Fig. 1. The Cirrus Detection product portrays semi-transparent (optically thin) cirrus as blue and opaque (optically thick) cirrus and areas of deep convection as magenta. A natural color (left) view of Hurricane Bonnie (August 2004) shows the storm centered in the Gulf of Mexico, and the Cirrus Detection product (right) shows the full extent of the cirrus fan (dark blue) spiraling off the top of this system. Thick clouds associated with convective rainfall bands near the stormís center are highlighted in magenta. Lower atmosphere clouds and the Earthís surface are depicted as black & white scale visible imagery.
1) Product Information:
- Who is developing and distributing this product?
The Cooperative Institute for Research in the Atmosphere (CIRA) in Fort Collins, Colorado, in cooperation with the Naval Research Laboratory (NRL) in Monterey, California are developing and distributing the Cirrus Detection product. The product is based on shortwave and thermal infrared channels on the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor which will also become available on GOES-R ABI.
- Who is receiving this product, and how?
The Cirrus Detection products are being made available to the National Weather Service (NWS) Regional Headquarters from which they are distributed to Weather Forecast Offices (WFOs) for display on their local AWIPS systems. Imagery updates are available approximately two times per day from the MODIS sensors on board Terra (~10:30 AM local time) and Aqua (~1:30 PM local time).
- What is the product size?
The size of Cirrus Detection images is determined by the span and resolution of the domain itself. Since current AWIPS system displays accommodate 1-byte per pixel, a good rule of thumb is that the size of the imagery (in bytes) corresponds roughly to the total number of pixels in a given AWIPS domain. For example, an AWIPS domain having dimensions of 1000 x 1000 pixels will require approximately 1 Megabyte (~106 bytes).
2) Product Description:
- Purpose of this product:
Cirrus clouds are the most widespread class of cloudiness in the Earthís atmosphere. Researchers have dedicated considerable time and effort to understanding the complex role cirrus clouds play in defining the "energy budget" of our planetís climate. To weather forecasters, cirrus cloud formations help to identify mid-latitude "jet streams" (currents of rapidly moving air in the upper atmosphere), analyze the structure of frontal and orographic cloud systems, and understand sky cover conditions (e.g., thin cirrus and opaque stratiform clouds may both produce "100% overcast" conditions, but result in very different light conditions at the surface). In other applications, thin clouds are not the feature of interest but rather a source of contamination (for example, land characterization, rainfall estimation, estimation of temperature/moisture profiles from satellite, and pilot visibility). In these latter cases, detection of cirrus can be used to flag areas of uncertainty.
- Why is this a GOES-R Proving Ground Product?
The Cirrus Detection product demonstrates the kind of imagery that will be possible in the GOES-R era. The GOES-R series of satellites will feature the Advanced Baseline Imager (ABI) sensor, which includes channels that are currently unavailable on GOES satellites. The new channels will enable new and/or improved capabilities which can be demonstrated now only via proxy or simulated datasets. Of particular interest here is the use of the 1.38 µm shortwave infrared band (entirely new to geostationary satellites). The 1.38 µm band is situated in a part of the electromagnetic spectrum where water vapor is strongly absorbing. In this band, the atmosphere appears very opaque to radiation and effectively extinguishes (absorbs) the sunlight as it passes down into the lower atmosphere. The result is that very little, if any, reflected sunlight low-level clouds and the underlying surface is detected in this band. Instead, only the reflected sunlight from the upper portions of the troposphere (where the path of the sunlight through absorbing water vapor is small), and primarily any high-level clouds such as cirrus, is detected. The result is a Ďflavorí of visible imagery in which only the highest clouds, which reside over the bulk of the absorbing water vapor, are seen. Think of the lower atmosphere as having been screened out. Since cirrus clouds are often thin and produce a weak reflectance signal in conventional imagery, they are often "lost" when the override bright surfaces. Even infrared based techniques that use temperature or spectral differences in cirrus properties often fail to detect very thin cirrus. During the daylight hours, the 1.38 µm band will overcome many of these limitations. GOES-R ABI will be able to provide the Cirrus Detection capability at significantly higher temporal resolution (including 5 min CONUS and 15 min full disk refresh rates).
- How is this product created now?
The Cirrus Detection product is based on measurements from the NASA Moderate-resolution Imaging Spectroradiometer (MODIS). It is actually composed of two algorithms - one that works for day and the other for night. We can take advantage of additional information from solar reflection channels during the daytime orbits to produce a superior product. At night we resort to other channels for cirrus detection, but must be more conservative in order to avoid false alarms in the enhanced imagery.
The daytime algorithm hinges on the measurement of reflected sunlight in the 1.38 µm shortwave infrared channel mentioned above. This channel is special because the abundant water vapor of our planetís atmosphere is strongly absorbing in this region, meaning that sunlight that travels too deep into the atmosphere has no chance of reflecting back to the satellite. Cirrus clouds, which reside high in the atmosphere and above most of the water vapor, reflect this 1.38 µm radiation with minimal effect of water vapor absorption, and are therefore sensed by MODIS. Low-level clouds and the surface, on the other hand, are "buried in the vapor" and effectively filtered from the scene. Reflection detected in the 1.38 µm channel above a certain threshold is considered "cirrus" and color-coded as blue.
