Tropical cyclone morphology from spaceborne synthetic aperture radar
1 2 3 4 Xiaofeng Li1, Jun A. Zhang2, Xiaofeng Yang3, William G. Pichel4, Mark DeMaria4, David Long5,
and Ziwei Li3
1
5 2
6 3
7 IMSG at NESDIS, NOAA, Camp Springs, Maryland, USA
Hurricane Research Division, AOML, NOAA, Miami, Florida, USA
Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China
4
8 5
9 10 STAR, NESDIS, NOAA, Camp Springs, Maryland, USA
Electrical and Computer Engineering Department, Brigham Young University, Provo, Utah
USA
11 12 13 14 Submitted to BAMS
15 16 Apr. 2012
17 18 19 1 1 Abstract
2 In 2008, the Canadian Space Agency sponsored the RADARSAT Hurricane Applications Project
3 (RHAP), for researching new developments in the application of RADARSAT-1 synthetic
4 aperture radar (SAR) data and innovative mapping approaches to better understand the dynamics
5 of tropical cyclone genesis, morphology and movement. Although tropical cyclones can be
6 detected by many remote sensors, SAR can yield high-resolution (sub kilometer) and low-level
7 storm information that cannot be seen below the clouds by other sensors. In addition to the wind
8 field and tropical cyclone eye information, structures associated with atmospheric processes can
9 also be detected by SAR. We have acquired 161 RADARSAT-1 SAR images through RHAP
10 between 2001 and 2007. Among these, 73 images show clear tropical cyclone eye structure. In
11 addition, we also acquired 10 images from the European Space Agency’s ENVISAT SAR
12 between 2004 and 2010. Both Atlantic hurricanes and Pacific typhoons are included.
13 In this study, we analyze these 83 (73 RADARSAT-1 and 10 ENVISAT) images with tropical
14 cyclone eye information along with ancillary tropical cyclone intensity information from the
15 archive to generate tropical cyclone morphology statistics. Histograms of wave number
16 asymmetry and intensity are presented. The statistics show that when the storm has higher
17 intensity, the tropical cyclone eye tends to become more symmetric, and the area of the tropical
18 cyclone eye, defined by the minimum wind area, tends to be smaller. Examples of fine-scale
19 structures within the tropical cyclone, i.e., eye-eyewall mesovortices, arc clouds, double eyewalls,
20 and abnormally high wind or rain within eyes, are presented and discussed.
2 1 Capsule
2 We analyze the sea surface imprints of 83 hurricanes imaged by spaceborne synthetic aperture
3 radar. Hurricane morphology including eye structure, mesovortices, rain band, arc clouds and
4 unusual observations, i.e., high wind within eye, are presented.
3 1 1. Introduction
2 Ever since the launch of the first generation of meteorological satellites in the 1960’s, Atlantic
3 tropical cyclones and western Pacific counterpart typhoons have been extensively monitored
4 from operational polar-orbiting and geostationary satellite sensors. The striking tropical cyclone
5 cloud pictures taken by these conventional weather satellites have appeared in many
6 journal/magazine covers, newspapers and TV programs. These images are usually acquired by
7 passive remote sensing instruments operating in the visible (Vis) and infrared (IR) bands. What
8 we view from these images is the cloud top structure of the tropical cyclones at kilometer spatial
9 resolution. However, the intense air-sea interaction near the ocean surface cannot be directly
10 revealed by these Vis-IR satellite images. With the advance of spaceborne microwave remote
11 sensing, microwave data are also used extensively for tropical cyclone analysis. The advantage
12 of microwave data such as Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall
13 Measuring Mission (TRMM) imagery and QuikSCAT and Advanced Scatterometer (ASCAT)
14 scatterometer data is that they can see through most clouds and make operational measurements
15 at the air-sea interface. The spatial resolution of these measurements is usually in the range of
16 kilometers to tens of kilometers. In this study, we analyze a group of high resolution images
17 acquired by spaceborne synthetic aperture radar (SAR) to obtain even higher resolution. The
18 main advantage of SAR over existing passive microwave imagery and scatterometer data is the
19 high spatial resolution usually ranging from 10 to 100 m.
20 In 1978, the first spaceborne microwave SAR onboard the National Aeronautics and Space
21 Administration (NASA) SEASAT was launched (e.g., Fu and Holt, 1982). Since then, tropical
22 cyclones have also been observed on SAR images. A SAR sensor differs from Vis-IR sensors in
4 1 that it is an active microwave radar that emits radar pulses which can penetrate through clouds.
2 SAR then receives the radar backscatter, quantified by the physical value known as the
3 normalized radar cross section (NRCS), from the ocean surface return, a process quite similar to
4 the QuikSCAT scatterometer wind retrieval. As a result, a SAR image shows the sea surface
5 imprint of a tropical cyclone, not a tropical cyclone cloud top image. For a given radar position,
6 look angle and direction, NRCS is related to the surface roughness and is affected by sea surface
7 winds (both direction and speed), rain roughening of the surface, waves, and other atmospheric
8 and oceanic processes that modulate the sea surface Bragg wave and surface wave spectra (e.g.,
9 Valenzuela, 1978). While the imaged NRCS is also affected by attenuation and scattering of rain
10 in the atmosphere (e.g., Nie and Long, 2008), the primary signal is from the surface. This allows
11 us to extract information right at the air-sea boundary where the most intense air-sea interaction
12 happens. Compared to conventional Vis-IR sensors, SAR has higher resolution (<100m spatial
13 resolution). Its swath (450 km for ScanSAR mode images) is usually large enough to cover an
14 entire tropical cyclone. Another advantage is that microwave SAR can image the ocean surface
15 under all weather conditions, day and night. With the increasing number of SAR satellites that
16 have become available since the early 1990’s, tropical cyclones are frequently observed in SAR
17 images, such as those obtained from the European Space Agency’s (ESA) ERS-1/2 and
18 ENVISAT satellites; and the Canadian Space Agency’s (CSA) RADARSAT-1/2 satellites,
19 among others. The disadvantages of SAR are its data availability and high cost. As of 2012, there
20 is no governmental operational SAR mission in space. However, operational SAR missions are
21 being planned by both ESA (Sentinel-1) and CSA (RADARSAT Constellation Mission), with 5
22 satellites scheduled for launch in the coming years. This constellation of operational SAR
23 satellites will, for the first time, provide tropical cyclone researchers and forecasters with the
5 1 timely access to SAR images necessary to stimulate broader and deeper investigation of tropical
2 cyclones, especially at the air-sea boundary.
