Detecting Marine Winds from Space: An Introduction to Scatterometry and the Current 
Operational ScatterometersJamie R. RhomeTropical Analysis and Forecast Branch NOAA/Tropical Prediction Center/National Hurricane Center Einführung Weather analysis and forecasting over the open ocean remains a significant challenge 
given the relative lack of in-situ observed data as compared to terrestrial areas. While 
ship observations and buoys go a long way toward filling these gaps, there still exist 
areas of data void. Thus, forecasters must rely on satellite remote sensing techniques 
to fill in the large data gaps for these areas. The traditional application of satellite 
data has been weather surveillance for locating weather features and estimating their 
intensity based on cloud temperatures and patterns. This application, however, has some 
limitations since it provides little information on variables such as surface winds. 
Thus, estimating surface wind can be particularly problematic, especially if there are no 
nearby ship observations. More recently, a wealth of advanced satellite technology and 
instrumentation has been introduced, which now allows the estimation of surface and 
near-surface winds from space. One of the most applicable developments for the marine 
community is scatterometry, the only satellite-borne technology capable of estimating wind 
speed and direction with nearly global daily coverage. This paper provides a brief 
introduction into scatterometry, its application to the marine community, and a description 
of a new scatterometer instrument to begin operations during fall of 2003. Scatterometry Scatterometers are satellite-borne microwave radar instruments, which send out a series of 
specific signals or pulses and then measure how much of that signal or energy is returned. 
Microwaves have sufficiently high frequency to penetrate clouds, thus overcoming limitations 
of the more traditional weather satellites (e.g. Geostationary Operational Environmental 
Satellites or GOES), which measure lower-frequency infrared and visible radiation. The amount 
of energy returned to the instrument, also known as backscatter, is primarily a function of 
reflection and scattering off the ocean surface. This backscattered energy varies according to 
the sea surface roughness caused by small (on the order of centimeters) wind generated waves 
also known as capillary waves. Rough seas reflect a stronger signal than calm and this difference 
in backscattered energy is used to estimate the wind speed. The backscattered energy is technically 
a measure of surface stress rather than the near-surface wind, which means it must be converted to 
wind speed using empirical relationships (Atlas et al. 2001). The wind speed is also corrected for 
stability and adjusted to a standard height. Thus, wind measurements from scatterometers are 
indirect estimates, not observations, and reflect the wind speed at 10 meters under neutral 
stability (Atlas et al 2001). While the amount of backscatter determines the wind speed, the 
orientation of the backscattered energy with respect to propagation of the original pulse determines 
the wind direction. Scatterometers measure the amount of backscattered energy from multiple 
directions or different angles depending on their scanning geometry. This provides up to four 
different possible solutions for the wind direction. The most likely wind direction is determined 
by identifying the best fit between the wind direction and observed backscatter from the different 
angles. Typically, the best fit corresponds to the true direction while the next best fit corresponds 
to the opposite direction. Scatterometer instruments are typically deployed on sun-synchronous near-polar-orbiting satellites 
that orbit the earth in a fashion such that they pass over the equator at approximately the same local 
times each day. These satellites orbit at an altitude of approximately 800 km (~ 500 statute miles) 
and are commonly known as Polar Orbiting Environmental Satellites (POES). This orbit and lower 
altitude differs from the more commonly known GOES satellites, which orbit above the equator at an 
elevation of 22,240 miles. GOES satellites orbit above the Earth at a speed which matches the Earth�s 
rotation, thus allowing them to hover or remain stationary over one position above the earth. Conversely, 
POES satellites complete fourteen daily near polar orbits each taking approximately 100 minutes to complete. 
This orbit allows roughly two passes over any geographical location per day. Modern scatterometers deployed 
on POES can thus provide nearly global daily coverage via a series of 1800 km wide swaths 
(<a HREF="detect.shtml#Figure 1"=""><b="">Figure 1</b></a>), as opposed to the GOES which has more limited overall coverage. 
Also, the POES lower earth orbit improves resolution, allowing more detailed monitoring of the ocean surface. 
Another difference between GOES and scatterometer instruments is that GOES sensors do not send out a signal but 
simply measure radiation emitted from the earth. For this reason, scatterometers are known as active sensors 
while sensors on the GOES are known as passive. Additionally, because scatterometers employ a microwave signal 
which can penetrate clouds, they can sample the earth�s surface under most weather conditions day and night. 
