Rainband feature tracking for wind speeds around typhoon eyes

Transcription

International Journal of Remote Sensing, 2016
http://dx.doi.org/10.1080/01431161.2016.1142690
Rainband feature tracking for wind speeds around typhoon eyes
using multiple sensors
Shuangyan Hea,b, Antony K. Liua,b, Cheng-Ku Yuc, Zhiguo Heb*, Jingsong Yanga,b,
Gang Zhenga, and Ying Chenc
a
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of
Oceanography, State Oceanic Administration, Hangzhou 310012, China; bOcean College,
Zhejiang University, Hangzhou 310058, China; cDepartment of Atmospheric Sciences, National
Taiwan University, Taipei 10617, Taiwan
International Journal of Remote Sensing
(Received 30 December 2014; accepted 5 January 2016)
In this article, five typhoon cases observed by quasi-concurrent satellite-based synthetic aperture radar (SAR) and Moderate Resolution Imaging Spectroradiometer
(MODIS) were studied with a feature-tracking technique. The rainband features
around typhoon eyes were first delineated using wavelet analysis, and then the
wind speeds were estimated by feature tracking using quasi-concurrent multi-sensor
images. It was found that the feature tracking-estimated wind speeds are reasonable
compared with the maximum wind speed reported by the Joint Typhoon Warning
Center (JTWC), which accounts for the radial dependence of wind speed using the
Rankine combined vortex approximation. In a specific case, with the aid of Doppler
radar observations near the northern coast of Taiwan, wind speed estimation based on
the multi-sensor also shows consistent results. This study demonstrates that the local
wind distribution of cyclonic winds around typhoon eyes at different radial distances
from the typhoon centres may be derived from rainband feature tracking using quasiconcurrent multi-sensor images. This technique may offer useful wind information for
typhoon simulations and forecasting.
1. Introduction
The feasibility of the wavelet tracking technique using multiple satellite sensors to
retrieve typhoon characteristics has received justifiable attention (Cheng et al. 2012;
Liu et al. 2014). Several recent articles have reported interesting findings regarding the
tracking of the typhoon eye using multi-sensor images. For example, with the use of
infrared (IR) images from the Multi-functional Transport Satellite (MTSAT) and synthetic aperture radar (SAR) data from Radarsat, Cheng et al. (2012) documented that the
horizontal distances between typhoon eyes at the cloud top and on the ocean surface are
significantly large. Pan et al. (2013) further examined typhoons in the western North
Pacific from 2005 to 2011 using Envisat ASAR, MTSAT, and Feng-Yun (FY)-2 IR
images. They pointed out that the characteristics of vertical wind shear associated with
typhoons might be much more complicated than expected due to a large core tilt of the
eyewall. In addition to the above studies, another important satellite application is
improvement in the estimation of typhoon intensity over the open ocean using multisensor observations (Liu et al. 2014). However, retrieving oceanic winds from satellite
sensors is highly challenging and its reliability is usually hampered by a wide variety of
*Corresponding author. Email: [email protected]
© 2016 Taylor & Francis
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S. He et al.
inherent uncertainties and precipitation effects (Bentamy et al. 2012; Weissman et al.
2012; Chou, Wu, and Lin 2013).
A general consensus is that a sensor used for the study of typhoon intensity should be
one which directly measures wind field. However, no direct measurements of surface
winds are currently available except those by aircraft, at high cost. SAR images with high
spatial resolution ranging from tens to hundreds of metres are considered as one of the
best alternatives. These measure surface roughness inferred from the radar backscattering
of capillary waves on the ocean surface (Liu et al. 2014). Roughness increases with wind
intensity, and the kinematic retrieval of typhoons requires a suitable geophysical model
function under high wind speeds (Brown 2000; Monaldo and Beal 2004). Unfortunately,
this kind of model, with satisfactory accuracy, is still not available (Li et al. 2013; Liu
et al. 2014).
Therefore, tracking and monitoring of ocean features, which have short coherent time
periods, from sequential satellite images such as geostationary or polar-orbit satellites, is
a good option to estimate the extreme wind speeds associated with typhoons. It should be
noted that this requires that the images have very high spatial resolution and short
temporal sampling intervals. Liu and Hsu (2009) presented a new multi-sensor approach
to overcome the long temporal sampling interval associated with a single SAR sensor
while taking advantage of high-spatial resolution SAR images for ocean feature tracking,
to reveal surface drift. In our study, following the SAR data, sequential Moderate
Resolution Imaging Spectroradiometer (MODIS) images and Doppler radar images (if
available near the coast of Taiwan) were used to track a typhoon eye and its surrounding
rainband features to estimate near-surface wind speeds.