3 In the literature, extracting quantitative tropical cyclone information from SAR images has been
4 a focus of several studies over the past decade. Scientists have tried to use limited SAR images
5 acquired from different SAR missions to understand tropical cyclone eye structure, oceanic swell
6 waves, wind rolls, and tropical cyclone wind speeds. Friedman and Li (2000) characterized the
7 ocean surface response to tropical cyclone wind and rain from 2 RADARSAT-1 SAR images
8 covering Hurricane Bonnie (1998). Katsaros et al. (2000) analyzed 4 RADARSAT-1 SAR
9 hurricane images and discovered the 3-6 km wavelength roll vortices associated with the
10 secondary circulations between the main rain bands of a hurricane. Li et al. (2002) detailed the
11 refraction of hurricane-generated oceanic swell waves at the Gulf Stream north wall. Du and
12 Vachon (2003) developed a wavelet technology to extract hurricane eye information from 8
13 RADARSAT-1 SAR images. Limited case studies by Horstmann et al. (2005) and Shen et al.
14 (2006) showed the capabilities of using single polarization RADARSAT-1 SAR for high
15 resolution wind speed mapping with existing geophysical model functions (GMF). Yang et al.
16 (2011) later showed that the wind retrieval accuracy is, however, highly sensitive to the NRCS
17 calibration accuracy. The NRCS errors are from both SAR instrument calibration and from
18 differences in receiving/processing infrastructure (i.e., different satellite ground stations and
19 SAR processors). For high winds over 20 m/s, in storm or hurricane conditions, the 0.5-1.0 dB
20 SAR calibration errors, which are comparable to the current ENVISAT and RADARSAT-1 SAR
21 instrument NRCS calibration errors, induce very large wind retrieval errors, due to the saturation
22 of the SAR wind GMF. Recent studies (Vachon and Wolfe, 2011; Zhang and Perrie, 2011;
23 Zhang et al., 2011) showed promising results by using low-noise-floor C-band polarimetric
6 1 RADARSAT-2 data for better wind estimation at high wind range. Reppucci et al. (2010)
2 developed a tropical cyclone intensity retrieval method and applied it to a Hurricane Katrina
3 (2005) image acquired by the ESA ENVISAT and then used 5 additional hurricane images to
4 validate the results.
5 Sporadic case studies from previous research shed light on the potential of using SAR-derived
6 information for tropical cyclone research, but the small number of case studies do not provide a
7 complete view of the sea surface response to tropical cyclone wind forcing. As we analyze more
8 SAR images, we often find inconsistencies from both an image segmentation/classification and a
9 physical retrieval point of view indicating that the results from tropical cyclone case studies
10 cannot be generalized. In this study, 161 RADARSAT-1 SAR tropical cyclone images over a 10
11 year span had been investigated. Among these, 74 contain complete tropical cyclone eye
12 structure. We also acquired 10 ENVISAT SAR tropical cyclone images from ESA. In this study,
13 we analyzed the 83 SAR tropical cyclone images to generate the first set of SAR-derived
14 statistics on ocean surface response to tropical cyclones. The morphology of the tropical cyclone
15 eye in terms of shape and size distribution is presented and discussed within the context of
16 tropical cyclone dynamics. In addition, examples of detailed atmospheric phenomena generated
17 within tropical cyclones, including eye/eyewall, rain band, boundary layer rolls, arc cloud, and
18 meso-vorticies (Fig. 1), are presented and discussed.
19 2. SAR imagery and ancillary data
20 All 73 RADARSAT-1 SAR images used in this study are processed by CSA. The images are
21 georeferenced ScanSAR wide beam (SCW) products with a pixel spacing of 50 m (range) x 50 m
22 (azimuth). RADARSAT-1 provides horizontal-transmit and horizontal-receive (HH polarization)
7 1 data. The spatial resolution and swath of a SCW image are 100 m and 500 km, respectively. The
2 images are acquired during Northern Hemisphere summer months (May – October) between
3 2001 and 2007. Among these, there are 25 typhoons and 38 hurricanes. The 10 ENVISAT SAR
4 images are provided by ESA. One image is acquired in April, and the rest are acquired in August
5 and September between 2004 and 2010. There are 5 typhoons and 5 hurricanes. Eight ENVISAT
6 SAR images are wide swath mode (WSM) images with a medium resolution of 150 m and a
7 swath of 405 km, at HH or vertical-transmit and vertical-receive (VV) polarization. The other 2
8 images are image mode data; one has a spatial resolution of 30 m (image mode precision - IMP)
9 and the other has a spatial resolution of 150 m (image mode medium - IMM). The swath width
10 of both is 100 km.
11 The storm intensity data are obtained from HURDAT (North Atlantic Hurricane Database) best
12 track data for hurricanes and RSMC (Japan meteorological agency Regional Specialized
13 Meteorological Center Tokyo) best track data for typhoons. The original RSMC wind data is a
14 10-min averaged measurement (Knapp et al., 2010). In this study, the 6-hourly wind product is
15 used. Both best track datasets are ASCII (text) files containing the 6-hourly (0000, 0600, 1200,
16 1800 UTC) center locations (latitude and longitude in tenths of degrees) and intensities
17 (maximum 1-minute surface wind speeds in knots and minimum central pressures in millibars)
18 for tropical storms and hurricanes/typhoons. Among the 83 tropical cyclones captured at the
19 SAR imaging time (not the maximum intensity the tropical cyclones reached during their life
20 span), 1 is a tropical depression, 19 are tropical storms, 29 are Category 1 hurricanes, 28 are
21 Category 2, 1 is Category 3, 3 are Category 4, and 2 are Category 5 storms. This wide range of
22 intensities allows us to investigate how the shape and size of the tropical cyclone eye as observed
8 1 in the SAR images are relate to the intensity of a storm. We list all basic characteristics of these
2 cyclones derived from the SAR images in Table 1.