Conversely, GOES weather satellites are unable to see through cloud cover, thus limiting their ability to provide 
information of surface conditions during many weather situations. Scatterometers Past and Present The history of scatterometry dates back to World War II when radar observations over oceans contained 
clutter from signal returns off unknown sources. During the 1960s, this clutter was found to be due to 
the effects of wind. This led to the first tests of space based scatterometry during the Skylab missions 
of 1973 and 1974. These relatively short missions were followed by the deployment of the SeaSat-A 
Satellite Scatterometer (SASS). The SASS was a Ku band (microwave frequencies near 14 GHz) radar 
designed specifically for monitoring the ocean surface. It had antennas equipped on both sides of the 
satellite, which allowed for two simultaneous observational swaths approximately 500-km wide separated by 
a data free region called the nadir gap. The SASS mission operated from June through October of 1978 and 
demonstrated that accurate winds could be measured from space. The European Remote Sensing (ERS) 
satellites (<b="">Figure 2</b>) later followed the SASS and marked the beginning of nearly continuous scatterometer 
coverage. The ERS satellites were equipped with a C band (~ 5 GHz) scatterometer with 25 km resolution. 
These scatterometers utilized three antennae that generated radar beams looking 45 degrees forward, 
sideways, and backward with respect to the satellite's flight direction. The multiple beams allowed three different viewing angles of any particular point with different incidence 
angles and covered a 500 km-wide swath. The first ERS satellite (ERS-1) operated from 1991-1995 with the 
successor, ERS-2, becoming operational in 1995. While the ERS-2 satellite remains operational, the 
scatterometer failed in 2001. However, the radar altimeter equipped on the ERS-2 continues to provide sea 
height measurements. In 1996, the National Aeronautics and Space Administration (NASA) began a joint mission 
with the National Space Development Agency of Japan to maintain continuous scatterometer missions beyond ERS 
satellites. The first scatterometer via this joint effort was the NASA scatterometer (NSCAT) aboard the Advanced 
Earth Observing Satellite (ADEOS). The NSCAT was similar to SASS in that it employed antennas on each side of 
the satellite, allowing two simultaneous swaths but with slightly improved swath widths (~600-km). The Ku band 
NSCAT instrument operated with 50 km resolution and collected data for 9 months prior to prematurely losing power 
in 1997. NASA�s solution to the failed NSCAT was the QuikSCAT mission (for Quick Scatterometer), which 
was launched in 1999. To date, the QuikSCAT satellite remains in operation, far outlasting its 2-3 year 
mission life expectancy. The QuikSCAT mission includes a more advanced scatterometer instrument (called 
SeaWinds) that utilizes a conical scanning technique. This novel technique employs rotating dish antennae 
that eliminates the nadir gap and increases the swath width to approximately 1,800 km. The improved sampling size allows approximately 90% of the earth�s ocean surface to be sampled on a daily 
basis as opposed to two days by NSCAT and four days by the ERS instruments. The new scatterometer also has much 
improved horizontal resolution (25 km) over the 50 km NSCAT. Wind estimates taken by QuikSCAT are currently 
available to forecasters within about three hours of the observation time. Wind estimates are accurate within 
two meters/sec for speed and 20 degrees for direction in rain-free areas. The newest scatterometer instrument 
(commonly known as SeaWinds) was launched in December of 2002 onboard the ADEOS-2 satellite, later named Midori 
2. SeaWinds on Midori 2 (<b="">Figure 3</b>) is very similar to the SeaWinds on QuikSCAT and was first activated in 
January 2003. A six-month calibration/validation phase began in April, and regular operations are currently 
scheduled to begin this October. SeaWinds on Midori 2 will initially complement and eventually replace the 
existing QuikSCAT scatterometer. It is expected to last three to five years (<a HREF="detect.shtml#Figure 2"=""><b="">Figure 
2</b></a>). In addition to the SeaWinds scatterometer, the Midori 2 satellite is also equipped with an Advanced Microwave 
Scanning Radiometer (AMSR). The AMSR instrument detects microwave emissions from the Earth's surface and 
atmosphere and is able to measure various parameters such as precipitation. The ability to measure precipitation 
will allow for a more explicit detection of rain which can only be inferred from the current QuikSCAT instrument. 