Outside the eyewall region, rainbands are one of the most striking and persistent
features of tropical cyclones (Wexler 1947; Senn and Hiser 1959; Willoughby, Marks,
and Feinberg 1984; Gall, Tuttle, and Hildebrand 1998; Cecil, Zipser, and Nesbitt 2002;
Yu and Cheng 2008; Houze 2010; Yu and Tsai 2010; Yu and Chen 2011; Yu and Tsai
2013; Yu and Cheng 2013). Rainband features can usually be found in the inner and
outer regions of a tropical cyclone (Anthes 1982; Wang 2009; Houze 2010; Yu and Chen
2011). A radar reflectivity threshold is commonly adopted to delineate the edge of
tropical cyclone rainbands (TCRs) (Jorgensen and Willis 1982; Barnes et al. 1983;
Barnes and Stossmeister 1986; Yu and Chen 2011) for a better definition of the rainband
envelope. The axis of the rainband is considered the central point between the outer and
inner edges. Rainband-related features around typhoon eyes derived from SAR images
are characterized by a smooth area with low surface roughness, which is similar to the
raincells in SAR images (Atlas 1994a, 1994b; Atlas and Black 1994; Alpers and
Melsheimer 2004; Hsu et al. 2010). Over the ocean, SAR measures signals from roughness components of approximately similar wavelength or scale to SAR. The roughness
components represent wind-generated short waves that range from capillary to short
gravity waves. The strong downdraft associated with rainbands may produce turbulent
mixing in the upper water layer, which attenuates short waves on the ocean surface and
reveals a micro-scale smooth surface (Nystuen 1990; Tsimplis 1992). On a SAR-derived
image in greyscale, the rougher areas (usually with stronger short waves) appear as
brighter pixels and the smoother areas (usually calm surfaces) as darker pixels. Hence,
the rainband may be shown as a dark band on the SAR image. The smooth ocean surface
(darker area) within the range of typhoon influence (usually rough surface, bright-pixel
area) on SAR imagery has been studied extensively. After we observed and compared the
SAR images to Vis imagery in our study, we determined that the smoother areas in the
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former correspond to the less cloudy areas (darker areas as opposed to ambient bright
areas) in the latter. Thus, for a visible channel of MODIS, the rainband may be revealed
as a less cloudy band due to the downdraft of the raincells. Note that SAR observes sea
surface roughness while MODIS observes clouds at the top of typhoons (at a height of
about 10–15 km) by using Vis bands. The tracking of rainbands over the ocean surface is
made possible by coastal Doppler radar (e.g. Yu and Tsai 2010), although typhoon
rainbands are highly dynamic and do not necessarily follow cyclonic winds (Anthes
1982). It should be noted that it is important to confirm that the features we track are the
same on multi-sensor images but at different altitudes. However, the identification of
rainband features is difficult since they are very dynamic. At present, we judge those
features mainly based on our expertise. In fact, if the same features can be observed on
different images obtained from different sensors, their shapes and locations may not be
exactly the same but should be similar even though they were sensed based on different
principles. If they occur in a similar location at a similar time, we assume that they are
the same features. This issue deserves further investigation in the future using multisensor data and cloud modelling.
Since kinematic retrieval under conditions of extreme wind is currently an unresolved
and challenging issue, this study proposes a new approach to estimation of wind speeds
around typhoon eyes by multi-sensors, in an attempt to enhance the capability of typhoon
monitoring and forecasting. Moreover, a unique aspect of this study is to track rainband
features based on two or three different types of sensor, in contrast to previous typhoon
feature-tracking studies using the same sensors from different satellites (Dvorak 1975;
Hasler et al. 1998; Wu, Chou, and Cheng 2003; Piñeros, Ritchie, and Tyo 2008). The
demonstration and relevant results presented below may provide a new possibility for
future applications of multiple sensors and data fusion.
2. Data and approach
Accurate wind speeds around a typhoon eye are very difficult to measure and track
directly, and this has long been an unresolved issue. In this study, we attempt to
estimate the wind speed (at different radial distances from the typhoon centre) by
tracking the rainband features around the typhoon eye using quasi-concurrent
Envisat ASAR (Advance SAR with 150 m resolution, at the C-band, 5.331 GHz)
and MODIS images, as well as by ground-based Doppler radar observations.
MODIS Band 1 (at 645 nm, visible) data with a spatial resolution of 250 m are
used for all typhoon cases studied here. The rainband features are first subjectively
selected visually and then delineated using wavelet analysis of the quasi-concurrent
images.
