Journal of Oceanography, Vol. 58, pp. 137 to 151, 2002
Review
Microwave Remote Sensing of Tropical Cyclones from
Space
KRISTINA B. KATSAROS 1*, P ARIS W. VACHON2 , W. TIMOTHY L IU3 and PETER G. BLACK 1
1
NOAA/Atlantic Oceanographic and Meteorological Laboratory,
4301 Rickenbacker Causeway, Miami, FL 33149, U.S.A.
2
Canada Centre for Remote Sensing, 588 Booth Street, Ottawa, Ontario K1A 0Y7 Canada
3
Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109-8099, U.S.A.
(Received 10 August 2001; in revised form 18 September 2001; accepted 20 September 2001)
This article reviews several microwave instruments employed in research and analysis of tropical cyclones (TCs), typhoons, and hurricanes. The instruments discussed
include scatterometers, microwave radiometers, synthetic aperture radars (SARs),
and rain radar from space. Examples of the particular contribution by one or more of
these instruments in analysis of several storms illustrate the comprehensive new views
provided by the SeaWinds scatterometers, the detailed high-resolution wind field
provided by RADARSAT-1 SAR, particularly inside and in the vicinity of hurricane
“eyes,” and the presence of secondary flows in the region between rainbands in TCs.
The high spatial resolution of precipitation data from the Tropical Rainfall Measuring Mission’s rain radar, combined with scatterometer or SAR data, give a significant improvement in the details that can be seen from space, at the surface, and in the
precipitating areas of TCs. The microwave instruments provide a penetrating view
below the upper level cirrus clouds.
Keywords:
⋅ Microwave remote
sensing,
⋅ tropical cyclones,
⋅ scatterometer,
⋅ SAR, syntheticaperture-radar,
⋅ rain radar.
warmed by the sun’s radiation. A tropical depression can
grow rapidly through the various stages to a tropical storm
and tropical cyclone (TC). Once a tropical cyclone, the
intensity is delineated by the Saffir-Simpson scale widely
used in the United States (Weatherwise, 1974), which is
based on the maximum sustained wind in the center
eyewall region of the storm and defined in terms of the
associated destruction when the storm arrives on land
(Table 1). In east Asia, another definition of storm intensity is used, the main difference being that the U.S. designation refers to maximum wind speed averaged over 1
minute, while the east Asian definition employs 10-minute
averages (Longshore, 1998).
Since the storms develop over the ocean, information was often unobtainable at the early stages in the time
before satellites. Landfall often surprised the unwarned
population with disastrous effects. The same is true today, if communication systems do not allow the information to reach the population at risk. High clouds in the
storm’s outflow and waves, generated in the high wind
region, traveling ahead of a storm could give warning, if
the population has experience. However, these storms are
sometimes not very large, so that relatively small areas
1. Introduction
Tropical weather systems, whose generic name is
tropical cyclones, are locally known by the name typhoons
in the western Pacific Ocean and by hurricanes in the
Atlantic Ocean, Caribbean, and eastern Pacific. Typhoon
means “big wind” in Chinese and hurricane is an ancient
Native American name (hurak’an) for “wind” and “to
blow away” (Webster’s Third New International Dictionary, 1986). Tropical cyclones also occur over many other
regions of the tropical ocean.
These storms can be the most terrifying and destructive weather phenomena on Earth. They form over warm
tropical waters, a short distance from the equator, where
the Coriolis force can act on air accelerating towards a
central low pressure. The convection associated with these
storms is fueled by large amounts of water vapor that
evaporate from the sea, rise and condense, releasing internal energy or latent heat. The evaporation requires a
steady heat source, which is provided by the tropical ocean
* Corresponding author. E-mail: [email protected]
Copyright © The Oceanographic Society of Japan.
137
Table 1. Saffir-Simpson hurricane intensity categories.
Category
1
2
3
4
5
Central pressure
(millibars)
>980
965–979
945–964
920–944
<920
Wind speed
MPH
Knots
74–95
96–110
111–130
131–155
>155
64–83
84–96
97–113
114–135
>135
Damage
Minimal
Moderate
Extensive
Extreme
Catastrophic
Fig. 1. Two Atlantic hurricanes, Floyd in 1999 and Andrew in 1992, seen from the GOES satellites in visible light at the same
scale (note the outlines of Florida and Cuba). Floyd was about 1000 km in diameter, while Andrew was half the size at 500
km.
are affected and the understanding and knowledge gained
by experience may not have been easily shared or it may
not have been very factual. Tropical cyclones can vary
widely in intensity and size. Figure 1 shows two recent
Atlantic TCs, Andrew and Floyd, at their peak of development, both category 4 in these images, illustrating that
TCs can be very different in size at the same intensity.
Very few storms go through all the intensity levels of the
Saffir-Simpson scale and category 4 and 5 storms are,
fortunately, rare events.
The first satellites were polar orbiters at about 800–
900 km altitudes providing images in visible and infrared (IR) wavelengths, now so familiar from television
news programs. The geostationary satellites also employ
visual and IR images from a 36,000-km altitude to allow
frequent updates (every hour to every 15 minutes being
possible). These satellites have an orbit that coincides with
the daily rotation of the Earth and can, therefore, remain
above a fixed point on the equator. The images in Fig. 1
show the eyes of the storms clearly, although occasionally, the eye can be obscured to visible light or infrared
138
K. B. Katsaros et al.
radiation by high cirrus clouds. Infrared images are more
practical because they can be employed day and night.
