Your focus on this page should be on interpreting scatterometry data, which requires an understanding of the abilities and limitations of scatterometers. Specifically, you should be able to discuss the primary use of scatterometry data and interpret a variety of scatterometry data using its basic principles of operation. You should also be able to interpret all panels of a multiplatform satellite surface wind analysis, and discuss the data sources that go into these analyses.
Of all of the remote sensing instruments you've studied in this lesson, scatterometers are unique because they have the ability to remotely measure surface wind speed and direction over water. For the record, a scatterometer is a high-frequency radar ("high" compared to the standard network of ground-based Doppler radars, which are "S-Band radars"). So, a scatterometer is an active remote sensor--it emits pulses of microwave radiation and measures the radiation that backscatters to the unit, similar to standard weather radar.
A number of scatterometers have been mounted on polar-orbiting satellites and have made key contributions to tropical forecasting since the 1990's. Their ability to measure wind speed and direction give forecasters valuable data about tropical cyclones forming and developing over remote seas. For example, even though Tropical Storm Isabel (eventually Hurricane Isabel) was well outside the range of aircraft reconnaissance, forecasters at NHC used scatterometry data to classify the storm, as noted in their 5 P.M. discussion from September 6, 2003.
In a nutshell, scatterometers transmit pulses of microwaves with relatively short wavelengths (relatively high frequencies) and measure the backscatter from the wind-roughened ocean. The faster the winds are, the rougher the ocean surface, and the more radiation that backscatters to the scatterometer. In turn, meteorologists correlate backscattered microwave energy to wind speed and direction. As you'd expect, actually determining wind speeds and directions is a complex, imperfect process, but we'll explore those issues in a bit.
Before we get into the interpretation of scatterometry data and a deeper discussion of how scatterometry works, I want to quickly elaborate on the relevance of scatterometry. From a forecasting perspective, scatterometry gives forecasters the ability to detect tropical cyclones in their earliest stages of development. Early detection is important, of course, because it affords the general public and maritime interests greater lead time to prepare for any eventual threat. Scatterometry can detect centers of wind circulations that have the potential to develop into tropical cyclones many hours in advance of their attaining formal status as a tropical depression.
For example, the graph on the left below shows how indispensable scatterometry can be. The data apply to the 2001 hurricane season over the Atlantic basin and the vertical axis on the graph represents the number of lead-time hours provided by scatterometry. Using scatterometer winds, researchers forensically identified potential tropical cyclones an average of 43 hours before forecasters at the National Hurricane Center had formally classified the systems as tropical depressions. The equally impressive results for the eastern Pacific basin in 2001 are shown in the figure on the right below.
Identifying potential tropical cyclones from scatterometer data involves the detection of low-level relative vorticity (recall that vorticity is a measure of "spin") associated with a developing cyclonic circulation of winds. For example, check out the low-level cyclonic vorticity derived from scatterometer data at 11Z on September 1, 2001, that indicated the potential for a tropical cyclone to form. As it turned out, this low-level circulation served as the seed for Hurricane Gabrielle.
Interpreting Scatterometry Data
What does scatterometry data look like? Once the data have been processed by computers, the output looks something like the image below, which shows data from the QuikSCAT scatterometer on September 11, 2008. Notice a few important things. First, there's no scatterometry data over land (remember scatterometers measure backscattered radiation from ocean waves, so that makes sense). Secondly, there's a notable swath of missing data that extends south-southeast from the Carolina coast. Like the other sensors mounted aboard polar orbiting satellites, coverage gaps exist in the data (the satellite's "view" on any one pass is only so wide, so some areas naturally get missed). Finally, the surface wind barbs on this particular image show a large cyclonic swirl over the Gulf of Mexico, which corresponded with Hurricane Ike.
Note that most of the wind barbs near Ike's center are black and show very high speeds, which doesn't make sense with the color code used on the graphic (black represents speeds five knots or less). Furthermore, the circulation is hardly neat and tidy. It turns out that these black wind barbs have a special meaning -- they indicate that the data are unreliable. This "black flag" convention isn't universal, however. Some Web sites use other symbols to indicate unreliable data (a gray dot at the base of a wind barb is another common indicator). Scatterometers have trouble collecting good data in areas of heavy rain because raindrops severely attenuate microwave radiation, which weakens the signal received at the satellite. In addition, heavy rain splashing down on the ocean surface alters the small-scale structure of the surface ocean waves, which changes the nature of the backscattering to the satellite. Ignoring the unreliable "rain-contaminated" data on this particular image, it suggests that Ike's maximum surface wind speed was only about 50 knots (an underestimate since Ike was a hurricane).
