![clscd_2](https://rammb2.cira.colostate.edu/wp-content/uploads/2019/10/clscd_2-300x300.png)
Fig. 2. Clockwise from upper-left: Visible (VIS), shortwave infrared (SIR), thermal infrared (TIR), and 1.38 micrometer cirrus-band (CIR) for the same date/time/domain as shown in Fig. 1. Each of these channels contains unique information that can be exploited in complement to discriminate between cloud and snow cover.
Knowledge of snow cover is important for numerous applications, including search and rescue (e.g., assisting pilots over mountainous terrain), water supply resource monitoring and management, short-term forecasting (e.g., of radiation fog or monitoring the progress of a major winter storm), and recreation. Satellites offer a distinct perspective on snow cover, particularly in areas where there are few ground observing stations. The purpose of this product is to assist satellite imagery analysts in distinguishing snow vs. cloud cover during the daytime hours, and is particularly relevant during the winter months when snowfall occurs across many parts of the United States. This product takes an additional step beyond the Cloud / Snow Discriminator product (which depicts all clouds as yellow) by differentiating between low and high cloud cover.
Why is this a GOES-R Proving Ground Product?
The Cloud Layers & Snow Cover Discriminator product demonstrates the kind of imagery that will be possible in the GOES-R era at significantly higher temporal resolution. The GOES-R series of satellites will feature the Advanced Baseline Imager (ABI) sensor, which includes channels that are currently unavailable on GOES satellites. The new channels will enable new and/or improved capabilities which can be demonstrated now only via proxy or simulated datasets.
The Cloud Layers & Snow Cover Discriminator product demonstrates the kind of imagery that will be possible in the GOES-R era at significantly higher temporal resolution. The GOES-R series of satellites will feature the Advanced Baseline Imager (ABI) sensor, which includes channels that are currently unavailable on GOES satellites. The new channels will enable new and/or improved capabilities which can be demonstrated now only via proxy or simulated datasets.
Discriminating between cloud and snow cover over complex terrain using conventional visible and infrared satellite imagery can be extremely challenging, even to experts in field of satellite imagery interpretation. At visible-light wavelengths, snow cover and clouds are both highly reflective and therefore difficult to distinguish. At thermal-infrared wavelengths, the temperature of snow cover can be similar to clouds as well. Stronger absorption by snow/ice at shortwave infrared wavelengths helps to distinguish these features from liquid-phase clouds, but some ambiguity remains between snow on the ground and ice-phase clouds. Even when adding the shortwave infrared channels (e.g., 1.6 and 3.9 micron, which provide improved discrimination of snow from clouds due to preferential absorption by snow cover), analyses are not entirely free of cloud/snow ambiguity due to the spectral behavior of certain varieties of cirrus. Additional channels available to the Moderate-resolution Imaging Spectroradiometer (MODIS), and particularly the 1.38 micron “cirrus detection” channel, improve the handling of this problem. Fig. 2 demonstrates how some of these discriminators appear in single-band imagery for a complex scene over Colorado. Combining information from several channels allows us to isolate the snow cover, but doing so via toggling back and forth between the individual channels can be a tedious and inaccurate process. The Cloud Layers & Snow Cover Discriminator product (see Fig. 1; right panel) attempts to eliminate this legwork by combining the complementary information from multiple channels into a single visual aide.
![clscd_3](https://rammb2.cira.colostate.edu/wp-content/uploads/2019/10/clscd_3-300x94.png)
Fig. 3. Example of color gun usage in the Cloud Layers & Snow Cover Discriminator product, showing how the red (R), green (G), and blue (B) guns combine to provide various tonalities to the features of interest (Land, Snow, Low-Level Liquid Water Cloud, Mid-to-High Mixed-Phase/Ice Cloud).
Channels offering unique information about the features of interest are combined to form a single product. The illustration above shows how this information is fed into the R/G/B “color guns” with varying strength of response (denoted by the height of the blue bar, with a full bar implying high strength) for land, snow, and different levels of cloud. In the current configuration of the product, all three of the color guns respond strongly to snow cover. As shown in the composite key to the right of the table, strong contributions from all color guns produces a white tonality (the intersection of the R, G, and B circles in the diagram) for snow. For low clouds, depression of the blue color gun information while maintaining strong red & green contributions produces a yellow tonality (the intersection of only the R and G circles). In this way, the composite technique reduces ambiguities associated with any single piece of information to yield an improved delineation of cloud and snow.
Channels offering unique information about the features of interest are combined to form a single product. The illustration above shows how this information is fed into the R/G/B “color guns” with varying strength of response (denoted by the height of the blue bar, with a full bar implying high strength) for land, snow, and different levels of cloud. In the current configuration of the product, all three of the color guns respond strongly to snow cover. As shown in the composite key to the right of the table, strong contributions from all color guns produces a white tonality (the intersection of the R, G, and B circles in the diagram) for snow. For low clouds, depression of the blue color gun information while maintaining strong red & green contributions produces a yellow tonality (the intersection of only the R and G circles). In this way, the composite technique reduces ambiguities associated with any single piece of information to yield an improved delineation of cloud and snow.