Another advantage of the infrared is that it gives an indication of the severity of the storms, since cold, high clouds
(especially if widespread) indicate severe convection.
This review presents examples of the satellite data
available in the third millennium to help weather services around the world identify these storms while over
the ocean, and allow researchers to advance the knowledge base that can lead to improved analysis and forecasts. It will not attempt to be exhaustive, but will instead focus on a few recent developments in remote sensing by radars and microwave radiometers.
2. Background
A tropical cyclone is an intense cyclonic vortex that
starts as a depression in the atmospheric surface pressure.
In the Atlantic Ocean, they are associated with “easterly
waves” that are triggered by mountains or convection over
Africa. The depression deepens as convection intensifies
in the region of the depression and is defined as a tropi-
Fig. 2. The classic diagram of a mature tropical cyclone (after Palmén and Newton, 1969). Left-hand side: P and T signify
pressure and temperature isopleths showing the decrease in pressure towards the storm center and increase in temperature
(except in the boundary layer). Right-hand side: Isopleths of the temperature departures from a standard atmosphere reflecting the result of strong heat production in the central part of the storm and outward advection of this warm air at higher levels.
Arrows illustrate the air motion. Cloud distribution is described below the x-axis at right as a function of radial distance from
the center.
cal storm when the surface wind reaches a maximum
speed of 17 m s–1. Further intensification, which is defined in terms of the surface wind speed, leads to tropical
cyclones of the various categories.
To place the discussion on a factual basis, we illustrate the structure of a mature tropical cyclone in Fig. 2
(Palmén and Newton, 1969). Over the past 20 years, a
technique to infer tropical cyclone stage of development,
or “intensity,” i.e., inner core surface wind maximum or
minimum central pressure, has been developed based on
remotely-sensed data (Dvorak, 1984). This technique
employs information on the minimum cloud top temperature and the distribution and area covered by cold temperatures around the central core of the storm. An alternative index using visible data is also applicable. An extension to a microwave index, derived from the operational Special Sensor Microwave/Imager (SSM/I) is also
in use (Roger Edson, personal communication).
Research questions are associated with the genesis
of TCs, since many depressions form and even become
tropical storms, but not all develop into TCs. The intensity change of a TC is currently not possible to predict
accurately. The rapid intensification of a category 2 to a
category 3 storm and beyond has several known causes
(e.g., DeMaria and Kaplan, 1994; Shay et al., 2000), one
being the additional heat source provided by a deep warm
thermocline, such as exists in the oceanic warm eddies in
the Gulf of Mexico. Sufficient inertial stability in the
atmosphere, as reflected in a small Rossby radius of deformation, is also needed to retain the heat released by
the cumulus convection (Ritchie et al., 2002). Similarly,
the Gulf Stream and the Kuroshio Current’s warm waters
can provide the fuel for intensification. Upper level
vorticity can also become aligned and add vertical velocity to the circulation in the TC; often this happens as the
storm transitions to a mid-latitude baroclinic system.
Research in these areas is still very active, and will require some time, since the storms are complex and one
mechanism is not sufficient to explain the intensification
of all systems. The U.S. National Oceanic and Atmospheric Administration (NOAA) operates research aircraft
that can penetrate TCs and measure the structure of the
storm by dropsondes and the ocean waters by airborne
expendable bathythermographs (AXBTs). They also carry
remote sensing instruments similar to those on spacecraft.
The Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory (AOML) carries out research on TCs employing these and other data.
The aircraft measurements are also useful and unique in
the world for calibration and validation of wind observations at high wind speeds, which contributes to making
the satellite data more useful worldwide. Microwave remote sensing has been used for almost 20 years on these
aircraft. The Stepped-Frequency Microwave Radiometer
(SFMR) has gone through many development phases, and
is currently operational, delivering information on surface wind speed in real time to the National Hurricane
Center, the office of the U.S. National Weather Service
that issues forecasts. A scatterometer radar is also operated from the aircraft experimentally. Figure 3 shows several wind measurements obtained from one of these aircraft while crossing the center of Hurricane Floyd in 1999.
Microwave Remote Sensing of Tropical Cyclones from Space
139
Fig. 3. Aircraft wind speed data collected during a transect through Hurricane Floyd. “SFMR” presents surface wind speed
measured with the aircraft’s Stepped-Frequency Microwave Radiometer (SFMR). The flight level winds at 4 km are higher, as
expected. Yellow symbols are from dropsonde data: squares represent the highest wind speeds measured, inverted triangles
represent 100 m winds, and circles represent the last measured winds, i.e., surface wind speeds below 10 m height. The
bottom trace is the rain rate (after Willoughby, 2002).
3. Microwave Remote Sensing
One great advantage of microwave signals, whether
simply emitted by the surface and received by the satellite or transmitted from a spacecraft reflected from the
sea and received at the spacecraft, is that at the longer
wavelengths (low frequencies, 5–14 GHz) they are not
severely absorbed by the clouds and rain in the storm. At
higher frequencies, 19 to 37 GHz, absorption by clouds
and rain provide information about these phenomena. At
22 GHz, the atmosphere has a weak absorption line due
to water vapor, so that the total amount of water vapor in
the atmosphere can also be obtained.
