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Section 1

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Introduction

Need for RGBs

Montage of single color enhancements and RGB products from NOAA and EUMETSAT satellites

The amount of imager data from the world's weather satellites is impressive and will increase dramatically when new geostationary and polar-orbiting satellites come online. But it poses a challenge: figuring out how to extract, distill, and package the data into products that are easy for forecasters to interpret and use.

8 RGB images from the DMSP OLS, Meteosat SEVIRI, and Metop AVHRR satellite imagers

"Red, Green, Blue" or RGB processing offers a simple yet powerful solution. It consolidates the information from different spectral channels into single products that provide more information than any one image can provide.

RGB products have long been used in research, education, and applied fields such as land management.

Enhanced Landsat image showing the oil spill in the Gulf of Mexico, 1 May 2010

For example, Landsat, an Earth resource satellite, has been observing land cover, vegetation, and water resources to help municipal planners and developers since the early 1970s. As the availability of RGB products continues to increase for a variety of environmental applications, including meteorological analysis, forecasters need information on what these products provide and how to integrate them into their operations.

Sample RGB

MODIS Fire RGB over Georgia (US)

While grayscale images are still useful, they often cannot match the effectiveness of RGB products. In fact, RGB products are often more useful than traditional single-channel color enhancement techniques.

Take this example-an EOS MODIS fire RGB product over the U.S. state of Georgia. (EOS stands for Earth Observing System, MODIS for Moderate Resolution Imaging Spectroradiometer.) It's easy to distinguish active fires in pink from smoke in blue. Recently burned areas appear dark magenta, and vegetated areas are green. The product was constructed by combining three channels at different wavelengths. Each channel contributes key pieces of information.

Vis 0.6 image showing smoke from fires over Georgia (US), 29 April 2007

The vivid depiction of smoke depends strongly on the Channel 1, 0.63-micrometer (µm) visible image.

Vis 0.8 image showing burn scar from fire in Georgia (US), 29 April 2007

The black burn scars from recently burned areas come from the longer wavelength Channel 2, 0.86 µm visible image.

Terra MODIS, Near IR 2.1 ch image showing fires over Georgia (US), 29 April 2007 1606 UTC

Information about hotspots from intense fires comes from the Channel 7, 2.1 µm near-infrared image.

Imagen MODIS RGB de incendios sobre Georgia (EE.UU.)

By assigning each of the three spectral channels to a different primary color and combining them into one product, we get far more information than any single channel could provide. Note that this MODIS product is referred to as a false color RGB.

RGB products like this are routinely used in fire monitoring even though the MODIS imager was originally intended as a non-operational research instrument.

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As more next generation polar-orbiting and geostationary weather satellites are launched, RGB products will be used routinely for a large number of applications, including fire monitoring. The Suomi-NPP satellite, launched in October 2011, marks the beginning of this new era from low earth orbit.

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From a geostationary perspective, the U.S.’s GOES-R satellites will complement the existing constellation of EUMETSAT’s Meteosat satellites. This will expand the coverage of RGB capabilities from Europe and Africa to the Americas and eastern Pacific. RGB products will also become more common across Asia and the Western Pacific, as countries across that region launch geostationary weather satellites with similar spectrally enhanced imagers over the next decade.

RGB Animations

GOES RGB over Hurricane Katrina, 29 Aug 2005 1645 UTC

This simple but useful GOES visible and infrared RGB product shows Hurricane Katrina making landfall over Mississippi. The visible channel depicts cloud cover while the infrared channel is used to indicate cloud height.

The yellow from the visible channel shows features where the overlying cirrus is absent or thin. For example, we can see lower-level features, such as clouds within the storm's eyewall region. The blue shows cirrus clouds at the periphery.

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Now look at this two-day loop of the hurricane. It uses the same RGB formula that we demonstrated earlier but substitutes the shortwave infrared channel for the visible channel during nighttime.

Notice how much information is available about clouds in the low-level environment during the daytime when the visible channel is available compared to nighttime when the infrared channels are used.

Since the infrared channel is present both day and night, we never lose sight of the storm's fundamental development.

This satellite product which covers the tropics is distributed by NESDIS in near real-time. The next generation GOES-R ABI (Advanced Baseline Imager) will have an expanded suite of 16 spectral channels. This will enable much improved RGB products for depictions of cloud cover that can include additional quantitative information about composition and evolution at higher spatial and temporal resolution.

RGB Products

This table shows some of the RGB products that are routinely produced from EUMETSAT's MSG (Meteosat Second Generation) SEVIRI imager, and Terra and Aqua MODIS imagers. Similar products are coming online with the Suomi NPP polar-orbiting satellite, and still more will be possible with future satellites including GOES-R and the Suomi NPP follow-on polar orbiters know as JPSS (or Joint Polar Satellite System).

Click each product to see a sample image.

Table describing some of the most widely used RGB products, with sample images for each type popping true color image popping natural and false image popping Vis IR RGB image popping nightime visible image popping air mass image popping cloud over snow image popping convection image popping dust image popping volcanic ash image popping day microphysis image popping fog and stratus image

Note that from this point on, we will refer to RGB products simply as RGBs.

RGB Applications

List of applications for which RGB products can be used with the corresponding products

This table presents the same information but from a different perspective, applications rather than products. You can see the purposes for which RGBs are used.

Using RGBs Operationally

These are some of the questions that forecasters often ask about using RGBs. Many of the answers will be elaborated on in other parts of the module.

Click each question for a brief response.

How easy is it to interpret RGBs?

RGBs are generally easier to use than single channel images and are often more effective at depicting important meteorological phenomena. Although the color schemes for RGBs are generally straightforward, it typically takes some training and experience to use them correctly. Some products are "intuitive," while others are not and can easily be misinterpreted.

Are RGBs available in real-time? How does one access them?

RGBs are increasingly available in real-time and near-real-time via the Internet. Efforts are underway to get RGBs into forecast offices.

Here are some commonly used RGB sites:

Who produces operational RGBs?

A number of meteorological services produce their own RGBs, following a set of best practice guidelines developed by the World Meteorological Organization. The guidelines are intended to standardize channel selection and color assignment for a common suite of products across international organizations. http://www.wmo.int/pages/prog/sat/documents/RGB-1_Final-Report.pdf

What are the benefits of creating your own RGBs?

It can be useful to make an RGB when you're in a unique forecasting situation for which no other products are available. However, you need to be aware of the challenges and pitfalls of making RGBs. In general, it is better to use standardized RGBs.

How do RGBs differ from single channel color enhancements and quantitative products?

RGB processing is one of a range of techniques developed to extract, emphasize, and optimize information from satellite imagery.

  • Grayscale images display imager information from single channels over a range of gray shades; products are made from 256 colors
  • Color displays of single channels are similar to grayscale images but the information is displayed using a set of assigned colors, rather than gray shades, to highlight specific features of interest, such as the colder cloud-top temperatures associated with deep convection; products are made from 256 colors
  • RGBs are generally made from three or more spectral channels or channel differences; each is assigned to one of the three primary colors, and the final product highlights specific feature(s); products are made from millions of colors
  • Classification products depict various non-quantitative classes of phenomena, such as cloud classifications (stratus, cirrus, and cumulus, etc.), using a color bar key
  • Quantitative products depict physical quantities, such as sea surface temperature and total precipitable water content, in various colors using a graded color bar; for more information, see the COMET module "Creating Meteorological Products from Satellite Data" at https://www.meted.ucar.edu/training_module.php?id=485

About the Module

graphic with 8 RGB images from the DMSP OLS, Meteosat SEVIRI, and Metop AVHRR satellite imagers

This module provides an overview of meteorological and environmental RGBs: how they are constructed and how to use them.

The RGB development process is described in the context of two RGBs: natural color and dust. This is followed by a discussion of the future of RGBs when most geostationary and polar-orbiting satellites will have far more channels than they do today.

The second half of the module, the "Applications" section, focuses on the use of RGBs and provides examples, interpretation exercises, and background information for many of the commonly used products.

The module is written for operational forecasters, meteorology and remote sensing students from the undergraduate level on up, scientists, and all others who rely on satellite products for environmental information.

About RGB Colors and Products

RGB Color Model, 1

Before exploring the RGB construction process, it's helpful to know a little about the RGB color model.

Various models are used to describe colors, with the RGB color model being the one used to produce the colors that we see in electronic devices such as televisions and computer monitors.

The RGB model has three primary colors: red, green, and blue. By combining them in various ways, we get a broad array of colors, from the secondary colors of yellow, magenta, and cyan, to the grays, black, and white. Understanding how these colors are made is important for producing and interpreting RGB products, so we'll take a few minutes to explore the process.

Dust RGB image with blocks of color for each primary and secondary color plus black and white displayed (this is used in an exercise) Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black

Click each colored box in the graphic. A graphic will display, with numbers indicating the contribution of red, green, and blue to the color. The numbers range from 0 to 255, representing the intensity of the color.

RGB Color Model, 2

Dust RGB image with blocks of color for each primary and secondary color plus black and white displayed (this is used in an exercise) Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black Box showing the contribution of red, green, and blue to creating the color black

A Simple Example

MODIS True Color Product with Hotspot Info Overlaid

Perhaps the best known RGB combination is the true color product. It highlights atmospheric and surface features that are hard to distinguish with single channel images alone and imitates how the human eye might see the scene. Among current weather satellites, true color products are available from the Terra and Aqua MODIS imagers and the Suomi NPP VIIRS imager, since both have the requisite visible channels. Additional RGB capabilities will come online as new imagers are launched. Some of these include:

  • The Visible Infrared Imaging Radiometer Suite (VIIRS) on U.S. JPSS (Joint Polar Satellite System) polar-orbiting satellites
  • The Flexible Combined Imager (FCI) on the Meteosat Third Generation (MTG) geostationary weather satellites
  • The Visible and Infra-Red Radiometer on China's FY-3 polar-orbiting satellites
  • The Advanced Himawari Imager on Japan's third generation geostationary weather satellites
MODIS True Color Enhancement Color, Conceptual Illustration

The true color RGB is constructed from the three visible wavelengths that correspond to the red, green and blue components of visible light. The first spectral channel is assigned to be red, the second channel green, and the third channel blue.

In the resulting RGB, it’s easy to distinguish the small smoke plume in Australia from the large area of blowing dust. The suspended dust particles take on a light brownish appearance because they reflect more light at the longer visible wavelengths, the red and green regions of the spectrum. Smoke from burning vegetation appears gray, reflecting the red, green, and blue components of visible light in relatively equal amounts. In general, clouds are easily separated from suspended dust in true-color images.

The small group of red pixels at the western end of the smoke plume indicates hot spots or fires. These were inserted after the channel compositing took place, using information from the thermally sensitive shortwave infrared channels on MODIS. Later we will discuss the 'natural color' product, which has some similarities to true color as well as some important differences.

