimination 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 Snow/Cloud Discrimination product.
The Snow/Cloud Discrimination product, developed at the 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 Red-Green-Blue (RGB or 3-color) combination of images and image products, producing a single daytime-only Snow/Cloud Discrimination product. Inputs are the 3.9 µm (shortwave) window band, the 10.7 µm (longwave) infrared window band, and the 0.7 µm (visible) window band from the GOES Imager. Only the infrared window band is used directly, whereas the visible and shortwave bands are first converted into the Visible Albedo product, and the Shortwave Albedo products, respectively.
The Snow/Cloud Discrimination 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 Snow/Cloud Discrimination product.
Here is a brief description of how the Snow/Cloud Discrimination product is created from GOES Imager data:
The Snow/Cloud Discrimination product can be computed from three (longwave infrared, shortwave infrared, and visible) window bands available on nearly all operational geostationary and polar-orbiting satellites, such as GOES, MODIS, etc.
Since the Snow/Cloud Discrimination product is a combination of three images, two of which are derived image products, each of those images or products are explained separately as follows:
1) For the shortwave infrared window band, 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. (This part of the Snow/Cloud Discrimination product treats the shortwave infrared window band in the same way as in the Low-Cloud/Fog product, another Proving Ground product that is also available from CIRA, for which a product description is available.)
The reflected component of the shortwave infrared window band is a 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. A zenith-angle-correction is also applied to each pixel of the product, to further enhance the parts of the image with low sun angles. An example of this shortwave component is given in Figure 2.
Figure 2. Example of the Shortwave Albedo product, as a component of the Snow/Cloud Discrimination product, during the daytime over the eastern United States. This product depicts how the shortwave infrared window band is treated prior to being combined into the Snow/Cloud Discrimination product. This product is also known as the Low-Cloud/Fog Product, another Proving Ground Product available from CIRA. In this image, high clouds are color-enhanced, but that enhancement is not part of the Snow/Cloud Discrimination product.
2) For the visible window band, a simple algorithm is used to compute the zenith-angle-corrected reflectance at each pixel of the image, resulting in a lighter/brighter image that appears is if the sun is overhead at each pixel. This part of the Snow/Cloud Discrimination product treats the visible window band in the same way as in what’s known at CIRA as the Visible Albedo product. For a detailed description of the Visible Albedo product, see Kidder et al (2000).
An example of this visible component is given in Figure 3.
Figure 3. Example of the Visible Albedo product, as a component of the Snow/Cloud Discrimination product, during the daytime over the eastern United States. This product depicts how the visible window band is treated prior to being combined into the Snow/Cloud Discrimination product. This product is also known as the Visible Albedo Product.
3) For the longwave infrared window band, this band is used directly. There is no intermediate product, as for the other two components of the Snow/Cloud Discrimination product. An example of the longwave infrared window band is given in Figure 4.
Figure 4. Example of the longwave infrared window image, as a component of the Snow/Cloud Discrimination product, during the daytime over the eastern United States. In this image, high clouds are color-enhanced, but that enhancement is not part of the Snow/Cloud Discrimination product.
At this point the three components described above are combined using Red-Green-Blue techniques that are common in most imagery manipulation software. The Visible Albedo is used as the Red component, the Shortwave Albedo is used as the Green component, and the Infrared window band is used as the Blue component. Figure 5 is a flowchart of the RGB process.
Figure 5. Flowchart of the Red-Green-Blue processing of the three components of the Snow/Cloud Discrimination product.
The Snow/Cloud Discrimination product provides a visual display of different types of clouds, as well as snow coverage for the land areas that are not covered by cloud. The color enhancement clearly shows higher, colder ice clouds in magenta, distinct from lower clouds, which appear as off-white. Low clouds are clearly differentiated from snow, which appears red to its low reflectivity in the shortwave region and to its cold temperatures in the infrared.
The main disadvantage of this product is the fact that snow-covered land clouds cannot be seen when clouds obscure the land below. Some of this may be alleviated by observing a temporal loop of the Snow/Cloud Discrimination product, to see through the clouds as they move. Product mages are currently available every 15 minutes at 4 km spatial resolution from current GOES imagery.
The Snow/Cloud Discrimination product is generated both day and night, but with major deficiencies at night, due to the lack of visible radiation. The lack of visible radiation causes a loss of the visible band and the Visible Albedo component, as well as a change in the shortwave band and the Shortwave Albedo component. There is also a transition in the product as the day-night terminator approaches or leaves the image. At night the lack of visible radiation eliminates the red color for snow-covered land and along with it, the distinction between high-level ice clouds and snow-covered ground.
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.