This image provides a good example of why scatterometry data is primarily used to identify cyclonic circulations in embryonic tropical cyclones. Because heavy rain can prevent scatterometers from accurately discerning wind direction and speed, they typically don't provide useful data near the center of stronger tropical cyclones (because that's where lots of heavy rain falls in eye wall thunderstorms). So, scatterometry is generally not a good way to assess the intensity of a strong tropical cyclone. In weaker tropical systems, fewer organized areas of heavy rain exist, which yields a more useful data set.
You should also note that scatterometry has applications beyond the tropics, such as identifying sea ice in polar regions. Glacial snow and ice very effectively backscatter microwaves to the scatterometer (more effectively than even wind-roughened oceans), which allows scientists to identify boundaries of sea ice from their strong return echoes.
Characteristics and Limitations
Rain contamination isn't the only limitation of scatterometry data, however. A number of scatterometers have provided useful data in recent decades, and each one was a bit different. Therefore, each had its own unique set of characteristics and limitations. I'm going to briefly summarize the main characteristics and limitations of some significant scatterometers since you may encounter data from them if you're exploring current or past tropical cyclones online.
- QuikSCAT (operational 1999 - 2009): The SeaWinds Scatterometer aboard the QuikSCAT satellite was a "Ku-Band" radar, which transmitted microwave energy at a frequency of 13.4 GHz. The use of this frequency had a couple of important consequences. First, QuikSCAT had a relatively high resolution (about 12 kilometers), but it was also highly sensitive to areas of precipitation (which led to more rain contaminated data). Like data from any polar orbiting satellite, gaps in coverage existed, but QuikSCAT did "view" the earth in relatively wide 1800-km swaths. If you're interested, you can read more about the QuikSCAT mission.
- ASCAT (operational 2006 - current): The Advanced SCATerometers are mounted aboard Europe's Metop satellites. Each is a "C-Band" radar, which transmits microwave energy at lower frequencies (longer wavelengths) than QuikSCAT (5.255 GHz, to be exact). The use of lower frequencies means that ASCAT's resolution (about 25 kilometers) is reduced compared to QuikSCAT; however, ASCAT is a bit less sensitive to attenuation in areas of heavy rain (although rain contamination isn't eliminated entirely). Despite a reduced sensitivity to heavy precipitation, ASCAT does have a documented low bias when wind speeds are high (especially higher than 20 meters per second, or 39 knots). ASCAT passes have larger coverage gaps since it views the earth differently than QuikSCAT. ASCAT views the earth in two parallel swaths 550 kilometers wide, with a nadir (the point on the earth directly beneath the satellite) gap of about 700 kilometers between them. The bottom line is that each ASCAT unit only sees roughly 60% of what QuikSCAT saw, but having more than one ASCAT unit orbiting the earth helps to compensate. Feel free to read more about the ASCAT mission, if you're interested.
Each scatterometer passes over a region twice per day (one "ascending" pass and one "descending" pass), and to gain a better understanding of the differences in coverage for a single QuikSCAT and ASCAT pass, check out the image below, which shows a coverage comparison between the "ascending pass" of QuikSCAT (right) and the "descending pass" of ASCAT (left). The superior spatial coverage of QuikSCAT is obvious, and note that the coverage gaps of both scatterometers are maximized at the equator, get smaller in the middle latitudes, and are eliminated entirely near the poles (which doesn't really help tropical forecasters).
A few other scatterometers have made important contributions to tropical cyclone forecasting:
- OSCAT / SCATSat: The Oceansat-2 SCATtereometer was part of a mission launched by the India Space Research Organization (ISRO) / Space Applications Center (SAC). Operationally, OSCAT was very similar to QuikSCAT in its capabilities and limitations, but only 4.5 years after its launch in 2009, OSCAT became inoperable due to a technical malfunction. It's initial replacement (SCATSat) became operational in 2016.
- ISS-RapidScat (operational 2014 - 2016 ): The RapidScat instrument was NASA's formal replacement for QuikSCAT, and was very similar to QuikSCAT in its instrumentation (it's also a Ku-Band radar, which is highly sensitive to rain contamination). Of note, RapidScat flew aboard the International Space Station (hence the "ISS" in its name). One key difference was that ISS-RapidScat has an orbital altitude only about half that of QuikSCAT, which resulted in a narrower viewing swath of earth (only about 1100 km). You're welcome to read more about the ISS-RapidScat Mission, if you're interested.
