Prioritize...
After reading this section, you should be able to describe what is displayed on infrared satellite imagery, and describe the connection between cloud-top temperature retrieved by satellite and cloud-top height. You should also be able to discuss the key assumption about vertical temperature variation in the atmosphere that meteorologists make when interpreting infrared imagery. Finally, it is important that you be able to differentiate an IR image from visible, water vapor, and radar imagery. This skill involves knowing what clues distinguish one type of imagery from another.
Read...
Visible satellite imagery is of great use to meteorologists, and for the most part, its interpretation is fairly intuitive. After all, the interpretation of visible imagery somewhat mimics what human eyes would see if they had a personal view of the earth from space. But, visible satellite imagery also has its limitations: it's not very useful at night, and it only tells us about how thick (or thin) clouds are.
By limiting our "vision" only to the visible part of the spectrum, we diminish our ability to describe the atmosphere accurately. Consider the images below. The image on the left shows a photo (which uses the visible portion of the spectrum) of a man holding a black plastic trash bag. On the right is an infrared (IR) image of that same man. Notice that switching to infrared radiation gives us more information (we can see his hands) than we had just using visible light. Furthermore, the fact that the shading in the infrared image is very different from the visible image suggests that perhaps we can gain different information from this new "look."
Before we delve into what we can learn from infrared satellite imagery, we need to discuss what an infrared satellite image is actually displaying. Just like visible images, infrared images are captured by a radiometer tuned to a specific wavelength. Returning to our atmospheric absorption chart, we see that between roughly 10 microns and 13 microns, there's very little absorption of infrared radiation by the atmosphere. In other words, infrared radiation at these wavelengths emitted by the earth's surface, or by other objects like clouds, gets transmitted to the satellite with very little absorption along the way.
You may recall from our previous lesson on radiation that the amount of radiation an object emits is tied to its temperature. Warmer objects emit more radiation than colder objects. So, using the mathematics behind the laws of radiation (namely Kirchhoff's Law and Planck's Law), computers can convert the amount of infrared radiation received by the satellite to a temperature (formally called a "brightness temperature" even though it has nothing to do with how bright an object looks to human eyes). Finally, these temperatures are converted to a shade of gray or white (or a color, as you're about to see), to create an infrared satellite image. Conventionally, lower temperatures are represented by brighter shades of gray and white, while higher temperatures are represented by darker shades of gray.
When you look at different visible satellite images, you will notice that they pretty much all look the same. Not so with infrared imagery (see the montage of images below). Some infrared images are colored to resemble visible images (upper-left), while others include all the colors of the rainbow! Such infrared images that contain different color schemes are usually called enhanced infrared images, not because they are better, but because the color scheme highlights some particular feature of the image (usually very low temperatures). There's really no fundamental difference between a "regular" (grayscale) infrared image and an enhanced infrared image; the coloring does not change the data it is presenting. The key with any IR image is to locate the temperature-color scale (usually on the side or bottom of the image) and match the shading to whatever feature you're looking at. Here are the uncropped images for the "traditional" IR image and lower-right "enhanced image". Do you see their temperature scales?
So, we know that an infrared radiometer aboard a satellite measures the intensity of radiation and converts it to a temperature, but what temperature are we measuring? Well, because atmospheric gases don't absorb much radiation between about 10 microns and 13 microns, infrared radiation at these wavelengths mostly gets a "free pass" through the clear air. This means that for a cloudless sky, we are simply seeing the temperature of the earth's surface. To see what I mean, check out this loop of infrared images of the Sahara Desert. Note the very dramatic changes in ground temperatures from night (light gray ground) to day (dark gray/black ground). This is because dramatic diurnal changes in ground temperatures often occur over the deserts, where the broiling sun bakes the earth's surface by day. At night, however, the desert floor often cools off rapidly after sunset. Want another example? Check out "Cold Frontal Passage" in the Case Study section below.
Of course, sometimes clouds block the satellite's view of the surface; so what's being displayed in cloudy areas? Well, while atmospheric gases absorb very little infrared radiation at these wavelengths (and thus emit very little by Kirchhoff's Law), that's not the case for liquid water and ice, which emit very efficiently at these wavelengths. Therefore, any clouds that are in the view of the satellite will be emitting infrared radiation consistent with their temperatures. Furthermore, infrared radiation emitted by the earth's surface is completely absorbed by the clouds above it. So, even though there is plenty of IR radiation coming from below the cloud and even from within the cloud itself, the only radiation that reaches the satellite is from the cloud top. Therefore, IR imagery is the display of either cloud-top temperatures or the Earth's surface temperature (if no clouds are present).