Creating RGBs: A More In-Depth Look

Natural Color RGB

The Process of Building RGBs

graphic with 8 RGB images from the DMSP OLS, Meteosat SEVIRI, and Metop AVHRR satellite imagers

The process of building RGBs is part science and part art, part random exploration and part methodical experimentation. Over time, a set of best practices has been established to guide the development process. While some good RGBs arise from random experimentation, your best chance of making a useful product will result from following an established procedure. Even if you never experiment with RGB compositing, being aware of the process will help you better understand and interpret the products.

The process of building an RGB has five steps.

  • Step 1: Determine the purpose of the product
  • Step 2: Based on experience or scientific information, select three appropriate channels or channel derivatives (such as an inter-channel difference) that provide useful information for the product
  • Step 3: Pre-process the images as needed to ensure that they provide or emphasize the most useful information
  • Step 4: Assign the three spectral channels or channel derivatives to the three RGB color components
  • Step 5: Review the resulting product for appearance and effectiveness

We will examine the steps on the following pages, applying them to build two products: the natural color RGB and the dust RGB.

Step 1: Determine the Purpose of the Product

MSG dust RGB showing the volcanic ash cloud from the Eyjafjallajoekull eruption moving from northern Finland to the UK

A good RGB should convey information that would be difficult or time consuming to assess from one or more individual satellite images. To the extent possible, the product should be unambiguous and use intuitive colors to help highlight important meteorological and surface features.

Drawing of different types of land surfaces, from desert to mountains to water

Let's say, for example, that we want to develop a product that emphasizes features such as topography, vegetation, low clouds, and snow cover. We want it to cover Europe using a geostationary satellite with looping capability. This means that we will use EUMETSAT's Meteosat Second Generation (MSG) satellite imager called SEVIRI (Spinning Enhanced Visible and InfraRed Imager). True color images are not possible since the instrument does not carry blue and green visible channels. Therefore we will build a similar product that EUMETSAT scientists call the 'natural color' RGB.

Step 2: Select Appropriate Channels or Channel Derivatives

Move your mouse over each channel for a description.

Now that we've identified the purpose of the product, we can select the spectral channels that are sensitive to the features that we want to highlight.

The MSG SEVIRI imager has twelve channels, more than are currently on other operational geostationary satellites. MSG also offers the best preview of the upcoming GOES-R satellite that carries the 16-channel ABI imager.

To begin, we'll take a quick tour through the MSG toolbox so you know the channels that can be combined into an RGB. Move your mouse over each channel for a description.

Chart of the 12 Meteosat channels • A solar channel since it reflects solar radiation<br/><br/>• Compared to other channels, the atmosphere is relatively transparent at solar wavelengths, producing a good view of surface features • A solar channel since it reflects solar radiation<br/><br/>• Compared to other channels, the atmosphere is relatively transparent at solar wavelengths, producing a good view of surface features • A solar channel since it reflects solar radiation<br/><br/>• Compared to other channels, the atmosphere is relatively transparent at solar wavelengths, producing a good view of surface features • A shortwave IR channel<br/><br/>• For the most part, it acts like a longwave IR channel, measuring emitted terrestrial and atmospheric radiation<br/><br/>• However, during daytime, it also reflects solar energy <br/><br/>• Has special abilities to detect fire and fog A longwave IR channel sensitive to water vapor in the atmosphere A longwave IR channel sensitive to water vapor in the atmosphere A longwave IR window channel capable of seeing low-level cloud and moisture features A longwave IR channel that highlights ozone detection A longwave IR window channel capable of seeing low-level cloud and moisture features A longwave IR window channel capable of seeing low-level cloud and moisture features A longwave IR channel that highlights carbon dioxide detection A high resolution visible channel with 1-km spatial resolution used for detailed close-up views of specific regions of interest

Selecting the Actual Channels

Now we're ready to select the channels for our product. Of the combinations below, which would produce the 'best natural' RGB product, one that emphasizes features such as topography, vegetation, and snow cover? (Choose the best answer.)

Note that for readability, we've removed the micrometer symbols following each channel's wavelength.

The correct answer is D.

The 0.6 µm Vis, 0.8 µm Vis, and 1.6 µm NIR (Near InfraRed) channels represent solar wavelengths that are effective for characterizing terrain and land cover features. Most of the other channels sense at shortwave, midwave, and longwave infrared wavelengths, only some of which can be used to detect surface features.

Step 3: Pre-Process the Images as Needed

3 EUMETSAT images (Vis0.6, Vis0.8, and NearIR 1.6) before processing for input into an RGB

Before combining the images, we may need to pre-process them for visual sharpness or to bring out vital information in a more prominent way. Pre-processing can, for example, transform an input image with a "washed out" appearance to one with high contrast.

In our case, no pre-preprocessing is needed since the images have a high enough brightness contrast. But when we build the dust RGB later, you'll see how this step can be more complex.

Step 4: Assign Colors

Schematic depicting satellite channel responses to key atmospheric and surface features

To assign the spectral channels to the right primary colors, we need to know how each channel responds to key atmospheric and surface features. From this generalized schematic, we can see that the relative amount of reflected radiation in the three solar channels varies depending on the features observed. The relative degree of reflectivity is about equal over some features, such as the ocean. But there are sharp differences over others, such as ice clouds. We exploit these differences when we match a channel with one of the three RGB colors.

Reflectances of different land/atmospheric features in the VIS0.6, VIS0.8, and NearIR 1.6 channels, each is placed in one of the 3 RGB color guns (red, green, blue)

Here is a summary of the relative reflectivities in our hypothetical landscape.

  • Bare land, especially when dry, is strongly reflective in the 1.6 µm near-infrared channel
  • Vegetated surfaces are strongly reflective in the 0.8 µm visible channel
  • Water phase clouds have about the same reflectivity in all three channels
  • For ice phase clouds and snow cover, reflectivity is strong for 0.6 µm Vis and 0.8 µm Vis channels, but weak for 1.6 µm NIR channel since ice crystals reflect poorly at that wavelength
  • Ocean water is poorly reflective in all three channels

Selecting the Best RGB Combination

Let's see how we use this information to build the natural color RGB. Each of the three input channels could be assigned to any of the three colors, which means there are six possible combinations for building the natural color RGB. All of the combinations contain the same information since they are based on the same three input channels. But they have radically different color schemes.

Which combination produces the most natural looking product, one in which vegetation is green, deserts are brownish, and low clouds are white? View the products by clicking the six View RGB product links. Then select the most natural looking product by clicking the radio button beside it and clicking Done. Click here to review RGB color theory.

The correct answer is F.

MSG RGB 321 15 Jan 06

With its white low clouds, brownish desert, and green vegetation, combination #6 is the most natural-looking option. We'll discuss this RGB more on the next page.

Additional Information

MSG RGB 321 15 Jan 06

Here is some additional information about the natural color RGB.

Bare land (including desert) is brownish red, representing the very strong contribution from the 1.6 µm near-infrared channel in red and the weaker contribution from the 0.8 µm visible channel in green. There is little contribution from the 0.6 µm visible channel in blue.

Vegetation, including much of the land over Europe, is highly reflective in the 0.8 µm visible channel, which produces the green vegetative shading in the product.

Water phase clouds are very reflective in all three channels and combine to produce white water phase clouds.

You probably noticed that snow cover is cyan. That’s because snow is highly reflective in the 0.8 µm and 0.6 µm visible channels. When assigned to be green and blue, the colors combine to produce cyan. Although this is unnatural in appearance, non-intuitive colors are common in RGBs and easy to interpret if you know the color scheme.

Ice clouds are also cyan.

Finally, water is dark because of the minimal reflection and hence contribution from all three channels.

What would happen if one of the less than optimal combinations was used for the natural color RGB? You would get used to it, and learn to interpret it correctly. But the goal is to create products that provide useful information, and communicate quickly to forecasters.

Dealing With Ambiguities

MODIS false color RGB over California, 24 Dec 2009 with cirrus, low clouds and fog, and snow covered labeled

A nearly identical RGB is available from Terra and Aqua MODIS data. It looks similar to EUMETSAT’s natural color RGB but uses the 2.2 µm NIR channel in place of the 1.6 µm NIR channel. The MODIS false color product has the same color interpretation scheme and is used to identify the same features. However, it does a better job of detecting fires. MODIS false color products are available in near real-time.

In this winter example centered on Northern California, we can differentiate the cyan cirrus cloud near the coast from the whitish water cloud trapped in valleys over Oregon. But it’s hard to distinguish cirrus cloud from snow cover over the mountains based on color alone. Both features are cyan because ice crystals reflect strongly in the visible channels (which have been assigned to be green and blue) and poorly in the near-infrared channel (which has been assigned to be red).

Ambiguous situations like this can often be resolved in various ways, for example by:

  • Seeing if there is another RGB developed for the situation; in this case, it would be useful to check the Cloud Over Snow RGB, described in the Applications section.
  • Looping images to differentiate surface from atmospheric features.
  • Noticing that surface features, such as snow cover, are often tied to familiar topographic features, such as mountain ranges, while atmospheric features, such as ice clouds, are typically not. Do you see that effect in our example?

Dust RGB

Step 1: Determine the Purpose of the Product

Natural color RGB over Northern Africa showing a dust storm

This natural color RGB provides a vivid depiction of northern Africa. However, it does not show the major dust front depicted by the arrows very well. That's because the input channels, 0.6 µm Vis, 0.8 µm Vis, and 1.6 µm NIR, and the resulting RGB fail to show dust in adequate contrast against the surface of the Earth. Therefore, we need a separate dust RGB for observing airborne dust. As you'll see, this is a more complex RGB than those discussed earlier.

Step 2: Select Appropriate Channels or Channel Derivatives

Here are the solar, water vapor, and longwave infrared groups of MSG channels for this daytime dust storm over northern Africa.

Chart of the MSG visible, water vapor, and longwave IR channels that can be used for creating the dust RGB.

Which channel group best depicts the boundary of the advancing dust front? See the area within the ovals in the 0.6 µm Vis and 10.6 µm IR images. (Choose the best answer.)

The correct answer is C.

The longwave channels (8.7 µm infrared and longer wavelengths) do the best job since dust contrasts with the thermal background, highlighting the dust front distinctly.

The solar channels are not the best choice here since the reflective dust tends to blend in with the bright desert background.

The water vapor channels cannot detect dust and land features since they do not see down to the surface boundary layer where the dust often resides.

Step 3: Pre-Process the Images as Needed

MSG IR 10.8 µm image  03 Mar 2004 1212 UTC before being stretched in the process of creating a dust RGB product

Before combining these channels into an RGB, the input images need to be processed to better highlight features of interest.