You may encounter data from any of these scatterometers or others (China and France have launched satellites with scatterometers aboard, too, for example) when looking at past or current tropical cyclones online, so it's important that you understand their basic characteristics and limitations (particularly with respect to problems in areas of heavy rain and any established biases in wind data). If you're interested in viewing scatterometry data for current or past storms, check out the links in the Explore Further section below.
Multiplatform Satellite Surface Wind Analyses
Despite the fact that scatterometry doesn't provide much help in assessing the maximum winds in a strong tropical cyclone, it can help meteorologists construct the overall wind field of a particular storm. Scatterometry contributes to a product called a "Multiplatform Satellite Surface Wind Analysis." The basic idea behind the product is to synthesize wind observations from remote sensors aboard satellites to construct a wind field for a tropical cyclone. These analyses are created with satellite-based data alone (no in-situ or aircraft reconnaissance data are involved), and since approximately 90% of the world's tropical cyclones aren't sampled by aircraft reconnaissance, you can appreciate just how important these analyses really are.
In order for you to be able to interpret these analyses and understand what data sources are used to create them, let's look at a sample of the product for Hurricane Ike at 18Z on September 11, 2008. Note that the product consists of two complete analyses. In the link provided, The first large image (top left) is the analysis of inner-core surface winds around Hurricane Ike at 18Z on September 11, 2008. The second complete analysis (shown below) displays a broader-scale surface wind analysis of the storm (the black contours are isotachs, expressed in knots). Finally, there are four other images, which show the building-block data for the inner-core and broader-scale surface wind fields.
Each complete analysis includes some text, which gives us additional information about the storm's wind field:
- QUA = Quadrant (Northeast, Southeast, Southwest, and Northwest)
- R34, R50, and R64 = The maximum radius of 30-, 50-, and 64-knot winds in each quadrant in nautical miles
- VMAX = The maximum wind speed in the analysis in knots
- RMW = The distance of the location of the maximum wind speed from the center in nautical miles
- BEARING = The direction of the location of the maximum wind speed from the center in degrees
- MSLP = The estimated minimum sea-level pressure of the storm in hectopascals (equivalent to millibars)
In addition to the two complete wind analyses, the product also includes images showing the building-block data used to create them. The image labeled "AMSU" represents surface wind data around Hurricane Ike that were derived from the Advanced Microwave Sounding Unit at 18Z on September 11, 2008. You may recall that AMSU-A can't directly measure surface wind speeds, but using complex equations that govern atmospheric motions (way beyond the scope of the course), AMSU-A brightness temperatures archived from past storms were correlated with QuikSCAT and other data to derive surface winds from AMSU-data.
The image labeled "CDFT" represents cloud-drift winds based on infrared and water-vapor imagery (about which you learned earlier in this lesson) around Hurricane Ike at 18Z on September 11, 2008. Of course, the winds directly derived from such techniques are not surface winds, but winds aloft are empirically adjusted downward to estimate winds at the ocean surface.
The image labeled "IRWD" indicates surface winds derived from cloud temperatures on infrared imagery around Hurricane Ike at 18Z on September 11, 2008. In a nutshell, researchers closely examined infrared imagery of 87 tropical cyclones and used IR temperature data in concert with observed and estimated wind data to create an algorithm to estimate low-level wind fields of tropical cyclones.
The image labeled "SCAT" (below) displays scatterometer winds from ASCAT (in red) and QuikSCAT (in blue) around Hurricane Ike at 18Z on September 11, 2008. In this particular case ASCAT completely missed much of Ike's circulation (only capturing the western and eastern edges with its scans), while QuikSCAT got a pretty good "look" at Ike. That's not surprising since the chances of sampling an entire circulation were much higher with QuikSCAT. The data void near the center of Ike's circulation resulted from unreliable, rain-contaminated observations.
I only gave very brief descriptions here about the various techniques for using AMSU, cloud-drift winds, and IR winds to determine surface winds, so if you would like more information, you can check out the product description, which includes some links to seminal research papers involved with the product's development.
I also only gave a simple overview of how scatterometry works in this section. If you're interested in the more complex nuances of scatterometry, check out the Explore Further section below. Otherwise, it's time to wrap up our extensive treatment of remote and in-situ sensing in the tropics. I hope that you can now appreciate the importance that remote sensing plays in analyzing tropical cyclones, but even with the application of new technologies and techniques, meteorologists face numerous challenges and can only make best estimates about the current state of tropical cyclones around the world!