3 3. Tropical cyclone eye morphology and dynamics
4 Understanding the asymmetric dynamics of intense vortices such as tropical cyclones is crucial
5 for understanding the physical mechanisms that control vortex evolution and intensity change.
6 During the past decades, asymmetric processes near and within the core of tropical cyclones
7 have been extensively studied in numerical simulations (e.g., Schubert et al. 1999; Nolan and
8 Montgomery 2000; Kossin et al. 2002; Yang et al. 2007; Rozoff et al. 2009). Observational
9 studies (e.g., Reasor et al. 2000; Kossin and Eastin 2001; Kossin and Schubert 2004, Eastin et al.
10 2006; Aberson et al. 2006, Reasor et al. 2009) also documented the important role of asymmetric
11 vorticity dynamics in explaining some of the physics of tropical cyclone intensity change. The
12 SAR images listed in this study provide us a new tool for investigating the asymmetric structure
13 of the tropical cyclone eye and/or eyewall.
14 The sea surface imprint of tropical cyclones as depicted in SAR images have similarities to the
15 depiction of these storms in Vis-IR cloud images but with much higher resolution. However, we
16 remind the reader that the SAR images are of the surface NRCS while Vis-IR images are optical
17 images of the cloud tops. Figure 2 illustrates a number of cyclone eyes. High winds and rain
18 result in brighter NRCS around the eye, which is darker due to lower winds.
19 There are different shapes of tropical cyclone eyes as shown in Fig. 2. To quantify if the
20 azimuthal wavenumber of the tropical cyclone eye is related to the intensity, we first determine
21 the azimuthal wavenumber for each SAR image. The wavenumber analysis is both quantitative
22 and subjective, based on the shape of the eye. We define an eye of circular shape with concentric
9 1 eyewall as wavenumber 0 (Fig. 3a). The circular eye with asymmetric eyewall as determined
2 from the SAR image is regarded as wavenumber 1 (Fig. 3b). The elliptical-shaped eye is defined
3 as wavenumber 2 (Fig. 3c). The triangular-shaped eye is defined as wavenumber 3 (Fig. 3d). The
4 square- or rectangular-shaped eye is defined as wavenumber 4 (Fig. 3e), and an eye shaped like a
5 pentagon is defined as wavenumber 5 (Fig. 3f). The above definition of the low-wavenumber
6 asymmetry is consistent with previous studies (e.g., Reasor et al. 2000, Kossin et al. 2002). It is
7 shown that a majority of data show wavenumber 1 and 2 asymmetries in the eye, suggesting that
8 the nature of the inner core asymmetry is dominated by these two wavenumbers. The azimuthal
9 wavenumbers of the eye asymmetry are plotted as a function of the storm intensity in Fig. 4a. It
10 appears that there is a tendency for an increase in wavenumber as the tropical cyclone intensity
11 decreases. This relationship suggests that stronger storms tend to be more symmetric in the eye.
12 While the shape of the tropical cyclone eye is a good indication of dynamics related to
13 asymmetric processes and intensity change, the size of the eye can also be an important factor for
14 intensity change. Previous theoretical and numerical studies have tried to understand why and
15 how the tropical cyclone eye is dynamically and thermodynamically formed, and how the eye
16 interacts with the eyewall and the circulation in the outer core region (e.g., Smith 1980; Shapiro
17 and Willoughby 1982; Willoughby 1990). While the size of the eye varies from storm to storm,
18 it is observed that the eye usually contracts during the intensification process. However, the
19 determination of which factors determine the eye size is still an open question. Since the SAR
20 image can detect the structure of a storm through clouds, it provides a good estimate of the size
21 of the eye, and has the potential to improve the understanding of eye size variability.
10 1 In measuring the size of the eye, we first manually select the high NRCS gradient at the tropical
2 cyclone eye boundary and then calculate the low NRCS area in the center of the storm based on
3 its geometric shape. The sizes of the eyes from all the images are calculated and are listed in
4 Table 2. It appears that the size of the eye ranges from hundreds to thousands of square
5 kilometers, with a majority of the values less than 1000 km2. When relating the eye size to the
6 storm intensity, we found that stronger storms tend to have a smaller eye (Fig. 4b). This result is
7 consistent with Kimball and Mulekar (2004) who found that the eye size as measured by aircraft
8 and satellite data tended to decrease with increasing intensity for storms of Category 2 and
9 higher, although for weaker storms the sign of the relationship reversed (weaker systems had
10 smaller eyes). Our finding is also consistent with a recent theoretical study on the effect of the
11 size of the eye on tropical cyclone intensity by Shen (2006). He argued that the potential
12 intensity of a tropical cyclone is sensitive to the size of the storm, in terms of the relative
13 dependence of the surface kinetic energy dissipation and the surface enthalpy flux on the area of
14 the high wind region (i.e. the eyewall). A tropical cyclone with a smaller eye tends to develop
15 into a stronger tropical cyclone, because the reduction in eye size leads to the decrease of the
16 area of high wind region which lowers the kinematic dissipation to offset the generation of
17 kinetic energy due to surface enthalpy flux.