The first image, the 10.8 µm infrared channel, needs what is called contrast stretching.

Conceptual depiction of IR dust detection by contrast with the surface during the daytime

To understand why, consider how the channel detects dust layers aloft. The radiating temperature of the surface is greater than that of the dust aloft, therefore the dust stands out against the hotter background.

Conceptual depiction of IR dust detection by contrast with the surface at night

But the contrast is often limited. For example, at night, the temperatures of the dust and background surface are similar.

Enhancement table with a portion stretched for use in creating a dust RGB

To make the most out of the limited contrast and really highlight the dust features, we stretch the temperatures within a relatively narrow temperature range, as shown in the chart. The warm cutoff is 289 Kelvin (the white in the image), and the cold cutoff is 261 Kelvin (the black in the image).

MSG IR 10.8 µm image  03 Mar 2004 1212 UTC after being stretched in the process of creating a dust RGB product

The resulting image usually enhances the dust signature and provides useful information when combined with the other inputs into the RGB.

Second Input: A Difference Image

2 images differenced to create an input to a dust RGB [12.0 - 10.8]

In addition to building RGBs from single channel inputs, we can also use difference images, where the calibrated pixel brightness temperature values of one image are subtracted from those in another image. For dust imaging, these differences often bring out the dust signature that cannot be observed easily on single channel images.

Therefore, we will use the 12.0 µm IR minus 10.8 µm IR brightness temperature difference (BTD) for our second input.

Depiction of land with a dust layer vs. cirrus cloud above and arrows depicting absorption differences between the 10.8 vs. 8.7 micrometer channels

The effectiveness of the BTD stems from the interaction of upwelling energy from the surface of the Earth with the dust cloud. Infrared energy passing through a dust layer has a colder brightness temperature at 10.8 µm than 12.0 µm because dust is more sensitive to and absorbs more energy at 10.8 µm. In effect, dust blocks more upwelling radiation from reaching the satellite at this wavelength.

This differential sensitivity of dust leads to a positive brightness temperature difference and bright shades in imagery.

Conversely, cirrus clouds are less sensitive to energy at 10.8 µm than 12.0 µm, which produces a negative difference and black shades on images. This straightforward channel difference provides a powerful way of differentiating higher clouds from dust.

Enhancement table with a portion stretched for use in creating a dust RGB

By differencing the 12.0 µm IR and 10.8 µm IR channels and scaling the difference from -4 to +2 Kelvin, we get a sharp depiction of the dust clouds on the brightness temperature difference image, which is perfect for input into the RGB. Notice how the difference image shows the dust cloud in white.

2 images differenced to create an input to a dust RGB [12.0 - 10.8]

Third Input: Another Difference Image

2 images differenced to create an input to a dust RGB [10.8 - 8.7]

For our third input to the RGB, we'll use another brightness temperature difference, 10.8 µm IR minus 8.7 µm IR channels.

Depiction of land with a dust layer vs. cirrus cloud above and arrows depicting absorption differences between the 10.8 vs. 8.7 micrometer channels

Both ice clouds and dust have negative brightness temperature differences in the 10.8 µm IR minus 8.7 µm IR channel difference, making it hard to tell them apart on the resulting image.

differenced image for input into dust RGB [10.8 - 8.7]

But the channel difference, which we scale from 0 to 15 degrees, effectively distinguishes dust clouds in black from desert (sand) surfaces, which provides vital additional information to the RGB.

Step 4: Assign Colors

Dust RGB with its 3 input images in different color guns; the inputs are: IR12.0-10.8, IR10.8-8.7, and IR10.8

The best assignment of spectral channels to colors for this RGB is:

  • Red for the 12.0 µm IR minus 10.8 µm IR difference image
  • Green for the 10.8 µm IR minus 8.7 µm IR image
  • Blue for the 10.8 µm IR image

In the resulting RGB:

  • Magenta, pink, and orange mark dust
  • Reds mark thick, cirrus cloud
  • Dark blue marks thin cirrus cloud
  • Orange and brown mark water cloud
  • The background appears in various shades of blue

Step 5: Review the Final Product

Dust RGB with color information for interpreting it

What are the dark line segments in the white box northwest of Morocco? (Choose the best answer.)

The correct answer is C.

These are contrails comprised of thin cirrus. The detection is based on one of the inputs (the 12.0 µm IR minus 10.8 µm IR brightness temperature difference), which enhances thin cirrus. This is an unexpected side benefit of the dust RGB.

Using RGBs in Different Situations

So far, we have seen three ways of viewing a scene that contains dust:

  • Single satellite images, such as longwave infrared images, where detecting dust depends on the thermal contrast between dust and the surface background
  • Channel differences (or BTDs, Brightness Channel Differences), which enhance the depiction of dust plumes
  • An RGB that combines the inputs from the first two options into something that is easy to interpret

The real test of an RGB is whether it can perform in varied conditions. In an operational setting, RGBs are often ‘tuned’ to account for seasonal and geographical differences, as well as different satellite viewing geometries, such as high versus low latitude views from a geostationary satellite.

Click each tab and see how well the dust product does.

Two RGBs, one dust, the other natural, depicting the same scene

This dust RGB animation shows how the dust storm evolves over four days and nights. You can tell when the sun is up since the heated land appears in warmer, bluish colors. Pinks and yellows predominate during the night and then fade to a bluish color typical of land during the day. Thick, high clouds are dark red while thin, high clouds are dark blue to black in appearance. Low cloud features, commonly water clouds, are shades of orange. Dust appears as magenta.

Dust RGB animation showing the evolution of a dust storm over several days over Saudi Arabia

Notice the strong system pushing through the Persian Gulf midday through the period followed by a major dust outbreak. At what time does the dust outbreak reach the southern shore of the Saudi Peninsula? (Choose the best answer.)

The correct answer is B.

Notice how the dust pushes offshore after it reaches the coast.

Dust RGB animation showing the evolution of a dust storm over several days over Saudi Arabia

This dust RGB animation occurs over the Atlantic Ocean for nearly one week. Which of the following are evident? (Choose all that apply.)

All three choices are correct.

Several dust plumes have arisen from specific source regions over Africa. This dust moves over the ocean and eventually reaches the Americas.

Dust is usually found at middle and low levels of the atmosphere and is often obscured by higher clouds on satellite products.

Note that this dust RGB can be useful for tropical cyclone forecasting over the Atlantic Ocean. This is because dust and the dry air that contains it tend to dampen storm strength.

Dust RGB over northern Africa overlaid with 1000-hpa winds and contours of divergence

RGBs do not by themselves provide quantitative information. But we can get this kind of information by overlaying derived satellite products or model data for example.

In this example, the RGB provides the location of the dust front while the model overlays provide information about the winds associated with that front and the airmass behind it.

Advantages & Limitations of RGBs

Advantages

Having examined several RGBs, the benefits of using them should be clear.

  • They combine different channels to highlight atmospheric and surface features that are harder to distinguish with single channel images alone; each channel usually represents a particular wavelength although channel combinations or differences can also be used.
  • RGB processing can use channels throughout the spectrum, from the visible and infrared to the passive microwave; for this reason, RGBs are often called ‘multispectral;’ they combine information from different wavelength regions of the electromagnetic spectrum.
  • RGB technology produces intuitive, realistic-looking products that can reduce ambiguities and simplify interpretation, making them useful for a wide range of users.
  • RGBs can be overlaid with quantitative information such as NWP output, radar, and synoptic observations, enabling far more sophisticated analysis and interpretation.
  • A new generation of satellite imaging instruments is coming online; incorporating more spectral channels, helping improve RGBs, and offering new options for users to view and analyze a variety of features and complex processes and interactions.

Limitations

Although RGBs are extremely useful, there are some limitations to be aware of. These are addressed in the questions below. Answer each question, clicking Done to move on to the next one.

RGBs eliminate interpretation ambiguities. (Choose the best answer.)

MSG Natural Color RGB with arrows pointing to areas with snow cover and high clouds

The correct answer is False.

RGBs reduce ambiguities, but they do not always eliminate them. Just consider the high clouds at point C and the snow cover at point A in this natural color RGB; both are cyan. This highlights the importance of having either good interpretation skills, ancillary information, or a different product altogether! However, the RGB product is still better than single channel images. For example, it enables us to distinguish high clouds and snow cover (A and C) from low clouds (B).

While RGBs are designed to help identify specific features, they do not by themselves provide quantitative information, such as cloud droplet size or snow depth. (Choose the best answer.)

Comparison of a natural color RGB and a classification product for the same period

The correct answer is True.

Although RGBs come with color interpretation guidelines, in general, you will not see color bars or legends on them. That's because they are intended for general interpretation and do not convey quantitative information or objective classifications. In contrast, classification products are derived products that classify each pixel into various classes. In this example, each of the 21 cloud or surface types has its own color. Unlike RGBs, classification schemes can be validated against ground truth and judged based on their performance. Take a minute to compare the natural color RGB with the companion classification product.

Anticipating the Next Generation Satellites

Spectral Channels

Chart showing the 16 ABI spectral bands and their uses

The imagers on board Suomi NPP, the upcoming GOES-R, and the future JPSS polar-orbiting satellites, have many more spectral channels than their predecessors. This will enable the development of improved as well as new RGBs, helping to satisfy forecasters’ needs for more concise, value-added information.

The GOES-R ABI imager will have five more channels compared to the MSG SEVIRI, allowing for an expanded suite of products. The polar-orbiting MODIS imager has these bands, which has allowed us to preview VIIRS and GOES-R ABI capabilities. Unlike MODIS however, ABI will produce animations of RGB imagery over the United States and most of the Western Hemisphere at frequent intervals between 30 seconds and 15 minutes.

VIIRS Capabilities

Table listing the channels on the VIIRS, OLS, and MODIS Imagers

With 36 imaging channels, MODIS was designed as a research and development imager. The operational VIIRS imager on board the Suomi NPP and future JPSS polar orbiters provides similar capabilities with 22 channels that represent 20 wavelengths. While that's fewer than MODIS, VIIRS has one channel from the DMSP OLS heritage imager that MODIS does not have, a day-night channel (also known as the Day Night Band) that can produce images at night when there's sufficient light from the moon or other sources.

before after

 

These two images show the improvements we are getting with VIIRS. With full moonlight, it's easy to see snow cover and low clouds over the mountainous terrain of northeastern Afghanistan.  High-level cirrus cloud is also visible in shades of light blue.

VIIRS Day Night Band

DMSP OLS Vis and IR images from 11 April 2004 0220 UTC

This DMSP OLS visible image, taken on a moonless night, shows many city lights in South Carolina, Georgia, Alabama, and portions of Mississippi. But most of Louisiana and Texas are dark, with the exception of the Houston and Dallas Fort Worth areas.