Key Data Resources
If you want to access scatterometry data for analyzing current or past tropical cyclones, you should bookmark these links:
- NESDIS Center for Satellite Applications and Research: Includes data from the major scatterometers, including an archive. This page also includes data from some passive microwave sensors (which we did not cover) that determine surface wind vectors. Feel free to explore those sensors on your own, if you wish.
- Naval Research Lab--Tropical Cyclones: By clicking on "Wind Vectors" you can access a variety of scatterometry data (if available) overlaid on some of the other remote sensing products we studied in this lesson.
- RAMMB-CIRA at Colorado State: Among many other remote sensing products, this is the home of the experimental multiplatform satellite surface wind analysis (for both current and past storms).
- NESDIS Multiplatform Tropical Cyclone Surface Winds Analysis: This is the operational home of multiplatform satellite surface wind analyses. The real-time interface is more user-friendly than the one at RAMMB-CIRA, but the archive is not as user friendly.
How does scatterometry really work?
Although you have a basic idea of how scatterometry works, the process of determining surface wind speed and direction is actually quite complex. To start gaining an appreciation for how scatterometry really works, imagine you're canoeing on a pond or lake. The wind is light, but occasionally a slight breeze kicks up and blows across the relatively smooth water. You look down at the water and notice tiny ripples on the surface of the water. Those tiny ripples are likely capillary and/or gravity waves, whose wavelengths are on the order of centimeters (we'll call these "short water waves"). For all practical purposes, these waves are a measure of the "roughness" of the sea surface, which, in turn, depends on wind speed (as wind speed increases, the air exerts a greater drag on the water, making the sea surface rougher).
When transmitted pulses of microwave energy strike the ocean, microwaves are scattered in all directions, but depending on the angle that microwave energy strikes the ocean, there is a "select" size of short water waves (whose wavelengths are comparable to that of the transmitted microwaves) that promote sufficient backscatter to the satellite. This unique kind of scattering is called Bragg scattering, and the "select" short water waves are Bragg waves. Of course, short water waves often "ride" on larger waves, thereby tilting the short water waves and changing their perceived size relative to the satellite. At this point, these "tilted" waves no longer have a strong Bragg-scatter signal, but other tilted waves now have the optimal perceived size to contribute to the overall signal. The bottom line is that with all of these effects, extracting the wind speed can be a messy process; however, the basic idea that faster wind speeds lead to rougher seas holds true. As a result, as the surface becomes rougher, the intensity of backscattering microwaves that reach the satellite increases, and the intensity of backscattering microwaves is then correlated to surface wind speed.
Wind direction gets a bit trickier. Although most wind-generated waves move with the wind, the small waves that backscatter microwaves to the radar travel every which way, and the scatterometer "sees" them all! So, there's definitely some "ambiguity" associated with determining wind direction from scatterometry. For each swath, ASCAT, for example, gets three looks at the ocean surface (one with each of its antennae), which help to reduce the ambiguity associated with wind direction.
To give you an idea of the possible wind directions that scatterometers have to manage, check out the image of QuikSCAT wind ambiguities from September 11, 2008 (when Hurricane Ike was swirling over the Gulf of Mexico, as you saw previously in this QuikSCAT image). Each line originating from a point represents a possible wind direction for that location (most observation points have two or three possibilities), and from these possibilities, computers determine the most likely wind direction based on the multiple looks that the scatterometer had.
Ambiguity selection is not a perfect process, however. Indeed, if the final scatterometer analyses look a bit odd to experienced forecasters, they will sometimes take a plot of the scatterometer ambiguities and conduct their own hand analysis to better determine wind direction based on their experience.
Now that you understand scatterometry's reliance on short water waves, you can truly understand its problems in areas of heavy precipitation. In addition to rain's significant attenuation of microwaves, raindrops splashing down on the ocean surface can also dampen out Bragg waves. For example, check out this image from the radar aboard the ERS-1 satellite, which shows the ocean footprints from strong surface winds generated by a cluster of evening thunderstorms that erupted over the Gulf of Thailand on June 5, 1992. The horseshoe-like footprints correspond to the winds caused by downdrafts of rain-cooled air impacting the sea and then spreading radially outward from the cores of the storms. The dark areas inside the footprints represent areas where heavy rain splashing down on the sea surface erased the Bragg waves that backscatter the radar signal to the ERS-1 satellite.
In either case, the weakened return signal to the scatterometer leads to erroneous results in wind speeds and directions over regions where rain rates are high, and the rain-contaminated data get marked as unreliable, often with a black flag.