18 4. Atmospheric and oceanic features observed within tropical cyclones
19 4.1 Meso-votices
20 Previous observations have shown that intense transient vorticity features are fairly common near
21 the inside edge of eyewalls of numerous intense storms: The example of TS Debby reported by
22 Marks and Houze (1984) is the first known documentation with airborne Doppler data. Meso11 1 vortices were also sampled in Hurricane Isabel (Kossin and Schubert 2004; Aberson et al. 2006),
2 and analyzed by Reasor et al. (2009) in Guillermo. Such vortical features are also clearly evident
3 in several recent high-resolution cloud-representing model simulations (e.g., Braun et al. 2006,
4 Nolan et al. 2009), and moreover in two-dimensional turbulence-resolving models (e.g., Shubert
5 et al. 1999; Kossin and Shubert 2001). The meso-vortices are believed to be the result of a
6 combined baratropic-baroclinic instability associated with the annulus of high potential vorticity
7 near and within the eyewall cloud. Montgomery et al. (2006) presented unprecedented
8 observational evidence that high-entropy air inside the low-level eye can sustain the storm at an
9 intensity above that predicted by the potential intensity theory of Emanuel (1986). They
10 articulated that the eye-eyewall meso-vortices may be responsible for transferring the high
11 entropy air from the low-level eye to the eyewall. As mentioned before, the use of SAR imagery
12 provides the advantage of observing features through the clouds at very high spatial resolution.
13 The meso-vortex features when seen in a SAR image would provide extra evidence that such
14 features do exist at low-levels below the clouds. Although meso-vortices were found in well-
15 organized cyclones, we also find they exist in much weaker cyclones. Among the 83 images,
16 meso-vortices are observed in 3 weaker cyclones with intensity ranging from tropical storm to
17 Category 2 (Category 1 Erin, 2001/9/11; Category 2 Kenneth, 2005/9/22; Tropical storm Bilis,
18 2006/7/11). An example showing meso-vortices near the center of Tropical storm Bilis is shown
19 in Fig. 5. Estimating the surface winds when the meso-vortices occur compared to non-vortex
20 cases will be a good research topic for the future.
21 4.2 Rain bands and arc clouds
12 1 There is a ubiquitous presence of rain band signatures in SAR tropical cyclone images. There are
2 4 types of signatures of rain bands apparent in the images: dark (2005/8/22), bright (2005/9/5),
3 dark pattern in the inner rain band and bright pattern in the outer rain (2006/7/3), and half dark
4 and half bright (2007/8/19). Examples are shown in Fig. 6. The bright and dark patterns of rain
5 bands are associated with a combination of five physical mechanisms that change the sea surface
6 roughness (e.g., Bliven and Giovanangeli, 1993; Lin et al. 2001).
7 attenuation due to heavy rain; backscattering from rain drops in the air and ice particles; sea
8 surface capillary waves induced by rain; damping of sea surface waves by rain-induced
9 turbulence; and wind gusts. Different mechanisms will increase or decrease the NRCS measured
10 by a SAR, depending on the observation geometry that varies over the image and the local
11 wind/wave conditions (e.g., Nie and Long, 2008). The modulation of C-band NRCS from the
12 first two mechanisms (attenuation and backscattering from rain drops in the air) is small for low
13 rain rates, but can be significant at the high rain rates experienced in hurricanes. These are the
14 primary mechanisms that make rain bands and arc clouds visible in hurricanes (Nie and Long,
15 2007; Nie and Long, 2008). However, there is only limited qualitative understanding concerning
16 which mechanisms dominate the NRCS signal in these SAR observations, as the rain rates are
17 unknown. The variations in the rain band signature are thought to be the result of variations in
18 attenuation and backscatter due to spatial variations in rain intensity coupled with the
19 background wind-induced NRCS.
20 Arc clouds are common features in mid-latitude thunderstorms and mesoscale convective
21 systems (MCSs), although they have only occasionally been noted in tropical cyclone (TC)
22 environments in the past (e.g., Knaff and Weaver 2000). It was not until recently that arc clouds
23 have been reported to consistently form in the tropics in the periphery of these tropical
These mechanisms are
13 1 disturbances and tropical cyclones, having been noticed in visible satellite images (e.g., Dunion
2 et al. 2010). The observed arc clouds have a length on the order of several hundred km and a life
3 span of several hours. Dunion et al. (2010) suggested that arc clouds denote the presence of a
4 density current that forms when dry middle level (~600-800 hPa) air has interacted with
5 precipitation. The convectively-driven downdrafts in the vicinity of arc clouds can reach the
6 surface/near-surface. These downdrafts can bring cool and dry air to the boundary layer that
7 helps stabilize the boundary layer and inhibits convection. It is hypothesized by Dunion et al.
8 (2010) that the processes leading to the formation of arc cloud events can significantly impact an
9 African Easterly Wave (AEW) or tropical cyclone, in particular, the relatively smaller and less
10 developed systems. Among the 83 SAR images, we saw evidence of arc cloud features in two
11 images (Typhoon Guchol and Hurricane Dean, shown in Fig. 6a and 6d). Arc clouds are visible
12 in the SAR NRCS images due to rain effects, both at the surface and in the atmosphere, with
13 some contribution due to modulation of the local winds by rain-induced wind downdraft (Nie
14 and Long, 2008). Since we can estimate the surface wind speed from the SAR image, we can
15 quantify how the arc clouds affect surface winds using future SAR images, especially multi-
16 polarization images, as well as possibly estimate the rain rate. Dunion et al. (2010) proposed that
17 as the arc clouds move away from the convective core region, they tend to create low-level
18 outflow in the quadrant/semicircle of the AEW or TC in which they form, countering the typical
19 low-level inflow that is vital for TC formation and maintenance. Using the SAR images, we can
20 try to test the above hypothesis.
21 4.3 Boundary layer rolls
14 1 Boundary layer (BL) rolls or ‘roll vortices’ can have a significant influence on turbulent
2 exchange of momentum, sensible heat and moisture in the tropical cyclone BL which is essential
3 for hurricane maintenance and intensification (Zhang et al. 2008). Foster (2005) has developed a
4 theory for roll vortices in curved flow at high wind speeds, such as in hurricanes, suggesting that
5 tropical cyclone BL rolls transport high-momentum air from the upper tropical cyclone BL
6 downward (and low-momentum air from the lower tropical cyclone BL upward) and enhance the
7 transport of air-sea flux. Most previous observational studies on rolls in the tropical cyclone BL
8 to date have focused on land falling storms (e.g., Wurman and Winslow 1998, Morisson et al.