The infrared image shows that the Texan city lights are obscured by thunderstorm cloud cover. They don't appear on the visible image since there is no illumination from moonlight at this time.

DMSP Vis IR RGB showing thick overcast over Texas and western Louisiana

Combining the two images into an RGB eliminates the need to interpret the visible and infrared images separately. It clearly shows the thick cloud cover over Texas and western Louisiana, which obscured the cities.

Recall that the DMSP OLS imager has only two channels, visible and longwave infrared. The RGB is made by assigning the visible channel to be red and the infrared channel to be both blue and green. This results in clouds that are cyan and cities that are red. With the new VIIRS imager, we are now able to combine the Day Night Band with potentially 21 other channels, resulting in many new opportunities for multispectral viewing.

To learn more about the VIIRS Day Night Band and its applications, see the COMET module "Advances in Space-Based Nighttime Visible Observation" at https://www.meted.ucar.edu/training_module.php?id=990

RGB Applications

Overview

This section describes many of the applications for which RGBs are used. The section is arranged by product, with examples, interpretation exercises, and background information for each one. Use the tabs to review the introductory tables, and then select the products that you want to learn more about from the menu.

Note that each application has two pages of information accessible via tabs at the top of the page (About and Examples/Exercises). When you reach the bottom of the first page, be sure to scroll up and select the second tab, rather than clicking the Next Button. The Next/Previous buttons move you between RGB applications.

List of applications for which RGB products can be used with the corresponding products

This section describes many of the applications for which RGBs are used.

Table of RGB applications and products, each with a description, satellite(s) involved, and use (day/night/both)

True Color

Table describing some of the most widely used RGB products, with a sample image for the true color RGB

Description:

True color images are derived by combining three solar wavelengths, all in the wavelength range of human vision. This makes for very realistic images, with colors that imitate how the human eye might see the scene.

Currently (as of 2013), only polar-orbiting satellites have the needed channels to produce true color images. The instruments include the MODIS imagers on board the EOS-Terra and Aqua satellites, the VIIRS imager on board the Suomi NPP satellite launched in October 2011, and the MERSI imagers on the Chinese FY-3 satellites. These satellites give us a preview for similar products from the upcoming U.S. JPSS polar-orbiting satellites and other planned international missions including the Japanese Himawari and EUMETSAT Meteosat Third Generation (MTG) geostationary satellites.

Coverage: Daytime only, requires solar reflectance information

Channels: Three solar wavelengths available on MODIS, Suomi NPP VIIRS, Chinese FY-3 MERSI; future JPSS VIIRS, Advanced Himawari Imager (AHI), and MTG (Meteosat Third Generation) FCI (Flexible Combined Imager)

  • Red (0.640 µm on MODIS, 0.672 µm on Suomi NPP & future JPSS VIIRS)
  • Green (0.555 µm on MODIS and VIIRS)
  • Blue (0.488 µm on MODIS and VIIRS)

Color scheme:

  • Vegetated areas are green
  • Deserts are brown
  • Clouds are white
  • Water is blue

Advantages:

  • Produces a result similar to color photography
  • Easy to interpret
  • Particularly useful for viewing land surfaces for geological and land-use analysis
  • Provides compelling views of smoke and dust storms

Limitations:

  • Daytime only
  • No microphysical information for clouds
  • At present (2013), only produced by two MODIS imagers and one VIIRS imager (on board Suomi NPP polar orbiter)

Live data links:

References:

  • Miller, S. D., J. D. Hawkins, J. Kent, F. J. Turk, T. F. Lee, A. P. Kuciauskas, K. Richardson, R. Wade, and C. Hoffman, 2006: NexSat: Previewing NPOESS/VIIRS imagery capabilities. Bull. Amer. Meteor. Soc, 87, 433-446.
  • Hillger, D. H., T. Kopp, T. Lee, D. Lindsey, C. Seaman, S. Miller, J. Solbrig, S. Kidder, S. Bachmeier, T. Jasmin, and T. Rink, in press: First-light imagery from Suomi NPP VIIRS. Bull. Amer. Meteor. Soc.

Examples:

MODIS True Color RGB over So. California in Oct 2007, shows smoke from fires

This MODIS true color product shows southern California in October 2007. Coastal regions, which are often green, are as brown as the deserts. The smoke from fires (bluish white) and the dust (brownish white) have been blown offshore by fierce offshore winds.

MODIS True Color RGB over Nebraska, Kansas, and Oklahoma, 23 Aug 2009

This true color image was produced during a relatively cool, wet summer over the Great Plains of the United States, with the green areas over Oklahoma, Kansas, and Nebraska representing dense crops. The 100th meridian traditionally marks a boundary, with greener, wetter conditions to the east, and browner, drier conditions to the west.

Natural Color

Table describing some of the most widely used RGB products, with a sample image for the natural color RGB

Description:

This RGB is similar to true color in that it depicts surface and atmospheric features, such as vegetated areas, deserts, clouds, and ocean. But it is made from satellites without the requisite solar channels for true color, using a combination of visible and near-infrared channels instead. Many of the features have natural looking colors but some do not. For example, snow is cyan.

Currently (as of 2013), several polar-orbiting and one geostationary weather satellite have the needed channels to produce true a Natural Color RGB. Additional satellites, especially geostationary, will be coming online over the next decade with the necessary channels to allow for near global coverage. These include the upcoming U.S. JPSS polar-orbiting satellites, and the GOES-R, Japanese Himawari, Chinese FY-4, and EUMETSAT Meteosat Third Generation (MTG) geostationary satellites.

Coverage: Daytime only, requires solar reflectance information

Channels (red, green, blue):

  • Polar-orbiting satellites:
    • Terra and Aqua MODIS:
      0.645 µm Vis; 0.856 µm NIR; 1.64 µm SWIR
    • NOAA and Metop AVHRR, FY-3 MERSI, Suomi NPP & future JPSS VIIRS:
      0.64 µm Vis; 0.865 µm NIR; 1.61 µm SWIR
  • Geostationary satellites:
    • MSG SEVIRI:
      0.6 µm Vis; 0.8 µm NIR; 1.6 µm SWIR
    • Future GOES-R ABI, Advanced Himawari Imager (AHI), FY-4 AGRI, and MTG FCI:
      0.64 µm Vis; 0.865 µm NIR; 2.25 µm NIR

Color scheme:

  • Low clouds are white
  • Vegetation is green
  • Deserts are reddish brown
  • Snow cover and high ice clouds are cyan

For each of the three channels, a solar zenith angle correction is recommended to make the images brighter and enhance the contrast for low sun elevations present at high latitudes and during mornings and evenings.

Advantages:

  • Does a good job of depicting many surface and near-surface features
  • Provides an intuitive and compelling view of the Earth

Limitations:

  • High ice clouds and snow cover are both cyan, making them hard to differentiate
  • Thin cirrus clouds are difficult to detect

Live data links:

Loop:

Animation of a global MSG natural product RGB, 20 April 2009, 0600

This loop shows the day night terminator move across the scene, illustrating the product’s daytime-only use. The RGB is particularly useful for showing surface type, such as vegetation (green) and desert (reddish brown), and flooded areas (black).


Example:

MSG Natural Color RGB  3 Oct 2005 with an eclipse over the Mediterranean, 3 Oct 2005

Since this RGB is based solely on solar channels, we see a large shadow area over the western Mediterranean Sea where a solar eclipse is centered. Notice that high clouds are cyan; low clouds are white; vegetation is green; and bare land and desert are brownish red.


Exercise:

MSG Natural Color RGB Overlaid with ECMWF Wind Analysis 250 hPa  1 Jan 2008 1200 UTC

The cyan streak over North Africa is oriented parallel to the wind direction indicated by the green wind barbs plotted for the 250 hPa level. What is the feature and why does it align so well with the winds? (Choose the best answer.)

The correct answer is A.

The cyan in this RGB can represent either high ice clouds or snow cover. Since snow cover is extremely unlikely in this part of Africa, we must be seeing cirrus clouds.

False Color

Example of a false color RGB

Description:

This false color RGB, developed from MODIS data, looks similar to EUMETSAT’s natural color RGB. Interpretation is very similar for most purposes. The MODIS false color product does a better job at detecting fires due to the inclusion of a near-infrared channel that is sensitive to intense fires. The RGB can also be made from MSG imagery (without the fire capability), from VIIRS imagery on board Suomi NPP and JPSS satellites, from imagery on board the FY-3 satellites, and will be possible using the next generation GOES-R ABI, Advanced Himawari Imager (AHI), FY-4 AGRI and MTG FCI imagers.

Coverage: Daytime only, requires solar reflectance information

Channels (red, green, blue):

  • Polar-orbiting satellites:
    • Terra and Aqua MODIS:
      0.63 µm Vis; 0.86 µm NIR; 2.1 µm SWIR
    • Suomi NPP and future JPSS VIIRS:
      0.64 µm Vis; 0.865 µm NIR; 2.25 µm SWIR
    • FY-3 MERSI:
      0.65 µm Vis; 0.865 µm NIR; 2.13 µm SWIR
  • Geostationary satellites:
    • Future GOES-R ABI, Advanced Himawari Imager (AHI), and MTG FCI:
      0.64 µm Vis; 0.865 µm NIR; 2.25 µm SWIR
    • Future FY-4 AGRI:
      0.65 µm Vis; 0.825 µm NIR; 2.25 µm SWIR

Color scheme:

  • Low clouds are white
  • Vegetation is green
  • Deserts are reddish brown
  • Snow cover and high ice clouds are cyan
  • Intense fire are orange or pink
  • Burn scars are orange or brown

Advantages:

  • Two out of the three MODIS input channels have 0.25 km spatial resolution, which provides very detailed views
  • VIIRS on Suomi NPP and future JPSS satellites has all three input channels at a relatively high 0.37 km spatial resolution for providing detailed views
  • ABI on future GOES-R satellites has all three input channels at refresh rates from 15 minutes across the full Earth disk to as frequent as 30 seconds over small domains for high impact weather and other environmental events
  • Product is similar to the MSG Natural Color product but can also identify fire hot spot signatures
  • Provides an intuitive view for terrain classification that makes it easy to interpret surface features

Limitations:

  • High ice clouds, deep convective clouds (e.g. thunderstorms), and snow cover appear as cyan, making them hard to differentiate
  • Thin cirrus clouds are difficult to detect

Live data links:

Example:

MODIS false color RGB over Southern California, 15 Mar 2010 with burn scars and mountain snow, and irrigated desert agriculture pointed out

This March scene over southern California shows springtime green over the coastal regions against desert tans and browns over the interior deserts. The intensive agriculture of the California’s Central Valley makes the desert green south of the Salton Sea. A burn scar from the previous summer’s Station Fire in orange contrasts vividly with snow on the nearby peaks of the San Gabriel Mountains.