Wavelet analysis has been widely used in feature extraction from SAR imagery
(Du and Vachon 2003; Jin, Wang, and Li 2014). Wavelet transforms are analogous
to Fourier transforms but are localized both in frequency and time, for example, as
used with internal tide data (Wang, Chern, and Liu 2000). A two-dimensional
wavelet transform is a highly efficient band-pass data filter which can be used to
separate various scales of process, such as that used in a sea-ice study by Liu,
Martin, and Kwok (1997). For effective identification and tracking of common
features in a pair of chosen images, a two-dimensional Mexican-hat wavelet transform is applied to the images with several spatial scales corresponding to the
extracted features. The detailed procedure and flow chart of the wavelet
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S. He et al.
transform-based tracking method have been reported in many published works and
are summarized in one chapter of a book written on wavelet analysis of satellite
images in marine applications (Liu, Wu, and Zhao 2003).
In this study, sequential images from SAR, MODIS, and ground-based Doppler
radar with a short separation time are used for analysis of feature tracking. Based
on our limited observations, we usually select rainband-like features with a length
of 50 km or so, but less than 100 km. In this study, we first consider the selection
of a relatively stable, common feature for both MODIS and ASAR within a short
time interval. The time interval is limited to within the typhoon rotation period
(usually less than one hour), and the shorter the better. If the feature is too big or
too small, and/or the time interval is too long, it may change too much in terms of
the shape and the distance to the typhoon centre during the rotation interval, and so
the radius and rotation angle cannot be determined accurately. Under these conditions, the reliability of our wind estimation would probably be reduced. In view of
this, in this study we use only a representative part of a rainband (instead of the
entire rainband) for feature tracking, which is more easily identified simultaneously
from multi-sensor images. The rainband features around typhoon eyes are first
delineated by wavelet analysis and the skeleton of the elongated rainband feature
and its centre location are derived. Then, based on the information from the overlaying of the ASAR and MODIS/radar images at the eye location, the rotation
angles and radius of these features are determined. The radius is defined as the
distance between the typhoon centre and the skeleton centre of the delineated
features. The moving speed of each feature can be estimated by dividing the rotated
arc distance by the time difference. The procedure is further elaborated in Case 3.1.
It should be noted that, for a given rainband feature, the radius near the sea surface
can be estimated from SAR data, while at the cloud top from MODIS data.
Experience indicates that a difference in the radius between SAR and MODIS
usually occurs, and this discrepancy may result from a number of factors, such as
the inward/outward propagation (with respect to the typhoon centre) and/or the
inherent vertical tilt of the selected rainband features. In view of this uncertainty,
both radii from SAR and MODIS are used for calculation, giving a range of wind
estimates for each selected feature at different altitudes. Finally, the estimated wind
speeds are compared to the Joint Typhoon Warning Center (JTWC) wind data and/
or Doppler radar data for evaluation.
3. Case studies
Five typhoon cases observed by quasi-concurrent SAR and MODIS, and one of these by
quasi-concurrent ground-based Doppler radar, were collected for study, as summarized in
Table 1. The typhoon name, date and acquisition times (in UTC), time difference, eye
separation distance, and linear moving speed of the typhoon eyes for ASAR, MODIS,
and Doppler radar data are listed for reference. It should be noted that the typhoon eyes
were observed on the sea surface by SAR, in the troposphere above the sea surface by
Doppler radar (Dradar), and at the cloud top by MODIS. These ‘eyes’ acquired from
multi-sensors were identified at different heights, and therefore the estimation of eye
transition speed might not be the real movement speed for typhoons, but are merely used
here for the purpose of checking consistency.
Table 1.
radar.
Summary of five typhoon cases observed by SAR, MODIS, and ground-based Doppler
Time
Eye
difference distance
Time (UTC)
No.
5
Typhoon
Case 1 Talim
Case 2 Khanun
Case 3 Sinlaku
Case 4 Aere
Date
30 August 2005
11 September
2005
10 September
2008
25 August 2004
(minutes)
(km)
(m s−1)
01:24:59 02:01:14
01:46:24 02:24:10
36.25
37.77
5.893
7.587
2.710
3.348
01:33:56 02:33:36
59.67
13.830
3.863
20.10
5.43
80.35
9.732
5.628
39.378
8.070
17.260
8.168
ASAR
MODIS
Dradar
01:52:43 02:12:49
01:58:09
Case 5 Matsa
4 August 2005
Eye
speed
01:41:46 03:02:07
3.1. Typhoon Talim (2005)
The rainband feature tracking for Typhoon Talim on 30 August 2005 is first selected for
demonstration, as shown in Figure 1. The eyewall for this case was not very clearly
identified as a perfect circle (i.e. it was asymmetric) because on 30 August it was still
building in strength as a category 2 storm with a maximum wind speed of 35 m s−1.
Hence, we first delineated the typhoon eye using the wavelet method, and then calculated
the barycentre of the delineations as the typhoon eye’s centres, labelled as ‘O’ and ‘O’’
for ASAR and MODIS imagery in Figures 1(a) and 1(b), respectively. Two rainbandrelated features around the typhoon eye were selected and delineated by wavelet analysis
for ASAR and MODIS images, as shown in red in Figures 1(a) and 1(b), respectively.