Microwave remote sensing has the drawback that it
requires large antennas and it has, therefore, only been
possible to boost such instruments into space on polarorbiting satellites and other low-earth orbiting platforms.
First, we discuss active microwave sensors called
scatterometers, used to deduce surface wind speed and
direction. Another active sensor is the Synthetic Aperture Radar (SAR), which also can be used as a wind speed
sensor. The third type is a microwave radiometer, a passive instrument, which simply receives the signals emitted by the Earth’s surface and the atmosphere. Here we
only consider measurements over the ocean. Another recent satellite employs radar and a radiometer in space to
evaluate the precipitation inside the storm, analogously
to the ground-based rain radar. Short descriptions of these
instruments follow.
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K. B. Katsaros et al.
Fig. 4. Wind scatterometer geometry. The three wind
scatterometer antennae generate radar beams 45° forward,
sideways, and 45° backwards across a 500-km wide swath,
200 km to the right of the sub-satellite track (after ESA,
1992).
3.1 Scatterometers
Scatterometers send microwave pulses to the Earth’s
surface and measure the backscattered power (so-called
radar cross-section) from the surface roughness. Over the
Table 2. Scatterometer instruments and their characteristics on the following satellites: Seasat (operating June–September 1978);
European Remote Sensing satellites 1 and 2 (1991–2000); Advanced Earth Observing Satellite, ADEOS (carrying NSCAT,
August 1996–June 1997); and QuikSCAT (launched August 1999).
ocean, the backscatter depends on ocean surface roughness due to small (centimeter scale) waves. The assumption is that these surface ripples are in equilibrium with
the local wind stress, which may not be true everywhere
but has proven valid in the predominant conditions. The
backscatter depends not only on the magnitude of the
wind-stress but also on the wind direction relative to the
direction of the radar beam (the azimuth angle, e.g., Jones
et al., 1978; Attema, 1991). Space-based scatterometers,
operating on polar-orbiting satellites, provide both wind
speed and direction through multiple looks at any one
pixel in the swath as the spacecraft travels over the surface. Two wavelength bands, at frequencies of approximately 5 and 14 GHz (C-band and Ku-band, respectively),
have been used with satellite scatterometers (see Table
2). The higher frequencies of the Ku-band allow greater
sensitivity at low wind speeds to wind direction but also
exhibits stronger influence from atmospheric precipitation, as compared to the lower frequencies of C-band. The
geophysical model functions, from which ocean surface
wind vectors are retrieved, using the measured radar crosssections, are largely based on empirical fits to the data
(e.g., Jones et al., 1978; Cavanié and Lecomte, 1987;
Freilich and Dunbar, 1993; Thiria et al., 1993; Stoffelen
and Anderson, 1997; Wentz and Smith, 1999).
The European Remote Sensing (ERS) satellites ERS1 and 2 (Attema, 1991) launched in 1991 and 1995, respectively, carried the Advanced Microwave Instrument
(AMI) which operated at C-band (5.3 GHz frequency)
and provide wind vectors at 50-km resolution over a 500km swath to the right of the satellite subtrack (Fig. 4).
This narrow swath limits the daily coverage to 40% of
the global ocean, and it required three days to provide
almost full coverage. Bentamy et al. (1999) found good
agreement between the satellite winds derived from ERS1 data and buoy winds (Fig. 5).
The National Aeronautics and Space Administration
(NASA) launched the very first scatterometer on the
Seasat satellite in June 1978, which survived for only three
months. It operated at Ku-band (14.6 GHz). Four fan-
beam, dual-polarized antennas illuminated two 500-km
swaths, on each side of the spacecraft, providing wind
vectors at 50-km resolution. However, only one side was
in operation most of the time. The U.S. developments
since then have built on that experiment. In 1996, a NASA
Scatterometer (NSCAT) was launched on the Japanese
spacecraft Midori (ADEOS). Its six fan-beam antennas
provided 600-km wide swaths on both sides of the spacecraft. It measured at Ku-band and provided ocean surface winds at 25-km resolution and covered 77% of the
global ocean every day until its demise in June 1997. The
quality of data was shown to be above expectation through
comparison with in-situ measurements (e.g., Bourassa et
al., 1997; Freilich and Dunbar, 1999) and through comparison with winds from National Weather Prediction
models (e.g., Liu et al., 1998; Atlas et al., 1999; Ebuchi,
1999).
A new scatterometer, SeaWinds, was launched on the
NASA mission QuikSCAT in June 1999. SeaWinds uses
pencil-beam antennas in a conical scan around nadir. The
antennas radiate Ku-band microwaves at 46° and 54° incident angles and measure the backscattered power across
a continuous 1800-km swath. SeaWinds is capable of providing wind speed and direction at 25-km resolution over
93% of the Earth’s ice-free oceans every day, under both
clear and cloudy conditions (Graf et al., 1998).