Loop:

Animation of a global MSG natural product RGB, 20 April 2009, 0600

This sequence of daily 250-meter resolution MODIS false color images shows the rapidly increasing size of the burn scar associated with the Station Fire from 28 August to 07 September 2009.

The fire burn scar is the large darker red feature, which grows very quickly to the north and east on 30 to 31 August.

The hottest actively burning fires appear as smaller clusters of pink to white along the periphery of the burn scar.

Thick smoke partially obscures the burn scar area on 01 September, while large pyrocumulus clouds form over the eastern portion of the fire activity on 02 September.

As of the morning of 08 September, the Station Fire had burned over 160,000 acres, making it the largest fire in Los Angeles County history and the ninth largest fire in California history.

Visible & Infrared

Table describing some of the most widely used RGB products, with a sample image for the Vis IR RGB

Description:

This product helps to distinguish between high and low clouds and can help reveal wind shear. It is very simple and easy to understand. Note that the spectral channels and color scheme are the same as those used for the nighttime visible RGB.

The product can be produced for any meteorological satellite since all carry the minimum requirement of one visible and one longwave IR channel, and most also carry at least one shortwave IR channel within the 3.5 to 4.0 µm spectral region to replace the Vis channel during nighttime.

Coverage: Daytime only, although some who generate 24-hour product loops insert shortwave infrared imagery in place of the visible channel at night for continuity.

Channels:

  • Polar-orbiting and geostationay satellites:
    • Daytime:
      • Red & Green: GOES 0.6 µm Vis
      • Blue: GOES 10.8 µm IR
    • Nightime:
      • Red & Green: GOES 3.9 µm IR
      • Blue: GOES 10.8 µm IR

Color scheme:

  • White indicates thick, cold ice clouds
  • Light blue indicates cold terrain or cold, thin ice clouds (cirrus)
  • Subdued yellow or green often indicates land
  • Dark blue shows water
  • Brighter yellow shades indicate low clouds or fog

Advantages:

  • Uses the traditional visible and longwave infrared window channels that forecasters are familiar with, combining them in an optimal way to distinguish higher/colder clouds from lower/warmer clouds
  • Is found on NOAA-NESDIS Web pages, making it accessible and useful for comparison to other imagery

Limitations:

  • Since it only uses two channels, it is only a pseudo-RGB product and it cannot distinguish between some features of interest, such as snow cover vs. cloud, or water cloud vs. ice cloud
  • Does not incorporate a water vapor channel and therefore does not show water vapor plumes

Data links:

Example:

GOES Vis and IR RGB for 15 Apr 2007 showing an intense Northeaster over New England

This GOES-East (GOES-12) Vis and IR RGB shows an intense Northeaster storm over New England on April 15, 2007. High cirrus clouds appear in light blue, tracing the circulation aloft. The yellow shades highlight low-level clouds, including mountain induced wave clouds over the Virginias. Wind gusts near the time of the image are overlaid in black.


Exercise:

GOES RGB animation of Hurricane Katrina, 28 Aug 2005 1745 to 29 Aug 2005 0215 UTC

In this GOES loop over the Gulf of Mexico, the RGB during the daytime is increasingly dominated by the brightness temperature signal from longwave infrared imagery as nighttime approaches.

At the beginning of the loop, the blue at the edges of the storm marks the first arrival of thin cirrus over the Gulf Coast states. Before the sun goes down, we see yellow near the storm center. What does the yellow signify? (Choose the best answer.)

The correct answer is C.

The yellow comes mainly from highly reflective cloud water droplets detected by the visible sensor. Water clouds make up the eyewall region that surrounds the storm’s center.

Nighttime Visible

Table describing some of the most widely used RGB products, with a sample image for the nighttime visible RGB

Description:

The Defense Meteorological Satellite Program (DMSP) has long had a nighttime visible observing capability with its Operational Linescan System (OLS) sensor. OLS has made it possible to see nighttime features such as fires, lights, and the aurora. Low clouds and snow cover can also be detected when there’s sufficient moonlight.

The launch of the Suomi NPP satellite in October 2011 marked the beginning of significantly improved nighttime visible imaging with the VIIRS Day Night Band, the successor to the OLS nighttime visible channel. The Day Night Band’s higher spatial resolution and greater sensitivity are providing opportunities for new products and applications. Future JPSS polar orbiters will have this same imaging capability.

Coverage: Nighttime only

Channels: Since the OLS has only two channels (visible and infrared), the RGB is constructed using these channels at night. The channels and color scheme are the same as the GOES daytime product. Interpretation is also the same when there is moonlight: low clouds are yellow, high clouds are blue.

  • Polar-orbiting satellites:
    • DMSP OLS:
      Nighttime Vis (red and green); longwave IR (blue)
    • VIIRS:
      Day Night Band Vis (red and green); I5 (11.45 µm) or M15 (10.763 µm) IR (blue)
  • Geostationary satellites: A nighttime Vis channel is currently not available on GEO satellites

Color scheme: With sufficient moonlight

  • Low (warm) clouds and snow cover are yellow
  • High (cold) clouds are blue
  • Thick, high (cold) clouds are white
  • Cities and fires are yellow

Advantages: Unlike nighttime longwave IR images, this product makes it possible to view features, such as low clouds and snow cover at night; it also shows city lights and fires, and occasionally shows lightning from thunderstorms with high lightning flash rates.

Limitations:

  • Features, such as low clouds and snow cover, are only illuminated when there is sufficient moonlight
  • The quality of the legacy DMSP OLS sensor is poor, but significant improvements have come online with the VIIRS imager on board Suomi NPP; VIIRS is also planned for future NOAA JPSS polar orbiters

Data links:

More information:

References:

Lee, T. E., S. D. Miller, F. J. Turk, C. Schueler, R. Julian, S. Deyo, P. Dills, and S. Wang, 2006: The NPOESS VIIRS Day/Night visible sensor. Bull. Amer. Meteor. Soc., 87, 191-199.

Hillger, D. H., T. Kopp, T. Lee, D. Lindsey, C. Seaman, S. Miller, J. Solbrig, S. Kidder, S. Bachmeier, T. Jasmin, and T. Rink, in press: First-light imagery from Suomi NPP VIIRS. Bull. Amer. Meteor. Soc.

Example:

DMSP/OLS 11.0 um IR window 19 Sept 2002 0250 UTC showing Tropical Storm Iselle

As this image of Tropical Storm Iselle shows, it’s hard to see low clouds at night with longwave infrared imagery alone. High clouds are in red. But where is the low-level center, a feature that’s critical to identify when locating tropical storms?

DMSP/OLS Nighttime Vis RGB 19 Sept 02 0250 UTC 19 Sept 2002 0250 UTC showing Tropical Storm Iselle

In the absence of a shortwave infrared channel, the nighttime visible RGB can detect low-cloud features that are illuminated by moonlight. We can now see that the center is far displaced from the one inferred from the infrared image alone.


Exercise:

Air Mass

Table describing some of the most widely used RGB products, with a sample image for the airmass RGB

Description:

The Air Mass RGB is designed and tuned for monitoring the evolution of cyclones, in particular, rapid cyclogenesis, jet streaks, and potential vorticity (PV) anomalies. Since the product relies heavily on infrared channels in the water vapor and ozone absorption regions of the spectrum, it provides information primarily about the middle and upper levels of the troposphere, not so much the lower levels and near-surface conditions.

Current imagers with the spectral channels needed to produce the Air Mass RGB include Meteosat SEVIRI and Terra and Aqua MODIS.  The future GOES-R ABI and Advanced Himawai Imager (AHI) will also have the required WV and IR channels to produce an Air Mass RGB that is nearly identical to today’s Meteosat product, but at a higher spatial and temporal resolution.

Coverage: Day and nighttime

Channels:

  • Polar-orbiting satellites:
    • MODIS:
      Red: 6.715 µm WV 7.325 µm WV BT difference
      Green: 9.73 µm IR 11.03 µm IR BT diffierence
      Blue: 6.715 µm WV BT
  • Geostationary satellites:
    • MSG SEVIRI and future MTG FCI:
      Red: 6.2 µm WV 7.3 µm WV BT difference
      Green: 9.7 µm IR 10.8 µm IR BT diffierence
      Blue: 6.2 µm WV BT
    • Future GOES-R ABI and Advanced Himawari Imager (AHI):
      Red: 6.19 µm WV 7.34 µm WV BT difference
      Green: 9.61 µm IR 10.35 µm IR BT diffierence
      Blue: 6.19 µm WV BT

Color scheme:

  • Ozone-poor tropical air masses are green
  • Ozone-rich polar air masses are blue
  • Dry air masses in the upper troposphere (such as those related to sub-tropical high pressure systems, PV anomalies, jet streaks, and deformation zones) are red to orange
  • High-level clouds are white
  • Mid-level clouds are brown
  • Magenta often appears at the edge of the full disk (due to limb darkening/cooling effect) and should be disregarded

Advantages:

  • Can see important boundaries between air masses, such as tropical and polar, at a glance; these are often invisible on single channel images
  • Helps detect the position of jet streams and areas of dry, descending stratospheric air with high PV; these appear in red
  • Can detect features commonly seen in water vapor images, such as deformation zones, wave features, and PV anomalies
  • The infrared channels make it possible to monitor cloud development at low, middle, and high altitudes

Limitations:

  • Air masses are only detectable in areas free of high cloud cover
  • Tends to depict conditions in the middle and upper troposphere, but not at the surface
  • At the edge of the Earth’s disk, air masses can have a magenta color but this does not represent true air mass characteristics, rather limb darkening/cooling due to the large satellite viewing angles

Live data links:

More information:

Example:

Airmass RGB over southern Europe and northern Africa from 7 - 8 July 2005

Loop: The polar front, marked by the clouds of several moving frontal systems, divides the scene into polar air to the north and subtropical air to the south. The bright red area to the north of the polar front may indicate stratospheric intrusion into the troposphere. Brown, cloud-free air masses to the southeast of the polar front mark dry air masses at middle and upper levels.


Exercise: This image shows a series of midlatitude waves moving across Europe.

MET9 Airmass RGB  10 Mar 2008  0700 UTB, with several countries labeled

Where is the strongest intrusion of dry stratospheric air down into the troposphere? (Choose the best answer.)

The correct answer is D.

The area of intense red (very dry air) over Ireland marks the intrusion of stratospheric air.

Cloud Over Snow

Table describing some of the most widely used RGB products, with a sample image for the cloud over snow RGB

Description:

This RGB, the Cloud / Snow Discriminator product, distinguishes clouds from snow cover, something that is hard to do in most visible images. The product is useful mostly in winter and over mountain ranges.