9 2005). At this point it remains unclear how frequently BL rolls occur in hurricanes, especially in
10 open-ocean conditions. It is unclear either how tropical cyclone BL rolls modulate the mean and
11 turbulence structure.
12 13 SAR can provide useful information for identifying tropical cyclone BL rolls, because streak
14 patterns in sea surface roughness can be explained by change in surface wind speed due to the
15 formation of BL rolls (e.g., Alpers and Brümmer 1994; Foster 2005, Zhang et al. 2008). There
16 are a number of SAR hurricane images that contain roll information in our database. An example
17 is given in Figure 7a. We extract a full-resolution subimage (Figure 7b) and perform a Fast
18 Fourier Transform (FFT) analysis (Figure 7c) to show the spatial dimension (2-3 km) and
19 orientation direction of the BL rolls within the hurricane. The BL rolls are found to be generally
20 in line with the wind direction. These BL rolls are also related to boundary layer height, as they
21 can be regarded as large eddies expanding the whole boundary layer. With the extensive dataset
22 of SAR images summarized in this work, quantifying the frequency of occurrence, location
23 relative to the storm center and wavelength distribution of the tropical cyclone BL rolls becomes
15 1 possible. Since more and more research aircraft missions have been conducted in both the
2 Atlantic and Pacific tropical cyclones, the chance to obtain SAR images over-passing aircraft
3 observations is larger than before. Our future research activities will include searching for
4 collocated SAR images and aircraft data collected by the NOAA, Air Force and other agencies,
5 to investigate the influence of rolls on the mean and turbulence structure in hurricanes, following
6 the methodology described by Zhang et al. (2008). It is believed that the effects of tropical
7 cyclone BL rolls can be better understood and eventually parameterized with success in
8 hurricane models, through analyzing the concurrent flight-level, dropsonde, Doppler radar data
9 and SAR images.
10 4.4 Storm patterns on land
11 Most of the tropical cyclone SAR images show conventional storm patterns over the ocean.
12 However, there are a few SAR images that have revealed several interesting phenomena that are
13 not well understood.
14 The first example is that two of the SAR images show that the storm systems partially cover the
15 land surface (Fig. 8). The 2003/8/7 (Fig. 8a) image shows Category 3 Typhoon Etau over
16 Kakeroma island, Japan. One can clearly see the single eyewall as a bright circular pattern in the
17 image. This eyewall shows a brighter pattern both over ocean and land (between the two “Ds” in
18 Fig. 8a). The dark patterns (marked as “D” in Fig. 8a) in the image also continue from ocean to
19 the land. In addition, the low backscatter area shows a very well defined typhoon eye over land.
20 Another example is shown in the 2006/8/10 Category 5 Typhoon Saomai image covering the
21 Fujian-Zhejiang coast of China (Fig. 8b). The high spiral tropical cyclone wind pattern is visible
16 1 over ocean. This storm pattern is continuous across the ocean/land boundary and remains the
2 same structure over land. We believe that these NRCS signatures are due to radar scattering and
3 signal attenuation from intense rain in the atmosphere, which is similar over land and ocean.
4 With appropriate validation, the rain source for the signature can be tested since the NRCS
5 signature will be horizontally displaced by the vertical height of the scattering and attenuation
6 (Nie and Long, 2008). This will be a future project.
7 4.5 High wind observed within certain tropical cyclone eyes
8 Within tropical cyclone eyes, the wind speed is usually low, according to tropical cyclone
9 dynamics (Smith 1980). In general, lower NRCS corresponds to lower sea surface roughness,
10 and thus lower wind. Eighty out of the 83 SAR images in this study demonstrate low NRCS calm
11 areas within tropical cyclone eyes. However, four images show abnormally higher NRCS within
12 the eyes of these tropical cyclones eyes (Fig. 9) than that of surrounding areas, indicating higher
13 roughness regions within some tropical cyclone eyes. At the SAR imaging times of these four
14 storms, the intensity varies from Tropical Storm (TS), to Category 1, 2 and 5. Bright eyes appear
15 on both ENVISAT and RADARSAT-1 images. These may be due to rainfall within the eye,
16 anomalously large wave/swell interaction, or abnormally high winds within the eye.
17 18 5. Conclusions
19 Utilization of SAR imagery is a relatively new tool for tropical cyclone research and forecasting
20 due to its limited coverage, lack of operational analysis tools and high cost. All these
21 impediments are in the process of being swept away. NOAA is implementing an operational
17 1 SAR wind operational processing system. ESA and CSA will launch the Sentinel-1 and
2 RADARSAT Constellation Mission SAR missions in 2013-2016. These missions will be
3 operational and data will be free and open. It is important now to develop new capabilities to use
4 SAR data for tropical cyclone research, as it shows detailed dynamical processes within the
5 tropical cyclone system.
6 This study demonstrates the advantage of SAR sensors for the imaging of fine-scale storm
7 patterns on the sea surface beneath the storm clouds. We are able to view the actual ocean
8 surface responses to the storm-forced winds. Different storm eye shapes are categorized and we
9 find that stronger storms tend to be more symmetric in the eye shape. Examples of eye-eyewall
10 meso-vortices are clearly presented because SAR has high spatial resolution (< 100 m). Rain
11 bands and arc clouds are all shown and discussed qualitatively. Quantitative studies will be
12 carried out in the future together with measurements from airplane and in situ instruments.
13 SAR images show a few unusual observations. One is that the storm pattern continues across the
14 land-sea boundary. We conjecture that this is due to rain scattering and attenuation in the
15 atmosphere. The other one is that higher NRCS values are observed within some storm eyes,
16 which is usually believed to be a relatively calm area within the storm system. Possible
17 explanations are rain and abnormally high wind. However, these phenomena cannot be addressed
18 by SAR observation alone. With the increasing number of spaceborne SAR satellites in the next
19 2-3 years, we believe there will be more simultaneous observations of storm systems from
20 different spaceborne, airborne and in situ sensors to help researchers understand these
21 phenomena.