The empirical form of the original scatterometer algorithm leads to an asymptotic decrease in the sensitivity
in scatterometer-derived winds for higher wind speeds
(Cavanié and Lecomte, 1987). Insufficient comparison
measurements from buoys has prevented significant improvement of wind retrieval for wind speeds over 25 m/s.
Quilfen et al. (1998) illustrated with the C-band Active
Microwave Instrument (AMI) data, at 25-km resolution,
obtained for a special 20-orbit data set, that the backscatter
cross section continues to show sensitivity beyond 20
m/s winds in tropical cyclones (Fig. 6). Similarly, Yueh
et al. (2000) have shown that a Ku-band scatterometer is
sensitive to both wind speed and direction under hurricane conditions, with wind speeds up to 35 m/s. ImproveMicrowave Remote Sensing of Tropical Cyclones from Space
141
Fig. 5. (a) Comparison of the wind speed, and (b) direction estimated from the scatterometer measurements with NOAA buoy
winds (after Bentamy et al., 1999).
Fig. 6. (a) Closeup of Super Typhoon Billis observed by QuikSCAT and the TRMM, August 21, 2000. The TRMM algorithm for
rain rate (mm/hr) is displayed in the background colors. Gray shading outlines the TRMM swath. (b) Winds over the Atlantic
Ocean. White streamlines indicating wind direction are superimposed on the color image of wind speed at 0Z for September
13, 1999, derived from objective interpolation. Typical average backscatter coefficients over land and Antarctica are also
added. The data are based on interim data products from SeaWinds on QuikSCAT at the standard resolution of 25 km. The
insert represents an area between 69.1°W to 72.8°W and between 22.4°N and 25.5°N occupied by Hurricane Floyd, and the
wind vectors are based on a 12.5-km resolution special SeaWinds product (after Liu and Katsaros, 2001).
ment of wind retrieval algorithms under the strong wind
conditions of tropical cyclones is being vigorously pursued (e.g., Jones et al., 1999; Liu et al., 2000).
The equivalent neutral winds produced by
spaceborne scatterometers (Liu and Tang, 1996) are
uniquely related to the surface stress by definition (Liu
and Katsaros, 2001). Another valid algorithm approach
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K. B. Katsaros et al.
relates the backscatter observation directly to measurements of surface stress instead of to the equivalent neutral wind; the feasibility was demonstrated by Liu and
Large (1981), Weissman and Graber (1999), and others.
The minimum requirements for the U.S.
scatterometer are wind speed accuracy of either 10% or 2
m s –1, depending on which is larger, and directional ac-
curacy of 20 degrees, in the speed range of 3–20 m s–1
(for NSCAT) and 3–30 m s–1 (for QuikSCAT), under all
weather conditions, except under heavy rain. Freilich and
Dunbar (1999) clearly showed that the accuracy of
NSCAT data exceeds the requirement. Like the comparison in Fig. 5 other data also meet the requirement.
Bentamy et al. (2000) illustrate that three different remotely sensed wind estimates show small biases, of the
order of 0–1.25 m/s and rms values of <2 m s–1 compared
to three sets of buoys: the Tropical Ocean Global Atmosphere-Tropical Atlantic Ocean (TOGA-TAO) array in the
tropical Pacific Ocean, the National Data Buoy Center
(NDBC) operational array, midlatitude U.S. coast, and
ODAS, the European network. Higher accuracy and spatial resolution are desirable in a number of applications,
including study of hurricanes (Liu et al., 2000).
3.2 Synthetic Aperture Radar
SAR can take high-resolution images of the Earth’s
surface by transmitting pulses of microwaves and receiving and processing the resulting backscattered signal. The
technique synthesizes a very large aperture by using the
Doppler (i.e., frequency) shift induced by the relative
motion of a small aperture past the scene of interest. Very
high-resolution imagery, up to 10-m resolution for satellite systems that are currently in operation, may be obtained by using this technique.
Launched by NASA in 1978 and operating for only
about three months, Seasat carried the first civilian
spaceborne SAR, an L-band (23.5 cm wavelength), HH
(horizontal transmit, horizontal receive) polarization SAR.
Seasat SAR images have a nominal resolution of 30 m
with a nominal image swath of 100 km. This instrument
provided the first high-resolution radar images of the
Earth’s surface, and provided startling images of ocean
structure (e.g., Vesecky and Stewart, 1982). Following
Seasat, NASA has had several short duration SAR missions on the Space Shuttle (Shuttle Imaging Radar, SIR).
Some longer duration spaceborne SAR missions include the instruments on the European Space Agency’s
(ESA) ERS-1 and ERS-2 (Attema, 1991), NASDA’s
JERS-1 (Nemoto, 1991), and the Canadian Space Agency’s RADARSAT-1 (Raney et al., 1991). The ERS-1 mission was launched in 1991, and the satellite continued to
function until 2000. ERS-2 was launched in 1995 and is
still in operation. Both satellites carried C-band (5.6 cm
wavelength), VV (vertical transmit, vertical receive) polarization SARs. The images have a nominal resolution
of 30 m with a nominal image swath of 100 km. The ERS
SARs were not intended to be operational systems, but
have proven to be very successful in both research and
pre-operational roles. JERS-1 was launched in 1992 and
operated an L-band SAR until 1998. The JERS-1 SAR
had inadequate transmit power for ocean applications.