So, infrared imagery can tell us the temperature of the cloud tops, but how is that useful? Well, if we make the simple assumption that temperature decreases with increasing height in the lower atmosphere (that is, the troposphere), then we can equate cloud-top temperature to cloud-top heights. In other words, clouds with very cold tops are high-altitude cloud tops (for example: cirrostratus, cirrocumulus, cumulonimbus). Clouds (such as stratus, stratocumulus, or cumulus) with warmer tops have tops that reside at a low altitude.
Given that infrared imagery can tell us about the altitude of cloud tops, and visible imagery can tell us about the thickness of clouds, meteorologists use both types of images in tandem. Using them together makes for a powerful combination that helps to specifically identify types of clouds. Let's apply this quick summary to a real case so I can drive home this point using the short video below (2:39).
PRESENTER: Let’s use these side-by-side visible and infrared images to see how weather forecasters use both types of images to diagnose cloud types. Even though these images look pretty similar at first glance, they’re displaying very different things. Visible satellite imagery is most like what we see with our eyes. It’s based on the amount of visible light that gets reflected back to the satellite. But, it’s critical to realize that infrared imagery is different. It’s showing us temperature, either of cloud tops or the earth’s surface. Note that even though no temperature scale is shown on the infrared image, brighter shades of gray and white correspond to lower temperatures, as is typically the case.
Let’s start by looking at Point A, which is located in the line of bright white clouds extending from the Outer Banks of North Carolina down into Florida. Their brightness on visible imagery indicates that these are thick clouds. These clouds also appear bright on infrared imagery, so they have cold tops, indicating that the tops are high in the troposphere. Thus, given that these clouds are thick and have cold tops, we can assume that they are cumulonimbus, which can have tops reaching altitudes upwards of 60,000 feet.
Now let’s look at Point B, located in the area of "feathery" clouds over the Atlantic. Obviously, these feathery clouds are not as bright as the area of cumulonimbus on visible imagery, which means the clouds at Point B are much thinner. On the infrared image, these thin clouds appear bright white, meaning that they have cold tops, which are high in the troposphere. Therefore, they must be cirrus clouds, which are high and thin. I should add the caveat that sometimes when clouds have very thin spots, infrared radiation from the earth's surface can leak through holes in the clouds and reach the satellite. That bit of extra radiation from the warm earth can make the tops of very thin clouds appear a little warmer and lower than they really are.
Finally, let’s turn our attention to Point C, which is located in the region of clouds over the Great Lakes and upper Ohio Valley. The darker grayish appearance on infrared imagery tells us that they're low clouds with warm tops. These clouds are fairly bright on the visible image, meaning that they must be moderately thick. Given the somewhat "cellular" nature and breaks in between blobs of clouds, these are likely stratocumulus clouds, although farther north in the Great Lakes there's likely a more solid deck of stratus.
The lesson learned here is that both visible and infrared imagery can be used together to identify cloud types during the daytime.
While both visible and infrared imagery can be used together to identify cloud types during the daytime, at night, routine visible imagery is not feasible, so weather forecasters must rely almost exclusively on infrared imagery. Though infrared imagery is indispensable at night, it has some drawbacks. Detecting nighttime low clouds and fog can be tantamount to impossible because the radiating temperatures of the tops of low clouds and fog are often nearly the same as nearby ground where stratus clouds haven't formed.
To learn more about the shortcomings of IR images at night and to review what you've already learned in this section check out this short video (2:22) showing an infrared satellite simulator (video transcript). As the video demonstrates, in cases where our assumption about temperatures decreasing with increasing height breaks down, the appearance of infrared images might not be what we expect. By the way, I encourage you to give the infrared imagery simulator a try for yourself. You can experiment through different hypothetical situations to see how they might look on infrared imagery, which can help you see what factors can affect the appearance of infrared satellite images.
One of the scenarios shown in the video is something that you might encounter at night or early in the morning: The ground in cloud-free areas can sometimes actually be colder than the tops of nearby low clouds. We'll learn the reasoning behind this in the next lesson, but it can cause IR images to look a bit strange. Take a look at the image below, collected at 1131Z on a February morning. Focus your attention on the slightly darker patch over south central Texas (between stations K3R5 and KNIR). Is this region covered by clouds, or is it clear?
You might be tempted to make the assumption that the darker patch is warmer and thus must be the bare ground. But, check out the station model observations. The stations in the dark region show overcast skies or sky obscured by fog. In fact, the colder areas are the clear sky and the warmer region is covered by low clouds and fog. In the case above, there are several indicators that the ground might be colder than the low cloud tops. First of all, during the cold season, nighttime temperatures near the ground are often colder than overlying air (remember this image is from February). Secondly, the time of the image is 1131Z, right before sunrise. This is when the morning ground temperatures are their lowest, and is the most likely time for surrounding ground to be colder than nearby cloud low cloud tops.