The current NOAA and Metop AVHRR, Terra and Aqua MODIS, Suomi NPP VIIRS, FY-3 MERSI, and MSG SEVIRI imagers have the needed spectral channels for producing the Cloud / Snow Discriminator product.  The future GOES-R ABI, MTG FCI, and FY-4 AGRI imagers will also have the required visible and near-infrared channels to produce a similar product with the benefit of routine 5 to 15 minute image updates for the full Earth disk.

Coverage: Daytime only, requires solar reflectance information

Channels:

  • Polar-orbiting satellites:
    • NOAA and Metop AVHRR, FY-3 MERSI:
      0.63 µm Vis; 0.865 µm NIR; 1.61 µm SWIR
    • Terra and Aqua MODIS:
      0.645 µm Vis; 0.86 µm NIR; 1.64 µm SWIR (Terra); 2.13 µm SWIR (Aqua)
    • Suomi NPP and future JPSS VIIRS
      0.64 µm Vis; 0.865 µm NIR; 1.61 µm SWIR
  • Geostationary satellites:
    • MSG SEVIRI and future MTG FCI:
      0.64 µm Vis; 0.81 µm NIR; 1.6 µm SWIR
    • Future GOES-R ABI:
      0.64 µm Vis; 0.865 µm NIR; 1.38 µm SWIR (cirrus detection); 1.61 µm SWIR
    • Future FY-4 AGRI:
      0.65 µm Vis; 0.825 µm NIR; 1.375 µm SWIR (cirrus detection); 1.61 µm SWIR

Color scheme:

  • Snow cover is bluish white
  • Clouds are shades of yellow
  • Cloud free land is darker green

Advantages:

  • Simplifies interpretation during winter and over mountainous terrain
  • Avoids the ambiguity between snow cover and clouds seen in visible and true color images

Limitations:

  • Sometimes features do not appear in the expected colors; snow cover can look like cloud and vice versa
  • Can not discriminate between high and low clouds
  • Old snow cover can look like cloud-free land
  • Product works best for fresh snow that’s less than a few days old
  • Does poorly in detection of patchy snow cover or trace amounts

Live data links:

More information:

Reference:

Miller, S. D, T. F. Lee, and R. L. Fennimore, 2005: Satellite-Based imagery techniques for daytime cloud/snow delineation from MODIS. J. Appl. Meteor., 44, 987-997.

Example:

Terra MODIS True color RGB over the Great Lakes Region (U.S), 03 December 2007

In this true color product, we cannot differentiate between snow cover and cloud. It’s particularly difficult to distinguish lake-effect cloud from snow on the ground.

Terra MODIS Cloud Over Snow RGB over the Great Lakes Region (U.S), 03 December 2007

With bluish-white snow cover and yellow clouds, this RGB minimizes the ambiguity.


Exercise:

MODIS Visimage, 4 April 2007

Convection

Table describing some of the most widely used RGB products, with a sample image for the convection RGB

Description:

This RGB can identify important microphysical characteristics and trends in convection, including small ice particles that point to intense updrafts and are potential indicators of imminent severe weather.

The MODIS and MSG SEVIRI imagers have the necessary channels to make this product.  With the future GOES-R ABI, 1-minute or 30-second imaging during severe weather will give forecasters unprecedented views of convective development across the contiguous U.S. Future Chinese FY-4 and Japanese Himawari geostationary satellites will also be able to provide this product.

Coverage: Daytime only, requires solar reflectance information

Channels:

  • Polar-orbiting satellites:
    • MODIS:
      Red: 6.7 minus 7.3 µm WV BT difference
      Green: 3.9 minus 11.0 µm IR BT difference
      Blue: 1.64 minus 0.645 µm reflectance difference
  • Geostationary satellites:
    • MSG SEVIRI and future MTG FCI:
      Red:6.2 minus 7.3 µm WV BT difference
      Green: 3.9 minus 10.8 µm IR BT difference
      Blue: 1.6 minus 0.64 µm reflectance difference
    • Future GOES-R ABI:
      Red: 6.19 minus 7.34 µm WV BT difference
      Green: 3.9 minus 10.35 µm IR BT difference
      Blue: 1.61 minus 0.64 µm reflectance difference
    • Future Himawari AHI:
      Red: 6.25 minus 7.35 µm WV BT difference
      Green: 3.85 minus 10.45 µm IR BT difference
      Blue: 1.61 minus 0.645 µm reflectance difference
    • Future FY-4 AGRI:
      Red: 6.25 minus 7.1 µm WV BT difference
      Green: 3.75 minus 10.7 µm IR BT difference
      Blue: 1.61 minus 0.64 µm reflectance difference

Color scheme:

  • The background is dark blue and magenta
  • High-level, thick, ice clouds, including convective cumulonimbus clouds, appear red
  • Yellow is usually indicative of small ice particles within convective cloud tops, but may also be associated with elevated updrafts such as in high altitude orographic wave clouds

Advantages:

  • Compared to many satellite images, this RGB highlights the youngest and most intense cells, showing overshooting thunderstorm tops, which can help distinguish new convection from dissipating convective activity.

Limitations:

  • Daytime only, requires solar reflectance information
  • Not effective for observing or discriminating types of weather other than convection
  • Yellow is indicative of small ice particles, which can be associated with either strong convection or in some cases thick high level ice clouds such as found with orographic wave clouds

Live data links:

Additional information:

References:

Heymsfield, A. J., L. M. Miloshevich, C. Schmitt, A. Bansemer, C. Twohy, M. R. Poellot, A. Fridlind, and H. Gerber, 2005: Homogeneous ice nucleation in subtropical and tropical convection and its influence on cirrus anvil microphysics. J. Atmos. Sci., 62, 4164.

Rosenfeld, D., W. L. Woodley, T. W. Krauss, and V. Makitov, 2006: Aircraft microphysical documentation from cloud base to anvils of hailstorm feeder clouds in Argentina. J. Appl. Meteorol., 45, 12611281.

Rosenfeld, D., W. L. Woodley, A. Lerner, G. Kelman, and D. T. Lindsey, 2008: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. J. Geophys. Res., 113, 22 p.

Example 1:

Animation of MSG Convection RGB showing Hurricane Isabel, June 2006

Loop: In this RGB animation of Hurricane Isabel, the reds and oranges show cloud tops composed of fairly large ice particles. The yellow suggests very small ice particles at high altitudes, which is indicative of intense updrafts.


MSG Severe Weather RGB, South of France & Italy  20 May 2003 1330 UTC

Example 2: This RGB over southern France and Italy shows strong convection in yellow, an indicator of potential severe weather. Here again, the yellow areas indicate cells with very small ice particles at cloud top, suggesting overshooting tops, intense updrafts and potential severe weather. The more reddish regions indicate larger ice particles associated with older or more benign, less threatening convection.


Exercise:

MSG Convection RGB 27 Oct 2004 1300

In this convection RGB over South Africa, three of the five circled areas could be associated with severe weather? The other two are not. Choose the circled areas that indicate a potential for severe thunderstorm conditions. (Choose all that apply.)

The correct answers are A, B, and C.

The yellow in these areas usually indicates small particles within convective cloud tops that are associated with strong updrafts. Strong updrafts often indicate the potential for severe thunderstorm conditions. The red areas are usually seen with non-severe convection and other cloud types.

Dust

Table describing some of the most widely used RGB products, with a sample image for the dust RGB

Description:

Based on infrared channel data, this RGB is designed to monitor the evolution of dust storms during both day and night. This is challenging because the appearance of dust changes radically from day to night. Note that the dust RGB is nearly identical to the ash RGB but has slightly different tuning (temperature difference thresholds and enhancement of individual red, green, and blue inputs are slightly modified).

The current MODIS, MSG SEVIRI, and Suomi NPP imagers have the necessary channels to make this product. Future JPSS VIIRS, FY-3 MERSI-2, GOES-R ABI, Himawari AHI, FY-4 AGRI, and MTG FCI instruments will also have the needed channels for producing a dust RGB.

Coverage: Both day and nighttime

Channels:

  • Polar-orbiting satellites:
    • MODIS:
      Red: 12.0 minus 11.0 µm IR BT difference
      Green: 11.0 minus 8.6 µm IR BT difference
      Blue: 11.0 µm IR
    • Suomi NPP and future JPSS VIIRS:
      Red: 12.0 minus 10.8 µm IR BT difference
      Green: 10.8 minus 8.6 µm IR BT difference
      Blue: 10.8 µm IR
    • FY-3 MERSI-2:
      Red: 12.0 minus 10.8 µm IR BT difference
      Green: 10.8 minus 8.55 µm IR BT difference
      Blue: 10.8 µm IR
  • Geostationary satellites:
    • MSG SEVIRI and future MTG FCI:
      Red: 12.0 minus 10.8 µm IR BT difference
      Green: 10.8 minus 8.7 µm IR BT difference
      Blue: 10.8 µm IR
    • Future GOES-R ABI:
      Red: 12.3 minus 10.35 or 11.2 µm IR BT difference
      Green: 10.35 or 11.2 minus 8.5 µm IR BT difference
      Blue: 10.35 or 11.2 µm IR
    • Future Himawari AHI:
      Red: 12.35 minus 10.45 or 11.2 µm IR BT difference
      Green: 10.45 or 11.2 minus 8.60 µm IR BT difference
      Blue: 10.45 or 11.2 µm IR
    • Future FY-4 AGRI:
      Red: 12.0 minus 10.7 µm IR BT difference
      Green: 10.7 minus 8.5 µm IR BT difference
      Blue: 10.7 µm IR

Color scheme::

  • The color of dust varies, from red for very high-level dust (quite rare), to bright magenta for low-level dust during daytime, to dark magenta for low-level dust at night
  • Thick, high-level clouds are red
  • Thin, high-level clouds are dark blue or black, except in sandy areas where they may appear in shades of are green and yellow
  • Thick, middle-level clouds appear brown
  • Thin, middle-level clouds appear green
  • Low clouds appear pink when the atmosphere is warm and olive green when the atmosphere is cold
  • Moist low levels, particularly a moist boundary layer, appear in bluish shades
  • Land and water backgrounds appear in shades of green and blue

Advantages:

  • Can follow the evolution of dust plumes during both day and night
  • Can depict dust plumes over land and water surfaces

Limitations:

  • The lack of solar channels can impede the detection of dust plumes, especially over the ocean; however, high-level dust clouds are always easy to detect given the large thermal contrast between elevated dust and the underlying surface
  • It is almost always easier to detect low-level dust clouds during the day when there is a larger thermal contrast between the land and elevated dust; this thermal contrast is smaller at night, making it more difficult to detect low-level dust with satellite products at night

Live data links:

Additional information:

Reference:

Lensky I. M. and D. Rosenfeld, 2008: Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT). Atmos. Chem. Phys., 8, 6739-6753.