RADARSAT-1 was launched in 1995 and carries a
C-band, HH polarization SAR with an acquisition swath
of 500 km. RADARSAT-1 offers a trade-off between spatial resolution and swath coverage, ranging from 10 m
for a 50-km wide swath in the fine beam mode of operation, to 100 m for a 500-km wide swath in the ScanSARwide mode of operation. RADARSAT-1 is operated on a
commercial basis and is used for operational surveillance
tasks including ship detection for fisheries surveillance
and sea ice monitoring (e.g., Vachon et al., 2000).
Future SAR missions include ENVISAT, the followon to the ERS missions, with launch expected in late 2001;
RADARSAT-2, the follow-on to RADARSAT-1, with
launch expected in 2003; and ALOS, the follow-on to
JERS-1 with launch also expected in 2003. Each of these
missions will offer both high and low-resolution modes,
as well as fully polarimetric modes and modes that will
allow the user to choose the transmit and receive
polarizations. These new modes will enhance and extend
the applications for spaceborne SAR images.
SAR images of the ocean provide a unique view of
many physical processes in the upper ocean and within
the marine atmospheric boundary layer. Any physical
process that modulates the ocean surface roughness at the
scale of the radar wavelength may be imaged. Surface
winds are the most important effect. Rougher areas corresponding to higher wind speeds appear bright, while
smoother areas corresponding to lower wind speeds appear relatively dark. The ocean surface imprint of many
atmospheric phenomena, such as gravity waves, convective cells, and storms, may be seen in SAR ocean images
(e.g., Mourad, 1999). Surface currents can also modulate
the roughness, providing imaging mechanisms for oceanic fronts, internal waves, and bottom topography (e.g.,
Johannessen et al., 1996). Surface films, naturally occurring or as a result of oil spills, damp the roughness at the
radar wavelength scale and appear dark in a SAR image
(e.g., Gade et al., 1998). The images of all of these processes are ambiguously combined, often making image
interpretation difficult.
A calibrated SAR image can provide information on
the wind speed at the ocean surface, but with a much
higher resolution than is possible with a scatterometer.
Indeed, the same wind retrieval models can be used. Required inputs include the observed radar cross section
from the SAR image, the local incidence angle, and the
relative wind direction (e.g., Scoon et al., 1996; Vachon
and Dobson, 1996; Wackerman et al., 1996). Estimation
of the relative wind direction is a problem since, unlike a
scatterometer, the SAR only provides one look direction
relative to the wind direction. As such, other wind direction cues are needed. For example, the orientation of
kilometer-scale boundary layer rolls, assumed to be parallel to the surface wind direction, may be measured (e.g.,
Microwave Remote Sensing of Tropical Cyclones from Space
143
Mourad and Walter, 1996). Alternately, other sources of
wind direction (such as from models or scatterometers)
could be used to guide the wind field retrieval from SAR
images. Intercomparison of SAR-derived winds with buoy
observations has shown wind speed errors of 1.9 m/s RMS
for the ERS SARs and 2.4 m/s RMS for RADARSAT-1
SAR (Vachon and Dobson, 2000). The latter figure also
applies to the ScanSAR wide mode of operation, a mode
that poses a challenging radiometric calibration problem.
3.3 Microwave radiometer
The radiometer development has an even longer history than scatterometry, but a major step was the Scanning Multichannel Microwave Radiometer (SMMR) on
board both Seasat (1978) and NASA’s Nimbus 7 satellite
from 1978–1985. Since 1987, there has been a series of
launches of Special Sensor Microwave/Imagers, SSM/Is,
which operate at frequencies of 19, 22, 37, and 85 GHz
in dual polarization, except for 22 GHz, which has only
vertical polarization. SSM/Is are on the polar-orbiting operational spacecraft of the Defense Meteorological Satellite Program (DMSP). They have provided continuous
wind speed measurements over the global ocean since July
1987. Several DMSP satellites have been in orbit at the
same time, providing complete daily coverage with ever
better algorithms based on the long data record (e.g.,
Wentz, 1997; Krasnopolsky et al., 1999). SSM/I has a
wide scan and, therefore, good coverage (1400 km) but
does not provide information on the surface wind direction. The wind speed from SSM/I has been combined with
surface wind data from a numerical model through a variational method to produce wind vector fields at six-hour
intervals and at 2° × 2.5° resolution (Atlas et al., 1996).
These large-scale wind fields over the tropical Pacific
were evaluated by Busalacchi et al. (1993) by comparison with operational numerical weather prediction (NWP)
products and interpolated wind fields from ship measurements and cloud motions. They found large-scale similarity between SSM/I and other wind fields. The SSM/I
wind fields have more structure and energy than NWP
winds but have the same directional characteristics as the
NWP winds. SSM/I is an operational sensor that will be
operated well into the 21st century. The wind from SSM/
I is, however, of secondary value in hurricane observations compared to the precipitation and cloud liquid water information derived from the higher frequencies of
37 and 85 GHz. Algorithms for rain rate are still somewhat uncertain as major intercomparison projects illustrated (Adler et al., 2001). Nonetheless, the presence of
rain is a more dependable piece of information and very
useful for hurricanes, whose thick cirrus shield sometimes
obscure completely what lies beneath to visible and infrared sensors. The Advanced Microwave Scanning Radiometer (AMSR) is currently planned for NASA’s Aqua
and Japan’s (NASDA) ADEOS-2 satellites and is expected
to provide new and interesting data on the temperature
structure of TCs.