This should improve the quality control of wind retrievals within and near areas of heavy rain, a major difficulty 
with the current QuikSCAT. This is discussed further in the limitations section below. An example of estimated 
surface marine winds from SeaWinds on Midori 2 is shown in <a HREF="detect.shtml#Figure 1"=""><b="">Figure 1</b></a>. Marine Forecasting Application There are many marine applications of scatterometer data including improved surface weather and sea state 
analyses, warnings, and forecasts. Scatterometers provide increased data availability over the oceans and fill 
in gaps between ship observations. The increased data aids in locating weather features such as fronts, 
troughs, high and low pressure centers, and surface circulations associated with developing tropical cyclones 
(TC). These features can have a large impact on both the current and forecast wind field and sea state. 
Scatterometer data has also greatly improved the determination of the TC wind radii, which has far reaching 
implications for both marine interest as well as pre-landfall emergency preparations. Atlas et al. (2001) 
provide a more comprehensive discussion of the impacts of scatterometer data on operational forecasts. It 
should be noted that scatterometer data is not a replacement for ship observations. In fact, these two 
sources of marine observations can complement each other. For example, Rhome (2003) showed an example where a 
ship observation concurrent with QuikSCAT wind estimates helped validate storm force winds within the Gulf of 
Tehuantepec. In situations where both ship and scatterometer data are available, there is much more confidence 
in the issuance of warnings. Cobb et al. (2003) further showed the operational impact of QuikSCAT data for 
validating marine warnings. One of the less commonly known applications of scatterometer data is improved initialization and evaluation 
of numerical weather prediction (NWP) models. Atlas et al. (2001) found that the ability of NWP to use 
scatterometer data has improved substantially. This has far reaching implications since accurate initialization 
of NWP models can be directly related to improved forecasts. Without accurate initial conditions, a model�s 
ability to provide accurate forecasts is severely limited. Imagine having very precise directions somewhere 
without knowing where to start. In effect, those directions are virtually useless. Meteorologists commonly 
use the phrase �garbage in equals garbage out� to convey that a numerical model with inaccurate initial 
conditions will produce an inaccurate forecast. For example, Atlas et al. (2001) showed an example from two 
powerful storms that affected France during December 1999. Control runs of NWP models without QuikSCAT data 
were unable to detect these features, leaving little warning time. However, similar NWP simulations with the 
QuikSCAT data included provided more evidence of the developing systems. In addition to aiding the initialization of NWP models, scatterometer data also allows forecasters to 
evaluate the accuracy of the initialization. For example, a feature of interest depicted in NWP output can 
be compared against the corresponding scatterometer data to determine if that feature is accurately positioned 
with the correct intensity (i.e., wind speed). This type of analysis is more applicable during situations where 
scatterometer data becomes available after the model�s initialization process. If the NWP model does not 
properly resolve the particular weather feature in question, then adjustments must be made to the forecast from 
that model, and/or another model must be utilized. A number of new marine applications of scatterometer data 
have recently been discovered, including detecting the extent of sea ice and ice concentration, iceberg tracking, 
and monitoring ice shelves. These new applications should significantly improve marine forecasts and warnings, 
thus continuing the scatterometer�s legacy as the mariner�s eyes in the sky. 	 Limitations Despite its many applications, scatterometer data is subject to several limitations and ambiguities, many of 
which can affect the availability and accuracy of the wind estimates. First, since scatterometers are deployed 
on polar orbiting satellites, data is only available at any given location approximately twice per day. This 
means that data is not updated continuously (as is the case with GOES satellites). Also the data is often 
outdated since it is delayed (by 3 hours for the current QuikSCAT). The forecaster must make adjustments, 
especially for cases of rapidly evolving weather systems, to compensate for the data delay. Additionally, gaps exist between the sampling swaths. These gaps are largest over the equator, smaller 
over the midlatitudes, and non-existent near the poles (<a HREF="detect.shtml#Figure 1"=""><b="">Figure 1</b></a>). This means 
that the satellite swath can completely miss a particular region or weather system, thus providing little or no 
wind information for that area. The problem is often compounded since the sub-orbital tracks (track in reference 
to the earth�s surface) do not exactly repeat on a daily basis. The satellite swaths progress from east to west, 
even though the local solar time of each satellite's passage is essentially unchanged for any latitude. Essentially, this means that the satellite swath can miss a westward-moving weather system during consecutive 
passes (often for many days) if the weather system moves at a speed similar to the satellite�s progression. 