The bottom line here is that you have to be careful when dealing with low clouds at night on an IR image. Just remember that you are looking at temperatures and that lighter gray doesn't necessarily mean cloudy skies. There are methods for detecting low clouds in such instances and they involve subtracting data collected at different IR wavelengths to extract only the low cloud field (if you're interested in seeing an example, check out the Explore Further section below).
This concludes our discussion of infrared satellite imagery. Now it's time to tackle water vapor imagery. But first, review the key points from this section, along with the Case Study below.
Infrared satellite imagery...
- is based on the fact that measuring an object's infrared emission tells you something about its temperature.
- displays the temperature of either cloud tops or the earth's surface (if the sky is clear).
- can be combined with the assumption that temperature decreases with increasing height to allow cloud-top heights to be determined. Colder cloud-tops (lower temperatures) mean higher clouds.
- is not able to give any direct indication of cloud thickness or the presence of precipitation (although inferences can be made in some cases).
- should not be confused with radar imagery. Inexperienced forecasters sometimes confuse enhanced infrared satellite images with similarly colored radar images. If you are uncertain, look at the color key (an infrared image will always have units of temperature).
Case Study...
Cold Frontal Passage
To drive home the point that infrared imagery shows us temperature, let's examine a case from January 17, 2012, when a strong cold front was knifing southward into northern Oklahoma and northern Texas. To see what I mean, compare the temperatures north of the cold front with temperatures south of the front on the 06Z analysis of isotherms. Even though it was 06Z (local midnight in this part of the country), forecasters could actually track the southward spread of cold air on infrared satellite imagery. On this loop of infrared satellite images from about 06Z to 1640Z (10:40 AM local time), you can see the southward progression of colder air.
What are we really seeing here? Well, skies were clear behind the front, so we're actually seeing the ground that became colder as chilly air arrived in the wake of the cold front. We're not looking at low clouds, fog, etc. Just to prove to you that there weren't many clouds associated with the passage of this cold front, check out the meteogram at Dallas, Texas. By the way, there were some clouds ahead of the front that you may have noticed on the IR loop. Most notably, there were some cumulonimbus clouds that developed over eastern Texas and moved eastward into Louisiana by the end of the loop (yellow and orange areas on the IR image, indicative of cloud-top temperatures as low as -60 degrees Celsius).
Explore Further...
As you learned in this section, one of infrared imagery's main advantages is that it's useful at night, but one of the challenges of interpreting IR images at night is that the tops of low clouds or fog can sometimes have similar temperatures as the surface of the earth in surrounding areas where it's not cloudy. In these situations, it can be difficult or impossible to pick out the areas of low clouds or fog with conventional infrared imagery, but differencing data at different infrared wavelengths can be help us with this problem. For an example, check out the short video below (2:30). If you're interested in learning more about the satellite product featured in this video, called the "Nighttime Microphysics RGB," check out this quick guide.
PRESENTER: Detection of low clouds and fog using infrared imagery can sometimes be tricky at night and early in the morning because one of the main assumptions that forecasters use when interpreting infrared images – that temperatures decrease with increasing height – isn’t always true.
Take this enhanced infrared image as an example. Assuming that temperatures decrease with increasing height might lead us to believe that this dark area has clear skies, meaning that the satellite is seeing emissions from the relatively warm ground, while the lighter shaded areas, which are colder, represent cloud cover.
But, that’s not the case at all. The brighter gray shaded areas actually have clear skies, and they appear colder on this enhanced infrared image because the ground is colder than the tops of the low clouds and fog in this area. For the record, these very brightly colored areas, actually do represent very cold cloud tops which are high in the troposphere.
Difficulty in discerning between low clouds or fog and clear skies on enhanced infrared imagery at night or early in the morning isn’t all that uncommon because the tops of low clouds can be warmer or have similar temperatures to the ground in surrounding areas with clear skies.
But, using multiple wavelengths of the electromagnetic spectrum gives forecasters another tool for more easily identifying low clouds or fog at night. This image was created by using multiple wavelengths from the infrared portion of the electromagnetic spectrum, differencing their contributions in order to better identify cloud thickness, composition, and temperature, and then applying different colors. Using this approach causes low clouds and fog to appear much more intuitively – we can see the area of low clouds across southeast Texas over into Louisiana and Arkansas in this whitish tan shading. The really high clouds to the northwest here now appear very dark, while the slice of cold ground in between appears pink.
Finally, once the sun rose on this particular day, traditional visible imagery confirmed our interpretation of the multi-channel approach – with a thick area of low clouds and fog, surrounded by clear skies. So, the multi-channel approach at night really made the interpretation of low clouds and fog much more intuitive compared to traditional infrared imagery.