3.4 Rain radar in space
An active radar in space that observes rain is flown
on the Tropical Rainfall Measuring Mission (TRMM)
(Kummerow et al., 1998). This satellite is in a low orbit,
350 km above the Earth’s surface. Its subtrack covers latitudes of ±35°. The satellite observes this subtropical-tropical belt once per month. The aim of the satellite was to
obtain better climatological rainfall estimates in the tropics. Table 3 summarizes the resolution and data available
from three of TRMM’s instruments (NASA, 1998).
Table 3. TRMM instrument characteristics.
After NASA Brochure (NP-1998-12-074-GSFC).
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K. B. Katsaros et al.
TRMM is a joint mission of NASA and NASDA and
was launched in November 1997, with a microwave
imager (TMI) and a precipitation radar (PR) onboard
(Kummerow et al., 2000). The TMI measures radiance
from 10.7 GHz to 85 GHz, from which a suite of parameters can be derived, including the sea surface temperature, surface wind speed, integrated water vapor, and rainfall over oceans. The spatial resolution varies with frequency; the lowest resolution is 45 km at 10 GHz and
increases to 5 km at 85 GHz. The PR sends radar pulses
and measures the backscatter from the atmosphere, giving TRMM the unique ability to measure the three-dimensional rainfall distribution over both land and ocean.
The horizontal resolution is 4.3 km. The low-inclination
orbit of TRMM is designed to give an optimal sampling
rate for monitoring rainfall at low latitudes.
kts (10 m/s) to the south of the center and a substantial
region of convection in the area (NOAA, 1997). The results were promising for the usefulness of this new type
of scatterometer. The rain was known to cause interference with the algorithm. Another study conducted during
the first hurricane season of QuikSCAT’s existence examined the contribution that the winds in the surroundings of a tropical storm or TC could make to evaluation
of surface wind fields. Uhlhorn et al. (2000) reported that
the gale force wind radius, important for forecasting and
for emergency managers, could be better defined with
inclusion of the QuikSCAT/SeaWinds wind vectors in the
surface fields produced at AOML (Powell et al., 1998).
Subsequent studies have led to analysis of the errors
caused by rain and attempts at developing a routine
method for detecting cyclonic vorticity (Sharp et al., personal communication).
4. Examples of Microwave Observations of TCs
Since many of the microwave instruments are on
experimental satellites, their data have not always been
available in real time, but rather near-real time, i.e., within
a few hours to one day of collection. Therefore, the examples that follow are based on research projects rather
than operational experience.
4.1 Value of large coverage by SeaWinds
The power of synoptic global coverage and high spatial resolution by a space-based scatterometer is clearly
illustrated in Fig. 6(a) of Super Typhoon Billis. The impact of scatterometer winds in the analysis of Hurricane
Floyd, shown in the insert of Fig. 6(b), is discussed by
Liu et al. (2000). The SeaWinds data used in the insert
were specially produced to have a spatial resolution of
12.5 km and for the strong wind and high precipitation
conditions in tropical cyclones. Both the scatterometers
on Seasat and NSCAT have a data gap at nadir between
the two swaths. The continuous 1800-km wide swath of
SeaWinds is a tremendous technical advance, especially
for measuring storm systems, although interference by
the heavy rain in TCs, especially at Ku-band, is a drawback.
4.2 Early detection of cyclones by scatterometer data
The web-based dissemination of SeaWinds data on
QuikSCAT provided an opportunity to examine whether
the surface wind vectors in SeaWinds 1800-km swath
could help detect wind circulations around a low pressure center very early in the genesis phase. A demonstration project in the Atlantic Ocean was carried out at AOML
in 1999 before detailed quality control of the data had
been completed (Katsaros et al., 2001). Indeed, even very
weak circulations could be identified very early before
the full criteria of a tropical depression were present (Fig.
7). “Full criteria” include a minimum wind speed of 20
Fig. 7. Top: QuikSCAT wind field at 10:44 UTC on October
27, 1999 over the tropical cyclonic circulation that developed into Tropical Storm Katrina. Bottom: GOES infrared
image of the same area approximately four hours later. The
exact time is labeled on the figure (after Katsaros et al.,
2001).
Microwave Remote Sensing of Tropical Cyclones from Space
145
Scatterometers have reached high enough development technologically that the signals have low noise and
do not require averaging over as many pulses as the design prescribed in order to obtain a meaningful signal.
Thus, the backscatter power, σ o, has been used from the
ERS-1 scatterometer at 25-km resolution to identify the
lowest wind in a typhoon or hurricane, thereby locating
its center (Quilfen et al., 1998). Figure 8(a) shows such a
pattern of σ o from an ERS-1 pass, while Fig. 8(b) shows
the associated wind speed. Similar methods are being investigated using QuikSCAT data.
