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Infrared (IR) Imagery

Infrared camera systems (scanning radio-meters) permit the viewing of Earth’s weather in darkness as well as light. Infrared imagery and enhanced infrared imagery are created by a computer using the radiometer’s measurements of electromagnetic radiation emitted from Earth’s surface features and clouds. The computer converts the radiation readings to temperatures and assigns shades of gray based on a temperature-gray scale. In unenhanced imagery, the lower radiation measurements are synonymous with colder temperatures, while the higher readings relate to warmer temperatures. The gray shades assigned range from pure white (coldest) to black (warmest). An unenhanced IR picture is shown in figure 10-1-2. In terms of Earth and its cloud cover, the following features will usually appear in unenhanced IR imagery:

· Hot deserts—black

· Lakes and oceans—near black . Clouds with low tops (Cu, Sc, St)—dark gray

· Snow, ice, and clouds with medium tops (As, Sc)—light gray l Clouds with high tops (Cb, TCu, Ci)— white

Enhanced IR Imagery

An enhancement process is simply a modification of the temperature-gray scale in order to better differentiate features seen by the IR camera. The computers used with the Defense Meteorological Satellite (GOES) systems are programmed with many enhancement curves (temperature-gray scales). Figure 10-1-3 shows the difference between the unenhanced temperature curve (actually a straight line) and an enhanced

Figure 10-1-3.—Enhanced IR temperature curve compared to IR unenhanced temperature curve.

curve known as the ZA curve. You will notice the ZA curve modifies the shading assigned at the top and bottom of the temperature spectrum. The very warm temperatures in SEGMENT 1 (56.8°C to 29.3°C) are all shaded black, while the very cold temperatures in SEGMENT 5 (– 75.2°C and colder) are all pure white. SEGMENTS 2 and 4 are assigned a greater number of gray shades over smaller temperature ranges to increase the con-trast of features seen within these ranges. The finer definition in shading in these areas makes for easier recognition of significant cloud and surface features, such as cirrus and stratus decks, fog, haze, ocean current boundaries, and terrain features. SEGMENT 3 uses the same shading as that of the unenhanced curve, because the unenhanced curve does a good job over this range of temperatures. As you can see, the ZA curve only slightly modifies the unenhanced curve; therefore, IR pictures produced using this curve are interpreted in much the same manner as unenhanced IR pictures.

For a contrast to the slightly modified enhancement curve, you should look at an MB enhancement curve. See figure 10-1-4. Notice the degree of modification. This curve better defines areas of convective activity, and it is used to estimate rainfall amounts.

Figure 10-1-4.—MB enhancement curve.

Figure 10-1-5 is an example of the imagery pro-duced by the MB curve. This picture shows how the shading assigned by SEGMENTS 4 through 7 of the curve accentuate an area of convective activity.

As was discussed earlier, there are many enhancement curves used to produce GOES and DMSP (Defense Meteorological Satellite Pro-gram) imagery. For a full description of the GOES enhancement curves and examples of imagery pro-duced using these curves, you should refer to GOES User’s Guide. The DMSP enhancement curves are available to those ships and stations equipped with the AN/SMQ-11 satellite tracking system.

Infrared imagery (enhanced or unenhanced) provides far more information to meteorologists and oceanographers than its visual counterpart. However, a far superior degree of interpretation is possible when infrared and visual imagery are viewed together, since both exhibit strengths and weaknesses in differing conditions. A product of such comparisons is now incorporated in the GOES system. The computer combines unenhanced visible imagery with enhanced IR imagery to produce what is known as a COMPOSITE picture. Composite imagery combines the best

Figure 10-1-5.—EIR picture using MB temperature curve.

qualities of both visual and IR imagery. These pic-tures are primarily used in convective storm analysis and as a forecasting aid.

Learning Objective: State the effect of diurnal, latitudinal, and seasonal temper-ature variations on IR sensors and how to compensate for such variations.

EFFECTS OF NATURALLY OCCURRING TEMPERATURE VARIATIONS ON IR PICTURES

Large diurnal temperature variations over land surfaces produce quite a contrast in daytime and nighttime IR pictures. In the daytime, warm land appears darker in IR imagery and contrasts well with cool bodies of water, which appear gray, and with cold middle and high clouds, which appear white. As the land cools at night, its shading becomes much lighter in the IR image. The closer the land temperature to the temperature of water and/or clouds, the smaller the contrast between these features and the less obvious their boundaries. If land and water surfaces become the same temperature, the boundaries disappear. Seasonal and latitudinal temperature varia-tions are another cause of variation in the appearance of IR imagery. In the winter, especially at night, clouds over cold land are not easily detectable because they are often in the same temperature range as the cool land. On the other hand, in the tropics, there is always a large temperature contrast between the surface and middle and high clouds, so these clouds are easily recognizable. The effects of diurnal, latitudinal, and seasonal temperature variations can be compensated for, to a degree, by the use of IR enhancement curves.

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