To separate thin from thick cirrus, we use two more MODIS channels (6.7 and 11.0 µm) situated in the infrared part of the spectrum. The 6.7 µm channel corresponds to another one of those "water vapor absorption bands" just like the 1.38 µm channel, but since itís in the infrared part of the spectrum (where sunlight has minimal contribution) here we are dealing with Earth/atmosphere emissions only. The 11.0 µm channel is in a so-called "clean window" region, in the sense that water vapor and other gases are mostly transparent to radiation having this wavelength (so from a satellite vantage point w could in principle see all the way down through the intervening atmosphere to the surface in this band, if a cloud is not in the way). Measurements from these two channels are expressed in units of temperature (Celsius or kelvins (= Celsius+273.15)), and we are interested in the difference between these two measurements as a way to find thick, deep clouds in the scene.
The idea is that low clouds will again be "buried" in the water vapor, and since the 6.7 channel sees the temperature of the cooler water vapor present above the cloud (since temperature decreases with height), the satellite-measured temperature will be lower than the actual cloud top temperature. The same cloud observed at 11.0 µm (the window channel) will yield a temperature much closer to the true cloud top temperature. So, the difference between the two measurements (6.7 vs 11.0) will be large. Conversely, for a high and thick cloud, there is not much water vapor above it to depress the relative difference between 6.7 and 11.0, so the values in this case are small. Essentially we look for small differences between these two channels as a proxy for where the high and thick clouds are. We color code these areas as red. In terms of color composites, anywhere where we have both cirrus (blue) and "thick high cloud" (red) forms a magenta color.
While the 6.7-11.0 µm difference technique described above also applies to night imagery, we no longer have access to the 1.38 µm information to identify any thin cirrus component. Fortunately, another channel difference between 3.7 µm and 11.0 µm works fairly well for this purpose. 3.7 µm is a special channel because it is sensitive to both sunlight and earth/atmosphere radiation. At night, the sun-component is removed and this channel is very useful for detecting warm surface emissions (this channel is the cornerstone of fire detection). Because it is so sensitive to heat, any small amount that transmits through thin cirrus to reach the satellite creates a strong signal. This is in contrast to the 11.0 µm measurement, which will report a cooler temperature. In this way, we look for large differences between 3.7 and 11.0 µm as a way to highlight thin cirrus, which gets color coded as blue just like in the daytime enhancement.
3) Product Examples and Interpretation
Fig. 2. Example of the differences in appearance between the daytime (left) and nighttime (right) cirrus product for the same thunderstorm complex viewed roughly 9.5 hours apart.
Figure 2 shows an example of the Cirrus Detection product during the day and night for a squall line passing over southern Texas. In both day and night imagery, thin cirrus clouds appear dark blue while thick cirrus/t-storm tops appear magenta. Areas devoid of cirrus are substituted with visible-channel imagery during the day time, or infrared imagery at night. These regions are represented in black & white to provide context of the meteorology in terms of other cloud formations that may be present in the current scene.
What to look for: Mature thunderstorms typically appear as oval shapes with bright magenta cores and potentially large fans of blue (thin) cirrus, depending on the strength of the upper level wind field. Cirrus associated with large, organized storm systems often appear as extensive shields ahead of the storm front. Jet stream cirrus, also commonly observed during the winter time months and associated with storm systems, typically appear as elongated chords following an undulating path along the upper level flow.
Things to watch out for: Since the 1.38 µm channel only applies to daytime scenes, the nighttime cirrus product lacks the same high sensitivity to thin cirrus as the daytime product. Variable surface properties (especially over land) and lower atmospheric water vapor make the problem even more challenging. At night the product sometimes fails to pick up all the thin cirrus. Occasionally there will be "gray rims" surrounding enhanced cloud structures; the product has failed to discern these clouds as cirrus. Figure 3 illustrates an example of these missed thin cirrus regions (some pointed out with white arrows) associated with the remnants of thunderstorm anvils over southern Texas and northern Mexico.
Fig. 3. Example of gray rims (white arrows) around optically thin cirrus clouds, which are clouds that the nighttime Cirrus Detection algorithm failed to identify.
Another potential issue arises on occasion for high, snow-covered terrain and relatively dry atmospheric columns. In these cases there may not be sufficient total column water vapor to completely mask the bright, elevated surfaces, which begin to appear in the Cirrus Detection product as false cloud features particularly in the daytime imagery. These features are usually easy to spot (dendritic patterns tied to the topography), and in the GOES-R era would be even more readily identified as static features in the dynamic field. However, for demonstration from MODIS (where very limited temporal refresh is available) identifying these artifacts may be a greater challenge, so users should be more cautious when assessing this product in mountainous regions.
4) Advantages and Limitations
Owing to variable cloud opacity, infrared imagery does not provide a direct means to isolating the high cloud cover in the scene. A key advantage of the Cirrus Detection product is the ability to quickly identify regions of high cloudiness and qualitatively assess their opacity via color information (blue = thin, magenta = thick).
Although an attempt is made to provide a day/night capability, the algorithms are indeed different.