Fig. 8. Sections of: (a) S 1 and S 2; and (b) wind speed as a
function of the latitude, through the center of TC Elsie (also
the region of maximum winds, incidence 35.7°) on November 5, 1992. Solid and dashed lines are for the high- and
low-resolution modes (25 and 50 km), respectively. S1 and
S 2 are defined by S1 = ( σ1 + σ 3)/2 and S 2 = (σ 1 – σ 3)/(σ 1 +
σ 3), where σ 1 and σ 3 are the normalized radar cross sections for the two lateral antennas of the ERS-1 scatterometer
(after Quilfen et al., 1998), 90° apart (see also Fig. 4).
4.3 Details of surface wind structure from RADARSAT1 SAR
The RADARSAT-1 SAR images of surface wind
patterns at 100-m and higher resolution show details of
the inflow into convective cells and rainbands and show
low signals in the rain itself, probably due, in part, to
absorption by the rain, but also to destruction of the winddriven patterns of capillary waves and capillary-gravity
waves on the sea surface caused by impacting raindrops
(e.g., Atlas and Black, 1994; Katsaros et al., 2000).
An interesting phenomenon discovered in the regions
between rainbands on most of the RADARSAT-1 images
of hurricanes obtained in 1998 and 1999 are the striations,
longitudinal patterns of high and low wind speed, with a
cross-wind wavelength of 3–5 km (Katsaros et al., 2000).
Fig. 9. The RADARSAT SAR image of Hurricane Mitch is on the left (© CSA, 1998) and the TRMM rain rate for Mitch
approximately 3 hours prior to the RADARSAT pass is on the right. The red box delineates the limit of the SAR image. The
band seen in the upper right corner of the SAR image (E–E′) matches well with that seen in the TRMM radar image. It is
speculated that the long linear features on the ocean surface seen in the SAR image occur in a weakly convective region
between rainbands, as illustrated with the TRMM image. TRMM data courtesy of the Naval Research Laboratory, Monterey,
CA. Reprinted by permission (Katsaros et al., “Wind fields from SAR: Could they improve our understanding of storm
dynamics?” pp. 86–93, Vol. 21, No. 1, 2000) © The Johns Hopkins University Applied Physics Laboratory.
146
K. B. Katsaros et al.
These patterns are characteristic of roll vortices seen as
cloud streaks in cold-air outbreaks from a continent over
warm water (e.g., Thompson et al., 1983; Alpers and
Brümmer, 1994; Mourad and Walter, 1996). Figure 9
shows evidence of these roll vortices in the southwestern
part of the image from Hurricane Mitch by the Honduran
coast.
Another interesting feature is the high-speed incursions into the TC eye, probably due to mesovortices, rotating with the mean wind (e.g., Vachon et al., 2001). More
recently, several more hurricane eyes have been observed
by RADARSAT-1 in the ScanSAR wide mode. Figure 10
shows asymmetries and wave patterns in the surface wind
in the eye region that illustrate the contributions to be
made by these satellite data in understanding tropical
cyclones. Disturbances on the eyewall can be attributed
to so-called mesovortices. Coincidence with in-situ data
obtained by reconnaissance aircraft is desirable for understanding the features. An example of aircraft and satellite coincidence in Hurricane Floyd is seen in Fig. 11,
where dropsonde data obtained within an hour of the
RADARSAT-1 pass-time provided the surface wind vectors plotted over the RADARSAT-1 image. These data
are from a region between rainbands, at a distance from
the eye.
Another study of Floyd’s wind field is provided by
the high resolution scatterometer data in Fig. 6(b) (after
Liu and Katsaros, 2001). It also illustrates the variability
in surface wind speed around the eye of Floyd. However,
rain flags had not been applied to the data set, so that
some rain contamination of the image is possible.
Fig. 10. RADARSAT-1 SAR images of five hurricane eyes (after Vachon et al., 2001) (© CSA, 1998, 1999).
4.4 Microwave radiometers
The surface wind speed provided by SSM/I serves
the TC community mostly by their assimilation into the
background numerical models. However, the ability of
the higher frequencies of SSM/I to locate rain can be illustrated with two products available from the U.S. Navy
web site in real time (http://www.nrlmry.navy.mil/sat-bin/
tc_home). They include 85 GHz brightness temperatures,
which basically show the concentration of cold, highcloud mass (similar to the IR used in the Dvorak index),
but independent of the high cirrus ice cloud that can make
an IR signal cold with little precipitation below. SSM/I
also provides an index of precipitation, PCT, based on an
emission algorithm (Lee et al., 1999; Hawkins et al.,
2000).
4.5 Tropical Rainfall Measuring Mission
What TRMM can provide in terms of detail within
the central region of a tropical cyclone can be seen for
Super Typhoon Billis in Fig. 6(a) and for Hurricane Mitch
in Fig. 9. Liu et al. (2000) used the high resolution TRMM
Fig. 11. (a) Hurricane Floyd (September 14, 1999) SAR image
(© CSA, 1999) with dropsonde winds superimposed; and
(b) the derived vector wind field.
Microwave Remote Sensing of Tropical Cyclones from Space
147
data to discern the details in the inner core of Hurricane
Floyd. Rodgers et al. (2000), combining TMI and SSM/I
data, were able to examine inner-core convective bursts
and rain rate of Supertyphoon Paka. The precipitation
radar, with a swath width of 220 km, has less coverage
than the microwave imager swath, which is 760 km. The
narrow radar swath makes it difficult to catch tropical
cyclones. Nonetheless, Liu et al. (2000) were able to combine surface wind measurements by QuikSCAT and the
rain profile obtained by PR to demonstrate the interplay
between the dynamic and hydrological processes in the
hurricane.