In these cases, the scatterometer�s application is limited. Ambiguities in the retrieved wind direction are 
also a problem with scatterometer data. Scatterometers utilize measurements from multiple viewing angles in order 
to determine wind direction. Since these measurements provide up to four different solutions for direction, a 
best fit technique must be applied to determine the most likely direction among the possible solutions. Typically, the best fit corresponds to the true direction while the next best fit corresponds to the opposite 
direction. The process of determining the best fit can be complicated by noise within the data, especially at low 
wind speeds when the measured returned signal is weak. Also, determining the best fit is more difficult near the 
edges of the swath where there are relatively fewer observations due to the sampling process, and near the center 
of the swath where the ocean surface is viewed with a more limited set of incidence angles. Overall, the accuracy 
of wind directions near the swath edges, swath center, and within areas of weak flow is erroneous more often than 
strong winds in�sweet spots� of the swath on either side of the center (<b="">Figure 4</b>). Another limitation of scatterometers is that wind estimates are contaminated by heavy rain. This is caused by 
attenuation (reduction) of the signal as it travels to and from the ocean surface, increased scattering by the 
raindrops, and increased sea surface roughness caused by raindrop splashing on the ocean surface. For light 
wind conditions, heavy rain causes an overestimate of the wind speed, due to increased backscatter from the 
raindrops and increased roughness caused by splashing on the ocean surface. Conversely, rain contamination has 
a lesser effect on wind speed estimates in stronger wind situations since, the increased sea surface roughness 
caused by the wind will dominate over the rain effect. There are no sensors on the QuikSCAT satellite that can 
directly measure rain. Thus, identifying rain-contaminated retrievals is problematic. However, the post-processing 
of the QuikSCAT data includes a �rain flag� which alerts forecasters that certain wind estimates may be 
contaminated based on characteristics of the wind estimates that suggests that rain may be present.
These winds are often highly scrutinized by the forecaster or omitted altogether. Rain contamination can be 
a severe limitation especially for weather systems that cause deep convection such as tropical cyclones. An 
example from Hurricane Fabian (2003) is shown in <b="">Figure 5</b>. Rain flagging ability should be improved with the new SeaWinds scatterometer on Midori 2 since that satellite 
is also equipped with the AMSR instrument which can more directly measure precipitation. The above limitations 
of scatterometers can significantly affect the accuracy of the data and may present misleading information. In 
some cases, extensive experience and analysis is required to properly interpret that data and to compensate for 
untimely data. For this reason, mariners should use caution when making crucial decisions based solely on 
scatterometer information. The National Weather Service�s high seas and offshore forecasts incorporate 
forecaster interpretation of scatterometer data and other remotely sensed information as well as ship 
observations. Acknowledgments The author wishes to acknowledge the thoughtful comments by Dr. Richard Knabb, Dr. Chris Hennon, Christopher 
Burr, Robbie Berg, and Hugh Cobb which greatly aided in the composition of this paper. This article makes use of 
information presented on the Jet Propulsion Laboratory webpage (<a HREF="http://www.jpl.nasa.gov"="">www.jpl.nasa.gov</a>). 
For more information on the new SeaWinds scatterometer on Midori 2, see <a HREF="http://winds.jpl.nasa.gov/news/index.html"="">
http://winds.jpl.nasa.gov/news/index.html</a>. Biography Jamie Rhome has worked as a meteorologist/forecaster for the Tropical Analysis and Forecast Branch of the 
Tropical Prediction Center since September 1999. He received his B.S. and M.S degrees in Meteorology from North 
Carolina State University. Previous work experience includes the United States Environmental Protection Agency 
and the State Climate Office of North Carolina at North Carolina State University. References Atlas, R., R.N. Hoffman, S.M. Leidner, J. Sienkiewicz, T.-W.Yu, S.C. Bloom, E. Brin, J. Ardizzone, J. Terry, 
D. Bungato, and J.C. Jusem, 2001: The effects of marine winds from scatterometer data on weather analysis and 
forecasting. Bulletin of the American Meteorological Society, Vol 82, No. 9, 1965-1990. Cobb, H.D., D.P. Brown, and R. Molleda, 2003: The use of QuikSCAT imagery in the diagnosis and detection of 
Gulf of Tehuantepec wind events 1999-2002. Preprints 12th Conference on Satellite Meteorology and Oceanography. 
Long Beach, CA 9-13 Feb. 2003. PP 410. Rhome, J.R., 2003: Application and use of volunteer observing ship (VOS) data at the Tropical Prediction 
Center/National Hurricane Center. Mariners Weather Log. Spring/Summer, 2003. |
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