Example 1:

Dust RGB animation showing the evolution of a dust storm over several days over Saudi Arabia

Loop: This animation shows how dust (in brighter magenta) can be easy to detect, especially during daytime, and more difficult to detect at night. Higher clouds appear deep red or black but can also appear in shades of yellow and green over sandy areas. Notice the tremendous dust storm moving through Iraq and the Persian Gulf near the start of the loop.


MSG Dust RGB  03 Mar 2004 0112 UTC showing dust at night

Example 2: Notice how the dust RGB identifies dust at night, something that most dust enhancements fail to do. The keys for this product are the channel differences, which help identify dust regardless of the time of day.


Exercise:

MSG-1 Dust RGB 10-09, 09-07, 0925  June 2003, 1000 UTC

In this daytime dust RGB, notice how dust looks over water (below the black arrow) vs. land (beside the white arrows). Over which surface is dust easier to detect? (Choose the best answer.)

The correct answer is B.

Infrared RGBs will always show dust better over a heated land surface than over bodies of water, which tend to radiate at a similar temperature to the dust.

Volcanic Ash

Table describing some of the most widely used RGB products, with a sample image for the volcanic ash RGB

Description:

Using infrared channel data, this RGB detects ash, sulphur dioxide, and ice crystals from volcanic eruptions and can be used to track plumes over long distances downstream of an eruption site. The product helps forecasters track volcanic effluents and the information is used to provide warnings to aviation authorities and emergency managers. Note that the ash RGB is nearly identical to the dust RGB but has slightly different tuning (temperature difference thresholds and enhancement of individual red, green, blue inputs are slightly modified).

The current MODIS, MSG SEVIRI, and Suomi NPP imagers have the necessary channels to make this product. Future JPSS VIIRS, FY-3 MERSI-2, GOES-R ABI, Himawari AHI, FY-4 AGRI, and MTG FCI instruments will also have the needed channels for producing a day-night volcanic ash RGB.

Coverage: Both day and nighttime

Channels:

  • Polar-orbiting satellites:
    • MODIS:
      Red: 12.0 minus 11.0 µm IR BT difference
      Green: 11.0 minus 8.55 µm IR BT difference
      Blue: 11.0 µm IR
    • Suomi NPP and future JPSS VIIRS:
      Red: 12.0 minus 10.8 µm IR BT difference
      Green: 10.8 minus 8.55 µm IR BT difference
      Blue: 10.8 µm IR
  • Geostationary satellites:
    • MSG SEVIRI and future MTG FCI:
      Red: 12.0 minus 10.8 µm IR BT difference
      Green: 10.8 minus 8.7 µm IR BT difference
      Blue: 10.8 µm IR
    • Future GOES-R ABI:
      Red: 12.3 minus 10.35 or 11.2 µm IR BT difference
      Green: 10.35 or 11.2 minus 8.5 µm IR BT difference
      Blue: 10.35 or 11.2 µm IR
    • Future Himawari AHI:
      Red: 12.35 minus 10.45 or 11.2 µm IR BT difference
      Green: 10.45 or 11.2 minus 8.60 µm IR BT difference
      Blue: 10.45 or 11.2 µm IR
    • Future FY-4 AGRI:
      Red: 12.0 minus 10.7 µm IR BT difference
      Green: 10.7 minus 8.5 µm IR BT difference
      Blue: 10.7 µm IR

Color interpretation:

  • Sulphur dioxide cloud is aqua-green (lower and middle latitudes) and yellow (at higher latitudes and for larger viewing angles near the edge of the full Earth disk)
  • Depending on the height, temperature and particle size, ash goes from being bright red and pink (when it is very cold) to magenta (when it is warm) to yellow (when it is composed of very small ash particles)
  • Thin cirrus appears black or dark blue
  • High thick clouds and thunderstorms appear brown, with shades of orange and red for clouds composed of smaller ice particles
  • Middle and lower clouds may appear in lighter shades of brown, blue, and green (at higher latitudes and for larger viewing angles near the edge of the full Earth disk)
  • Blowing dust may appear as magenta
  • Moist low levels, particularly the boundary layer, appear in bluish shades
  • Surface features appear in lighter shades of blue, green, and dull magenta

Advantages:

  • Shows the three major volcanic effluents (ash, sulfur dioxide, and ice crystals) in distinct colors, enabling users to observe effluents drifting from the site of an eruption

Limitations:

  • Some everyday features can be mistaken for volcanic effluents
  • Black cirrus can be a part of either volcanic or non-volcanic cloud systems
  • Green clouds can resemble sulfur dioxide (especially noticeable at higher latitudes and for larger viewing angles near the edge of the full Earth disk)
  • Limited detection of ash and sulfur dioxide when present with ice particles (mixed volcanic clouds)

Live data links:

Additional information:

References:

Caseadevall, T. J., 1994: Volcanic Ash and Aviation Safety: Proceedings of the First International Symposium on Volcanic Ash and Aviation Safety. U.S. Geological Survey Bulletin, 2047.

Prata, A. J., 1989: Observations of volcanic ash clouds in the 10-12 µm window using AVHRR/2 data. Int. J. Remote Sensing, 10 (4 and 5), 751-761.

Example:

MSG RGB Ash Product from 24-25 Nov 2005 showing an eruption near Madagascar

Loop: This animation shows the volcanic eruption of Mount Karthala, with sulfur dioxide in a bright aqua-green color, volcanic ash in a bright red and magenta, and thin cirrus in black. The volcanic ash and cirrus appear first, followed by the sulfur dioxide. Notice how the volcanic effluents suppress the deep convection in brown over northern Madagascar. We see the effects of wind shear during the entire loop. Low-level clouds are moving toward the west while high-level effluents are moving toward the east. This lets us infer that sulfur dioxide is at low levels because it moves very slowly, unlike the ash and cirrus that are advected by stronger winds at upper tropospheric levels.


Exercise:

MSG RGB Ash Product from 24-25 Nov 2005 showing an eruption near Madagascar

In this nighttime scene taken from the animation, what effluents are apparent in the vicinity of Mouth Karthala? (Choose all that apply.)

All of the choices are correct.

Sulfur dioxide is bright green, volcanic ash is bright magenta, and cirrus cloud is black.

Day Microphysics

Table describing some of the most widely used RGB products, with a sample image for the day microphysics RGB

Description:

This RGB is useful for cloud analysis (for example cloud identification, type, and phase), monitoring convection, fog, and fires.

  • The visible reflectance in red approximates the cloud optical depth and amount of cloud water and ice
  • The 3.9 µm shortwave infrared solar reflectance in green gives a qualitative measure for cloud particle size and phase
  • The 10.8 µm infrared brightness temperature produces blue shading as a function of surface and cloud top temperatures (the warmer the surface, the greater the blue contribution); therefore warmer land and ocean surfaces appear in shades of blue whereas colder cloud tops have less blue input and appear more orange and red

Coverage: Daytime only, requires solar reflectance information

Channels:

  • Polar-orbiting satellites:
    • MODIS:
      Red: 0.86 µm NIR reflectance
      Green: 3.8 µm SWIR (reflected solar component only)
      Blue: 11.0 µm IR
    • NOAA and Metop AVHRR:
      Red: 0.865 µm NIR reflectance
      Green: 3.74 µm SWIR(reflected solar component only)
      Blue: 10.8 µm IR
    • Suomi NPP and future JPSS VIIRS:
      Red: 0.865 µm NIR reflectance
      Green: 3.74 µm SWIR (reflected solar component only)
      Blue: 10.8 µm IR
    • FY-3 imagers:
      Red: 0.865 µm NIR reflectance
      Green: 3.74 µm SWIR (reflected solar component only)
      Blue: 10.8 µm IR
  • Geostationary satellites:
    • MSG SEVIRI and future MTG FCI:
      Red: 0.8 µm NIR reflectance
      Green: 3.9 µm SWIR (reflected solar component only)
      Blue: 10.8 µm IR
    • Future GOES-R ABI:
      Red: 0.865 µm NIR reflectance
      Green: 3.90 µm SWIR (reflected solar component only)
      Blue: 10.35 or 11.2 µm IR
    • Future Himawari AHI:
      Red: 0.860 µm NIR reflectance
      Green: 3.85 µm SWIR (reflected solar component only)
      Blue: 10.45 or 11.2 µm IR
    • Future FY-4 AGRI:
      Red: 0.825 µm NIR reflectance
      Green: 3.75 µm SWIR (reflected solar component only)
      Blue: 10.7 µm IR

Color scheme:

  • The surface appears in shades of blue
  • Low clouds appear yellow to greenish (small droplets) to magenta (large droplets)
  • High ice clouds appear deep red (large ice particles) to bright orange (small ice particles)

Advantages:

  • Can clearly distinguish between ice phase clouds at high elevations and water phase clouds at lower elevations, providing a pseudo three-dimensional view of the atmosphere
  • Can identify subtle microphysical variations within clouds that are not apparent on other images or RGBs
  • Helps discriminate between precipitating and non-precipitating water clouds
  • Can help identify severe convective clouds with strong updrafts (see also the ‘Convection’ RGB)

Limitations:

  • The RGB is complicated in terms of the number and variety of colors and requires expertise to interpret it but it is a very powerful product
  • Only available during daytime

Live data links:

Additional information:

References:

  • Rosenfeld, D. and I. M. Lensky, 2008: Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT). Atmos. Chem. Phys., 8, 6739-6753. http://www.atmos-chem-phys.net/8/6739/2008/acp-8-6739-2008.pdf
  • Rosenfeld, D. and I. M. Lensky, 1998: Satellite-based insights into precipitation formation processes in continental and maritime clouds. Bull. Amer. Meteor. Soc., 79, 2457-2476.

Example 1:

MSG Daytime Microphysics RGB animation, 14 Aug 2003 1200 to 415

Loop: This loop shows convection (in orange) erupting over northern Italy. The outflow boundary emanating from it appears in greenish yellow.


Example 2:

Daytime Microphysics RGB based on Rosenfeld and Lensky over southern Europe and northern Africa, 17 Jan 2006 1312 UTC

This daytime microphysics RGB shows a variety of important microphysical cloud features. Deep red indicates thick, high cloud while violet indicates lower cloud with large water drops. Notice the more whitish blue, embedded streaks within the violet stratocumulus to the west of Spain and France; these are ship tracks caused by ship exhaust that produce local clouds with much smaller particles than the surrounding clouds.