5. Conclusions and Discussion
It is clear that observations of tropical cyclones from
satellites pose special advantages, since these weather
systems develop over the oceans near the equator far from
human habitation. They are dangerous, so it is advantageous if human life must not be put at risk when obtaining data. Microwave instruments have the great advantage that they penetrate into, even through, most clouds
and at certain wavelengths interact with the precipitation,
which can, therefore, be measured below the cirrus cloud
shield. Currently, the value to forecasters is limited by
the low resolution, since the structure and size of the inner core, the magnitude of the surface winds in a narrow
region in the eyewall, and presence of double eyewalls
(Willoughby et al., 1982; Willoughby, 1990), cannot be
readily resolved and these are the parameters that define
intensity and evolution of the storm. RADARSAT-1 SAR,
and TRMM’s radars are exceptions, but their sampling is
not adequate for operational use (see below). However,
the most recent scatterometer, the SeaWinds instrument
on QuikSCAT, has a low-enough noise level that individual pulses or smaller areal averages (~12 km) can be
used. Experimentally, the “backscatter cross section,” σo,
the basic signal has been used to locate the eye region
where the low wind speed corresponds to a minimum in
σo . This is a promising development that allows locating
the eye more precisely than with the wind vector solutions. It will be pursued in research mode in coming hurricane seasons in the United States.
Even for TRMM’s rain radar that has a relatively high
resolution, a trade-off on swath-width had to be made,
such that the radar only covers a swath of 220 km, which
will only be sufficient in viewing a typhoon or hurricane
when the eye happens to be near the center of the swath.
Another limitation is the limited sampling by polarorbiting satellites, especially in the tropics. An instrument
on a single polar-orbiting satellite will observe a spot on
the earth a maximum of twice per 24-hour period. The
SSM/I operational satellite system, which has a 1400-km
swath width and currently has three satellites in operation, can view the earth in the tropics several times a day.
148
K. B. Katsaros et al.
The scatterometer swath width of the ERS-1 and 2
scatterometers only came over a certain spot on the Earth
every three days, which clearly cannot provide operational
support for fast-changing weather systems such as tropical cyclones. A future rain radar system employing several satellites has been proposed, the Global Precipitation Mission (GPM).
The RADARSAT-1 is a polar-orbiting satellite which,
when operated in the ScanSAR-wide mode, does provide
adequate resolution at 100 m. For ScanSAR-wide mode,
the swath width is nominally 500 km. The next generation Canadian SAR, RADARSAT-2, will be left or right
steerable and thus will be able to provide more frequent
views, otherwise being limited to three-day repeats as with
the ERS satellites. Nonetheless, if the goodwill is present
to serve the need of the community, such as illustrated by
the new Canadian program, “Hurricane Watch,” where
the operators have committed themselves to obtaining all
possible RADARSAT-1 SAR images of Atlantic hurricanes, especially as they approach the American continent, such data will provide additional and new information that could improve a forecast even today and certainly will contribute to deeper understanding through
research with these special data. Tropical cyclones are
also examined inside the storm by using data from the
NOAA reconnaissance aircraft. These opportunities will
allow better understanding of the contributions that SARderived surface wind data can make towards identifying
storm intensity, asymmetry, and other important characteristics. As these remote sensing techniques become
proven and dependable, they can then be employed globally to typhoons, Indian Ocean cyclones, and cyclones
around Australia. Polar-orbiting satellites view the globe
indiscriminately. Limitations only involve either tape recorder space or the availability of direct readout receiving stations. In addition, cooperative agreements between
various meteorological and space agencies internationally must be established. Fortunately, the evidence is
steadily mounting that such sharing of resources and expertise is mutually beneficial and is becoming the norm.
An aspect of the remote sensing effort that is purely
technological, but which has been a requirement as important as the remote sensing of the physical variables, is
the data collection, choice of media for archiving, and
the processing and dissemination of the data. Here there
are still some advances required for scatterometer, SAR,
and rain radar data to reach optimal usefulness for forecasters. For fast developing weather systems, such as
tropical cyclones, the data must reach the forecast office
within 3 hours of collection to have real impact. This short
time delay has not always been achievable.
Another avenue of data use is, of course, assimilation of the information for each and every operating sensor into a continuously running model in four dimensions
(three space coordinates and time). For a well-formulated
cyclone model and well understood translation from radiance or backscatter power to the desired variable for
the model, there is hope for “no surprises” from these
storms in the future. Much more basic research, inventive development, and a will to persevere will bring that
utopian dream to pass. All elements for that to happen
exist.
Acknowledgements
The research of Kristina Katsaros was performed at
NOAA’s Atlantic Oceanographic and Meteorological
Laboratory with partial support from NASA grant
UPN261-75, Order for Supplies and Services (W-19330)
and NASA grant RADARSAT-0000-0080. The research
of Timothy Liu was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract
with NASA. Assistance by Gail Derr with production of
the manuscript is gratefully acknowledged.
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