22 18 1 ACKNOWLEDGMENTS
2 RADARSAT-1 SAR images were provided by CSA through the project “Innovative research
3 and development of applications in using RADARSAT-1 hurricane SAR data”. The Envisat
4 ASAR images were provided by the European Space Agency under Envisat projects 141 and
5 6133. The HURDAT best track data are obtained from the NOAA National Hurricane Center
6 (http://www.nhc.noaa.gov/pastall.shtml#hurdat). The RSMC best track data are obtained from
7 the Japan Meteorological Agency (http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-
8 eg/besttrack.html). The views, opinions, and findings contained in this report are those of the
9 authors and should not be construed as an official NOAA or U.S. Government position, policy,
10 or decision.
19 1 References:
2 Aberson, S., M. T. Montgomery, M. M. Bell, and M. Black, 2006: Hurricane Isabel (2003): New
3 insights into the physics of intense storms. Part II. Extreme localized wind. Bull. Amer. Meteorol.
4 Soc., 87, 1335-1347.
5 6 Alpers, W., and B. Brümmer, 1994: Atmospheric boundary layer rolls observed by the synthetic
7 aperture radar aboard the ERS-1 satellite. J. Geophys. Res., 99, 12613-12621.
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26 Figure Captions
Figure1 Schematic plot of tropical cyclone structure and atmospheric phenomena including eye/eyewall, rain
band, boundary layer rolls, arc cloud, and meso-vorticies
Figure 2. Examples of SAR hurricanes with different eye shapes
Figure 3. Examples of SAR hurricanes with different eye wall types
Figure 4. The wavenumber asymmetry and hurricane eye size versus maximum hurricane wind
Figure 5. Hurricane Eye-eyewall mesovotices
Figure 6. Different rain band patterns observed in SAR images
Figure 7. Analysis of boundary layer rolls within hurricanes
Figure 8. Hurricane patterns over ocean/land observed on SAR images
Figure 9. SAR images showing abnormally high roughness area within hurricane eyes
27 Tables:
Table 1. Basic tropical cyclone information derived from 83 tropical storms observed by SAR.
No.
Year
Mon
Day
time
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2002
2002
2002
2002
2002
2002
2002
2003
2003
2003
2003
2003
2004
2004
2004
2004
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
8
9
9
9
9
9
9
10
11
11
5
9
9
9
9
9
10
7
8
8
9
11
9
9
9
10
6
7
7
7
7
7
7
7
8
8
8
8
8
8
9
9
9
9
9
9
9
29
9
11
13
17
26
27
23
26
28
30
2
26
27
28
30
3
13
7
23
7
30
6
17
28
23
6
13
21
23
24
25
28
29
4
5
5
13
22
27
5
7
9
14
18
19
22
01:43:45
03:04:25
22:19:07
10:03:10
08:07:41
21:41:18
13:21:06
03:19:25
21:59:51
09:47:55
01:50:35
11:20:35
09:40:26
22:03:02
23:12:10
11:07:06
22:28:55
12:03:34
09:19:06
22:14:03
09:44:57
20:43:42
09:06:22
01:21:43
09:26:55
21:18:39
09:03:40
20:06:29
19:30:21
23:01:05
09:03:40
20:53:54
22:17:15
21:49:10
22:11:19
09:55:08
21:42:29
02:36:09
20:38:52
11:28:41
21:37:58
09:23:58
21:21:42
11:00:57
03:25:08
15:22:26
03:08:42
48
2005
9
25
03:21:45
wave
number
Eye Wall
type
elliptic eye
undifined eye
rectangular eye
pentagon eye
Asymmetric eye
elliptic eye
elliptic eye
undifined eye
elliptic eye
circle eye
circle eye
weak eye
rectangular eye
circle eye
triangle eye
circle eye
elliptic eye
Asymmetry eye
elliptic eye
rectangular eye
rectangular eye
elliptic eye
circle eye
Bright circle eye
elliptic eye
elliptic eye
circle eye
Asymmetric eye
no eye
Bright eye
Not organized eye
weak eye
Asymmetric eye
weak eye circle
circle eye
sigma shape
elliptic eye
circle eye
elliptic to triangle
circle eye
elliptic eye
weak eye elliptic
weak eye
pentagon eye
Triangle dark eye
Elliptic bright
rectangular eye
Area of
eye
(km2)
1122
N/A
2973
7368
4134
366
3224
756
840
970
494
N/A
1570
1105
212
416
1423
N/A
2363
5829
2385
5512
131
228
3138
715
405
N/A
N/A
243
N/A
N/A
3966
4966
143
1630
1896
704
668
382
366
N/A
N/A
3454
376
1000
6047
2
N/A
4
5
N/A
2
2
N/A
2
1
1
N/A
4
1
3
1
2
N/A
2
4
4
2
0
1
2
2
1
N/A
N/A
1
N/A
N/A
N/A
N/A
0
2
2
0
3
0
2
N/A
N/A
5
3
2
4
circle eye
192
0
single
N/A
mesovortices
single
N/A
single
double
N/A
single
single
double
N/A
single
single
single
single
single
N/A
single
single
single
single
concentric
single
single
single
closed
N/A
N/A
single
N/A
N/A
N/A
single
concentric
single
single
single
single
concentric
Closed Arc
N/A
N/A
single
concentric
single
mesovortices
concentric
eyewall
Cyclone
location
Center
Lat
Center
Lon
Category
Vmax
(knot)
Pressure
(hPa)
Shape of Eye
FLOSSIE
GIL
ERIN
ERIN
FELIX
HUMBERTO
JULIETTE
NARDA
OLGA
OLGA
ALMA
EDOUARD
KYLE
KYLE
LILI
LILI
KYLE
CLAUDETTE
ETAU
KROVANH
FABIAN
LUPIT
IVAN
JAVIER
MEARI
NOCK-TEN
NESAT
HAITANG
NALGAE
