Fig. 3. Daytime composite of Hurricane Katrina advancing on the city of New Orleans, LA. Note cirrus transparency near Cuba, in contrast to the relative opacity of the primary hurricane cloud formations (e.g., spiral rainbands). Click on figure for full resolution.
Figures 3 and 4 demonstrate the performance of the GeoColor product for daytime and nighttime scenes using imagery from Hurricane Katrina.
During the day (Figure 3), the Blue Marble true color background depicts blue ocean water in the Gulf of Mexico as well as light green shallow-water features (highly reflective sand/shoals) near Key West, Florida, and Key Largo (south of Cuba). Green vegetation dominates the land portions of the background scene. Close inspection reveals semi-transparent thin cirrus over Cuba, in contrast to the opaque clouds associated with Katrina’s rain bands over souther Louisiana.
Fig. 4. Hurricane Katrina moves closer to the shore in this nighttime-only composite. Purple terrain has been turned off in the background of this example. City lights offer additional information on the proximity of storm features to major metropolitan areas. Click on figure for full resolution.
At night (Figure 4), the Blue Marble background is replaced by nighttime city lights, shown as yellow/orange patches (to simulate the appearance of sodium lighting which dominates most urban lighting), corresponding to the major metropolitan areas. Again, note the transparency (city lights shining through cirrus) to the north of Katrina in contrast to the opacity (coastal cities obscured by deep, cold clouds) closer to the storm’s rain bands. These city lights provide useful additional information to the imagery analyst in terms of relating the current location of weather features to population centers and major transportation corridors (e.g. small towns along interstates often manifest themselves as linear features traced out in the nighttime lights background.
Fig. 5. A comparison between GeoColor without (upper) and with (lower) the inclusion of an additional ‘low-cloud/fog detection’ layer included in red/pink tonality. The Proving Ground version of the product includes the low-cloud/fog detection capability. Click on figure for full resolution.
One of the inherent limitations of the visble/infrared version of GeoColor lies in the scaling for assignment of transparency. As explained previously, low/thick clouds at night may appear erroneously transparent in GeoColor due to their temperatures residing at the lower-end of scaling bounds. To overcome this problem requires the introduction of additional ‘spectral information’ available from GOES. It turns out that a difference between two infrared channels (3.9 and 11.0um – GOES channels 2 and 4, respectively) provide an ability to identify these low-clouds/fog layers by virtue of differences in scattering properties between the two channels. The differences give rise to an apparent difference in the cloud-top temperature due to these radiative property differences (sometimes referred to as a “radiometric temperature”), and we can then enhance areas that display these differences when they exceed a certain threshold.
Once we have isolated the low-cloud/fog layer in this way, we can introduce it as yet another layer in the vertical blending of GeoColor. In this case, we have chosen to place it in between the surface background and the standard infrared imagery. It makes physical sense to do so, since this is where low clouds exist in the atmosphere (below the high/thick clouds, and above the surface). Furthermore, since we have isolated this as a ‘low-cloud/fog’ layer, we can point it out as such in the GeoColor imagery by assigning it a color other than white/gray (which would have made it ambiguous with the high clouds). Figure 5 demonstrates the concept of introducing the low-cloud/fog layer to GeoColor, with a comparison of what information is lost if we do not.
The GeoColor technique provides a simple yet visually powerful mechanism for transitioning seamlessly between multiple sources of information both in the vertical and horizontal dimensions. Behind the scenes in the GeoColor algorithm itself, tunable scaling factors provide developers the flexibility to adjust the relative strength of transparency in both dimensions (i.e. providing control over the amount of information retained/lost during the blending operation). This technique results in dramatic improvement to the presentation quality of standard visible and infrared satellite imagery. The smooth transition from visible to infrared-detected clouds across the day/night terminator is superior to previous methods which either use infrared exclusively (to avoid the terminator problem) or invoke discrete cutoffs between the two kinds of imagery at a developer-defined location. Noteworthy in this regard is the image in Figure 1, which demonstrates a transition from infrared to visible imagery for frontal clouds extending from Arizona through Wisconsin (for clouds over the terminator, a blend of visible/infrared is used according to the product creation discussion above).
The main caveat users must always be aware of when using this product is that the backgrounds (ie, the Blue Marble and NGDC nighttime city lights) in the current GeoColor product are static. As such, almost all dynamics pertaining to the background will not be captured in this product. Unless some aspect of the change is captured in the real-time GOES visible/infrared observations, it will not be represented. For example, seasonal changes in vegetation, power outages, river plumes, variation of nighttime land brightness with lunar phase, etc, or impacts from a natural disaster, may not be represented in real-time imagery produced from this method. Examples of backgrounds that will be detected are snow cover and sea ice during the daytime, which will manifest as bright targets in the current GOES imagery. Although technically part of the ‘background’ since they are not atmospheric features, they will be revealed in the GeoColor imagery due to their high reflectance in the GOES daytime visible channel imagery. For an example of poor performance due to static backgrounds, the city lights of New Orleans appeared to shine brightly the night after Hurricane Katrina’s passage over the city.
When this product is available in the actual GOES-R era, the backgrounds will be available more often because of the instruments on the NPOESS satellites. Specifically, the backgrounds will be produced more often and at higher fidelity by the Visible/Infrared Imager/Radiometer Suite (VIIRS) sensors (which include the Day/Night Band for nighttime lights). More frequent updates will enable the capture of some of the dynamic background components that were mentioned previously—improving its representation of all components of the scene. As for the nighttime side of GeoColor, the GOES-R ABI provides ‘low light’ sensitivity, but the light levels in this case refer to twilight conditions as opposed to natural/artificial terrestrial light emissions and lunar reflection light levels several orders of magnitude fainter.
Finally, in discussing the example shown in Fig. 5, 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 channel 2-4 difference 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). There do exist techniques that attempt to distinguish between the two, and in principle this information could be introduced within GeoColor as yet another ‘layer’ of imagery. Other examples of additional layers that can be added are lofted dust, volcanic ash, smoke, and even snow-cover at night detected by way of moonlight reflection! Provided that there exists an algorithm capable of isolating these features, they can be converted into a distinct layer and incorporated as a unique piece of information within GeoColor. In this sense, GeoColor can be regarded as a platform for the simultaneous display of multiple pieces of information originating potentially from many different sources. Interaction with NWS users via the Proving Ground will assist GeoColor algorithm developers in determining which fields to include, the desired colors, scalings, etc., to best tailor this powerful application to the end-user needs.
Feedback Product | Feedback Date | User Office | Feedback Source | Feedback Text |
---|---|---|---|---|
GeoColor | 10/1/2011 | BOU | AFD(morning) | …. RGL GEO-COLOR IR SATELLITE CURVE INDICATES A THIN VEIL OF CIRRUS OVER THE AREA ATTM… |