Green indicates mid-level water cloud that is not too thick (otherwise more red, indicative of ice, would make the cloud appear yellow). The droplet size is small and temperatures range from -5 degrees C in eastern Spain to -25 degrees C in western Spain, making it a mid-level, supercooled water cloud that is not too thick.


Exercise:

MSG Daytime Microphysics RGB west of Spain, 30 Jan 2009

This RGB shows a variety of important microphysical cloud features. Thick, high clouds are shades of orange-red while lower clouds with smaller water drops are a greenish-blue color. The features in greenish-yellow over western Spain and along the east coast of Spain are low-level water clouds composed of smaller droplets.

What color is the post-frontal convective cloud in this RGB scene? (Choose the best answer.)

The correct answer is C.

The post-frontal convective clouds, which are orange-red, are just west of the frontal system and moving eastward into Spain and Portugal.

Fog & Low Clouds, MSG

Example of a MSG Fog and Stratus RGB

Description:

Made from infrared channel data, this RGB was originally designed for use with MSG SEVIRI data and tuned for monitoring the evolution of nighttime fog and low-level stratus clouds. Secondary applications include detecting fires and low-level moisture boundaries and classifying clouds in general. Since the product is tuned for nighttime conditions, its use during the day is limited.

Most polar-orbiting and geostationary environmental satellite imagers (with the exception of GOES-12 to -15) have the necessary channels to make this product. Future satellite imagers including JPSS VIIRS, GOES-R ABI, FY-3 and -4, Himawari, and MTG FCI will continue to the needed shortwave and longwave infrared channels for producing a similar nighttime fog and stratus RGB.

Coverage: Nighttime only

Channels:

  • Current MSG SEVIRI, NOAA and Metop AVHRR, FY-2 and -3, MODIS
    Future GOES-R ABI, MTG FCI, FY-4 AGRI, Himawari AHI

    Red: 12.0 minus 10.35 to 11 µm IR BT difference
    Green: 10.35 to 11 µm IR minus 3.5 to 3.9 µm SWIR BT difference
    Blue: 10.35 to 11 µm IR

Color scheme:

  • Low clouds are yellow to light green
  • Thick, high clouds are red
  • Thin, high clouds are dark blue to black
  • Land and sea surfaces appear in various colors

Advantages:

  • Fog and stratus often cannot be seen on infrared images at night because they blend in thermally with the background; this RGB enhances the fog/stratus signal
  • Is very important for ground and air transportation forecasting

Limitations:

  • Thin cirrus may obscure the view of fog and stratus
  • May be noisy and difficult to interpret in cold temperature environments (below approximately -10ºC)
  • Is difficult to detect thin radiation fog
  • The actual area of fog and low cloud is always slightly larger than in the image due the 3.9 µm IR channel’s increased sensitivity to warm pixels around the edges of the cloud cover

Live data links:

Additional information:

Example:

MSG Fog and Stratus RGB Animation Over Southern Africa, 05 July 2003, 0000 to 0600 UTC

Loop: The green areas over the southern portion of Africa are either fog or stratus. Notice how the cloud coverage increases throughout the night. When the sun rises toward the end of the loop, the low clouds turn red. That is due to the solar reflection off the water droplets in the fog and stratus as seen by the 3.9 µm shortwave infrared channel, which causes water clouds to appear similar to other features. For this reason, this particular RGB is only useful only during nighttime.


Exercise:

MSG Fog and stratus RGB product over South America, 0800 UTC 30 Jul 2001

In this RGB over South America, which of the labeled areas are fog or low cloud? (Choose all that apply.)

MSG Fog and stratus RGB product over South America, 0800 UTC 30 Jul 2001, with the main features identified

The correct answers are C, D, and E.

The areas around C, D, and E are fog or low cloud since they have a light green or cyan color. The reddish cloud is cirrus, which overlies a large area of stratus within the white enclosure.

Fog & Low Clouds, NexSat

Example of a NexSat Fog and Low Clouds RGB

Description:

This RGB helps in the detection of fog and low clouds at night, a task that is often difficult with single channel infrared images because features tend to blend into the thermal background. The most important input is the difference between the longwave and shortwave infrared channels.

Most current polar-orbiting and geostationary imagers have the necessary channels to make this product including the recently launched Suomi NPP polar orbiter. Future JPSS VIIRS, GOES-R ABI, Himawari AHI, and MTG FCI instruments will additional shortwave and longwave IR channels to improve the product and minimize false detections caused by low emissivity desert and other bare land surfaces.

Coverage: Nighttime only

Channels: Shortwave and longwave infrared on polar-orbiting and geostationary satellites

  • Red: 10.35 to 11 µm minus 3.5 to 4 µm SWIR BT difference
  • Green: 10.35 to 11 µm IR
  • Blue: 10.35 to 11 µm IR

Color scheme:

  • The land background is usually dark green, although it can be different shades of green or yellow
  • Fog and low cloud are shades of red, pink or orange
  • High cloud is cyan

Advantages: Enables the detection of low cloud at night when visible imagery is unavailable

Limitations:

  • Cirrus clouds can obscure the view of low clouds and fog at night
  • May not work well in regions of cold surface temperatures

References:

Miller, S. D., J. D. Hawkins, J. Kent, F. J. Turk, T. F. Lee, A. P. Kuciauskas, K. Richardson, R. Wade, and C. Hoffman, 2006: NexSat: Previewing NPOESS/VIIRS imagery capabilities. Bull. Amer. Meteor. Soc., 87, 433-446.

Lee, T. F., and S. D. Miller, 2003: Improved detection of nocturnal low clouds by MODIS, Preprints, 12th Conf. on Satellite Meteorology and Oceanography, Long Beach, CA, Amer. Meteor. Soc., CD-ROM, P5.23, also available online at https://ams.confex.com/ams/annual2003/techprogram/paper_51975.htm

Lee, T. F., S. D. Miller, C. Schueler, and S. Miller, 2006: NASA MODIS previews NPOESS VIIRS capabilities. Wea. Forecasting, 21, 649–655.

Example:

MODIS longwave IR image from 11 Jan 2008 over Lake Superior

There seems to be only cirrus in the MODIS longwave infrared image, while clouds are abundant in the fog and low cloud RGB (the orange-pink areas).

MODIS Contrail RGB from 11 Jan 08ODIS over Lake Superior

The MODIS fog and low cloud RGB is particularly valuable because the high-resolution, 1-km infrared channels produce a detailed view of low cloud features.


Exercise:

MODIS Longwave infrared over Florida, 28 Aug 2009

 Based on this longwave infrared image, where do you think the low cloud cover is? (Choose the best answer.)

MODIS Fog & Stratus RGB over Florida, 28 Aug 2009

The correct answer is C.

It is difficult to figure this out from the longwave infrared image alone, but the RGB makes it easy since low clouds appear as bright red or pink.

Fog & Low Clouds, GeoColor

Example of a GeoColor Fog and Stratus RGB

Description:

A robust, all-purpose product for general forecasters produced during both day and nighttime. The product is particularly useful for detecting low clouds and potential fog during nighttime. During the day, the clouds are superimposed on NASA’s Blue Marble image derived from the MODIS imager, and at night, clouds are superimposed on either a DMSP OLS or VIIRS Day Night Band background that shows city lights. Note that while the GeoColor product is not a standard RGB, it is largely constructed using RGB methods.

Coverage: Day and nighttime

Channels:

  • Daytime: 0.6 µm Vis (on NASA’s Blue Marble background or similar true color background image)
  • Nighttime: GOES 3.9 µm IR and 10.8 µm IR (on top of a DMSP OLS background image showing city lights)

Color scheme:

  • All clouds from the visible channel are white during daytime
  • At night from infrared data, water clouds including low clouds and potential fog are pink and high clouds are white

Advantages:

  • Intuitive and easy to interpret
  • Can be produced 24 hours a day
  • Can discriminate ice from water clouds (including low clouds and potential fog) during nighttime, and shows all cloud types as white during daytime

Limitations:

  • Brief gaps in cloud detection near sunrise and sunset as the product transitions between infrared (nighttime) and visible (daytime).
  • Because only visible channel information is used during daytime, product can not distinguish between water and ice cloud types.

Live data links:

Additional information:

Example:

2 images, one GOES IR from 31 Mar 2010 1215 UTC and a corresponding GeoColor Low Cloud over a DMSP OLS image

On the left is a nighttime GOES infrared image over Texas. As you can see, no low clouds or fog are evident. But they appear in orange in the low cloud product on the right. The yellow city lights are from the DMSP OLS background image.


Loops:

GOES-East VIS/IR (Day/Night) animation, 29 Mar 2010 from 0712 to 1345 UTC

In this infrared loop, the white clouds are associated with a frontal system over the eastern seaboard, and the states of Michigan, Ohio, Kentucky, Tennessee, Alabama, and Georgia are almost cloud free. When the sun rises and the GeoColor product switches to visible data, low clouds appear over these states.

GOES-East VIS/IR (Day/Night) animation, 29 Mar 2010 from 0712 to 1345 UTC

This animation of the GeoColor product shows the evolution of low and high clouds. Nighttime water clouds are evident in pink. Notice how they turn white when the sequence transitions to daytime.

Summary

About RGBs:

  • Generally made from three or more individual or differenced spectral channels; each is assigned to a primary color (red, green, or blue); the final product highlights atmospheric and surface features that are hard to distinguish with single channel images alone
  • Provide intuitive, realistic looking products that can reduce ambiguities and simplify interpretation
  • In some situations, different features can have the same color or the same feature can appear in different colors. One way to handle this is to animate the products
  • Can be overlaid with quantitative information, such as model data or other observational data, enabling more sophisticated analysis and interpretation
  • Are increasingly available online and in near real-time
  • Future satellite imagers will have increasing numbers of spectral channels, allowing for more RGBs and new applications

Sources of RGBs:

The process of building RGBs:

  • Step 1: Determine the purpose of the product
  • Step 2: Based on experience and available scientific information, select three appropriate channels or channel derivatives that provide useful information
  • Step 3: Pre-process the images as needed to ensure that they provide or emphasize the most useful information
  • Step 4: Assign the three spectral channels or channel derivatives to the three RGB color components
  • Step 5: Review the product for appearance and effectiveness; revise or tune as needed

Colors in the RGB color model:

  • Primary colors: Red, green, and blue
  • Secondary colors: Yellow (red + green), cyan (green + blue), and magenta (red + blue)
  • Gray: Equal amounts of any three colors
  • White: The primary colors in equal intensities
  • Black: The absence of the primary colors

Uses of RGB products:

List of applications for which RGB products can be used with the corresponding products.

Commonly used RGB products:

Table of RGB applications and products, each with a description, satellite(s) involved, and use (day/night/both)

You have reached the end of the module. Please consider taking the quiz and filling out the survey.