FRANKLIN
BANYAN
BANYAN
FRANKLIN
FRANKLIN
HARVEY
MATSA
HARVEY
SANVU
MAWAR
KATRINA
MARIA
MARIA
NATE
OPHELIA
JOVA
JOVA
KENNETH
epa
epa
atl
atl
atl
atl
epa
epa
atl
atl
epa
atl
atl
atl
atl
atl
atl
atl
wpa
wpa
atl
wpa
atl
epa
wpa
wpa
wpa
wpa
wpa
atl
wpa
wpa
atl
atl
atl
wpa
atl
wpa
wpa
atl
atl
atl
atl
atl
epa
epa
epa
19.8
21.3
37.7
38.5
35.2
42.0
20.9
16.0
31.7
32.4
14.1
30.3
28.0
26.2
18.8
19.5
29.0
24.8
28.2
18.4
39.8
26.9
10.9
20.2
29.4
19.7
21.4
21.2
29.6
23.5
31.7
36.7
42.4
31.7
27.2
32.2
18.8
24.6
24.4
32.2
35.4
34.3
33.4
12.8
15.3
14.6
-115.2
-135.3
-64.5
-61.0
-31.6
-56.0
-110.8
-135.7
-56.2
-56.1
-115.5
-79.9
-57.9
-61.1
-75.9
-79.4
-67.9
-92.6
129.1
115.2
-54.6
137.6
-51.5
-111.3
127.4
126.6
133.7
145.1
-73.7
136.3
137.0
-67.2
-58.8
-61.2
122.5
-57.2
125.6
137.0
-84.6
-56.8
-52.7
-52.0
-77.6
-139.3
-142.6
-134.1
I
TD
I
I
I
I
II
TS
I
I
II
TS
I
I
I
I
I
TS
II
TS
II
I
IV
II
I
II
II
I
TS
TS
TS
TS
TS
TS
TS
I
TS
TS
I
II
II
I
TS
I
II
II
TS
75
30
80
70
70
80
90
60
65
75
93
35
73
66
45
60
55
50
82
55
81
80
110
100
65
85
85
68
43
60
55
42
53
50
58
71
41
50
80
99
93
72
47
74
85
102
45
983
1006
976
980
981
977
949
990
977
976
966
1007
982
985
1001
990
994
998
947
971
966
952
952
960
965
945
945
966
988
1001
975
975
995
999
993
963
998
985
950
942
966
981
988
980
973
957
1000
KENNETH
epa
16.0
-139.3
I
65
988
Name
SAR
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
28 49
50
51
2005
2005
2005
9
10
10
30
2
3
13:19:18
01:38:05
13:30:24
OTIS
OTIS
OTIS
epa
epa
epa
20.9
21.4
24.9
-110.6
-111.9
-112.9
I
I
TS
65
68
28
987
980
1003
Triangle dark eye
circle eye
undefined eye
1398
724
N/A
3
1
N/A
52
2005
10
14
09:12:13
KIROGI
wpa
22.6
131.4
II
85
940
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
2005
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2007
2007
2007
2007
2007
2007
2007
2007
2004
2005
2005
2005
2005
2008
2008
2008
2009
2010
10
7
7
7
7
7
8
9
9
9
9
9
9
7
7
8
8
8
8
10
10
8
8
8
9
9
4
9
9
9
9
15
3
11
12
15
24
10
19
20
21
23
25
30
11
13
1
17
19
31
4
20
25
28
30
11
22
18
1
13
14
2
21:04:06
20:53:52
09:35:42
21:29:32
02:36:20
03:12:39
10:02:54
09:30:59
21:52:31
20:17:59
09:10:32
08:10:03
22:38:38
21:13:14
09:33:45
20:57:57
09:50:33
23:17:51
19:42:51
21:33:56
20:25:09
01:52:41
15:51:16
01:24:39
01:46:11
03:44:32
02:31:50
03:59:19
04:23:09
14:22:16
02:44:23
KIROGI
EWINIAR
BILIS
BILIS
BUD
DANIEL
SAOMAI
GORDON
HELENE
YAGI
HELENE
HELENE
XANGSANE
MAN-YI
MAN-YI
USAGI
DEAN
DEAN
FITOW
KROSA
KAJIKI
AERE
KATRINA
TALIM
KHANUN
RITA
NEOGURI
GUSTAV
IKE
KOPPU
EARL
wpa
wpa
wpa
wpa
epa
epa
wpa
atl
atl
wpa
atl
atl
wpa
wpa
wpa
wpa
atl
atl
wpa
wpa
wpa
wpa
atl
wpa
wpa
atl
wpa
atl
atl
wpa
atl
24.1
15.0
19.2
22.4
19.7
15.2
26.8
24.9
25.9
22.9
36.8
43.9
16.0
17.4
27.7
29.7
14.3
17.4
27.8
20.4
26.8
25.5
26.2
21.4
27.5
26.1
18.2
27.0
28.8
20.8
31.0
132.7
133.2
128.6
124.4
-129.8
-138.4
120.2
-52.2
-57.0
144.3
-48.5
-32.4
109.0
124.9
127.6
133.2
-61.0
-77.6
152.9
125.4
142.8
121.0
-88.3
129.2
112.3
-87.0
111.3
-88.4
-94.6
113.8
-75.0
II
II
TS
TS
TS
II
III
II
II
II
I
I
II
II
II
II
II
IV
I
II
II
I
V
II
I
V
I
II
II
I
IV
85
85
55
55
32
89
97
84
73
105
80
63
72
95
79
90
86
125
63
98
92
75
145
95
80
150
80
95
95
65
120
940
950
987
975
1003
970
945
974
960
910
962
966
965
929
942
945
969
936
971
928
942
960
909
901
950
897
965
953
940
975
932
rectangular eye
956
4
rectangular eye
circle eye
double eye
Weak eye
Weak eye
weak eye elliptic
circle eye
elliptic eye
elliptic eye
circle eye
elliptic eye
Asymmetric eye
circle eye
elliptic eye
elliptic eye
Triangle dark eye
rectangular eye
circle eye
rectangular eye
circle eye
circle eye
elliptic eye
circle eye
small eye
circle eye
Bright circle eye
Bright circle eye
circle eye
circle eye
comma shape
comma shape
2560
32
N/A
N/A
N/A
N/A
108
1264
904
981
1913
N/A
370
3063
1530
1632
278
279
3936
1661
90
1568
754
162
379
828
502
1395
2984
770
863
4
0
N/A
N/A
N/A
N/A
1
2
2
1
2
N/A
1
2
2
3
4
1
4
1
1
2
1
1
1
1
1
1
1
N/A
N/A
single
single
N/A
double
eyewall
single
concentric
Mesovortices
N/A
N/A
N/A
single
single
single
single
single
N/A
single
single
single
single
concentric
Arc cloud
single
single
double
single
single
single
single
single
single
single
single
single
double
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
R1
ASA
ASA
ASA
ASA
ASA
ASA
ASA
ASA
ASA
ASA
*: epa: Eastern Pacific; wpa: Western Pacific; atl: Atlantic; R1: RADARSAT-1; ASA: Advanced SAR
**: Saffir–Simpson Hurricane Scales based on wind speed: Tropical Depression (TD: 0-62 km/h), Tropical storm (TS: 63–117 km/h), Category I (118-153 km/h), II (154177 km/h), III (178-209 km/h), IV (210-249 km/h), V (≥ 250 km/h).
29 Table 2. The number of SAR observations showing different tropical cyclone eye shapes
wavenumber
number of SAR
images
Max eye area
(km2)
Mean eye area
(km2)
Min eye area
(km2)
0
1
2
3
4
5
5
20
20
5
9
2
382
2984
3138
1632
6047
5610
176
743
1604
857
2945
4531
32
90
366
212
956
3452
30 Figures:
Figure 1. Schematic plot of tropical cyclone structure and atmospheric phenomena including
eye/eyewall, rain band, boundary layer rolls, arc cloud, and meso-vorticies (not to scale).
31 Figure 2. Examples of SAR hurricanes with different eye shapes (A) Circular eye. Hurricane IVAN, 2004-9-6 09:06:22
(B) Elliptical eye. Hurricane HELENE, 2006-9-20 21:52:31
(C) Triangular eye. Hurricane JOVA, 2005-9-18
03:25:08
(D) Rectangular eye. Typhoon KIROGI, 2005-10-14
09:12:13
(E) Elliptical to triangular eye. Typhoon GUCHOL, 2005-8-22
20:38:52
(F) Triangular to square eye. Hurricane KENNETH, 2005-9-22
03:08:42
(G) Sigma shaped eye, Typhoon MATSA, 2005-8-5 09:55:08
(H) Bright eye, Hurricane RITA, 2005-9-22 03:44:32
(I) Asymmetric eye, hurricane HELENE, 2006-9-25 08:10:03
32 Figure 3. Examples of SAR hurricanes with different eye wall types.
(A) Wavenumber 0. Hurricane IVAN, 2004-9-6
09:06:22
(B) Wavenumber 1. Hurricane KENNETH, 2005-9-25
03:21:45
(C) Wavenumber 2. Hurricane HELENE, 2006-9-20
21:52:31
(D) Wavenumber 3. Hurricane JOVA, 2005-9-18 03:25:08
(E) Wavenumber 4. Typhoon GUCHOL, 2005-8-22 20:38:52
(F) Wavenumber 5. Hurricane ERIN, 2001-9-13
10:03:10
33 Figure 4. The wavenumber asymmetry and hurricane eye size versus maximum hurricane wind
34 Figure 5. Hurricane Eye-eyewall mesovotices
(Tropical storm Bilis, 09:35:42 UTC, 2006-7-11)
35 Figure 6. Different rain band patterns observed in SAR images (All RADARSAT-1 images)
(A) Dark rain pattern. Typhoon Guchol, 20:38:49 UTC, August 22, 2005; (B) Bright rain pattern.
Hurricane Maria, 21:37:58 UTC, September 5, 2005; (C) Dark pattern in inner rain circle, and
bright pattern in outer rain circle. Typhoon Ewiniar, 20:53:52 UTC, July 3, 2006; (D) Brightdark rain pattern in the same rain band. Hurricane Dean, 23:16:40 UTC, August 19, 2007.
Figures 5a and 5d also clearly reveal the signature of arc clouds. Letters “B” and “D” stand for
“Bright” and “Dark”, respectively.
36 Figure 7, Analysis of boundary layer rolls within Typhoon Fitow. (A) is a SAR image acquired at
19:42:51 on August 1, 2007. Full resolution subimage in the white box is shown in (B) and its
two dimensional FFT analysis in (C), shows the dominant wavelength and orientation of the
boundary layer rolls with 180 degree ambiguity. The dominant wavelength is 2560 m and its
direction is 37o (53o) with respect to the satellite flying range (true North) direction.
37 Figure 8. Hurricane patterns over ocean/land observed on SAR images:
(A) Category 3 Typhoon Etau, 09:17:59 UTC, August 7, 2003; (B) Category 5 Typhoon Saomai,
10:02:03 UTC, August 10, 2006.
38 Figure 9. SAR images showing abnormally high roughness area within hurricane eyes.
(A) 2004/9/17 1:21:43, RADARSAT-1, Hurricane Javier (Category 2), Bright circle eye; (B)
2005/7/23, RADARSAT-1, Tropical storm Franklin (TS) Bright eye; (C) 2005/9/22, Envisat,
Hurricane Rita, Bright circle eye; (D) 2008/4/18, Envisat, Typhoon Neoguri (Category I).
39