Their skeletons are further derived as shown in green in Figures 1(a) and 1(b), respectively. To obtain an appropriate reference point to monitor the movement of each rainband feature, we calculate the centre locations of the skeletons, labelled as ‘A’, ‘B’ for
ASAR and “A’’, ‘B’’ for MODIS imagery in Figures 1(a) and 1(b). Thus, ‘OA’ and ‘OB’
(‘O’A’’ and ‘O’B’’), are the radii of rainband features derived from ASAR (MODIS)
imagery, which refer to the distances between the typhoon centre and the skeleton centre
of the delineated features. The overlay of these two features at the typhoon centre on the
Figure 1. Typhoon Talim on 30 August 2005, with two selected features on (a) ASAR, and (b)
MODIS images delineated by wavelet analysis, and (c) the overlay of feature skeletons for features
1 and 2. In (c), ASAR (MODIS) features and radius are indicated in blue (green). The domain size
is 256 km × 256 km.
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S. He et al.
ASAR image is shown in Figure 1(c), with ASAR (MODIS) features and radius
indicated in blue (green).
Rotation angles of 65.4° (AOA’, or AO’A’) and 80.8° (BOB’, or BO’B’) are found
for features 1 and 2, respectively. As described in section 2, the arc distance can be
determined by the angle (AOA’ for feature 1, BOB’ for feature 2) multiplied by the
radius of these features (OA or OA’ for feature 1 on ASAR or MODIS imagery, OB or
OB’ for feature 2). The wind speed around each feature can be estimated by dividing the
rotated arc distance by the time difference (36.25 minutes). For feature 1, the wind
speeds are estimated to be 52.23 and 44.43 m s−1 when using the radii (OA and OA’)
from SAR and MODIS, respectively. For feature 2, the calculated wind speeds are 60.75
and 44.18 m s−1, respectively. The maximum wind speed (MWS) associated with this
typhoon reported by JTWC is approximately 64 m s−1. As shown in Table 2, the radius of
maximum winds (RMW) from JTWC is about 28 km, which is much smaller than that of
the two rainband features. The wind speed decreases gradually with radius, following
roughly an (r/ RMW)−α decay law (the Rankine combined vortex approximation, r being
the radius of a rainband feature in this study), where α ≈ 0.5 (Depperman 1947; Miller
1967; Anthes 1982). Thus the wind speeds at A and B (A’ and B’) where tracking
features are located on the ASAR (MODIS) image can be calculated using MWS from
JTWC, and are indicated as WS_RA (WS_RM) in Table 2. The estimated wind speeds
derived from feature tracking using the radii of MODIS (44.43 and 44.18 m s−1) are
comparable to those from JTWC (36.82 and 41.04 m s−1), while a larger difference
around 20 m s−1 is found between wind speeds from JTWC (33.96 and 35.00 m s−1) and
by using the ASAR radius (52.23 and 60.75 m s−1). As the wind speeds of JTWC are
estimated based on the statistical relationship among cloud organization, and storm
intensity is defined by the maximum surface winds (the Dvorak Technique), it is reasonable that JTWC wind is consistent with MODIS estimations. Since ASAR mainly
observes the sea surface, such a marked difference in wind speed between ASAR and
JTWC estimations may imply that a complex vertical wind profile existed for Typhoon
Talim.
3.2. Typhoon Khanun (2005)
Figure 2 shows the sequential Envisat SAR and MODIS images collected for Typhoon
Khanun on 11 September 2005. This typhoon was moving with the full strength of a
category 4 with sustained winds of 50 m s−1, and its associated eyewall was highly
axisymmetric. Because the rainband features on ASAR and MODIS visible images are
selected visually, sometimes confused features may occur, as shown in this typhoon
sample. Selected rainband features 1 and 2 in the SAR image are observable far away
from and very near the typhoon eye, respectively. But in the MODIS image, feature 2 can
be either very close to the typhoon eye denoted as 2i or slightly outside of the eye as 2o
in Figures 2(b) and 2(c), respectively. Accordingly, two overlays of the features from
ASAR and MODIS images were obtained, as shown in Figures 2(d) and 2(e).
Following the same procedure, two radii estimated from SAR and MODIS data are
used for all selected features. The wind speed for each feature can then be calculated by
the time difference of 37.77 minutes. For feature 1, the estimated wind speeds are
found to be only 19.30 and 20.34 m s−1. For feature 2o, the wind speeds are estimated
to be 31.19 m s−1 and 59.32 m s−1, and for feature 2i, the wind speeds are 30.92 m s−1
and 45.31 m s−1. For this event, the MWS from JTWC is reported to be about 57 m s−1.
84.71
68.18
57.00
24.57
18.92
66.02
49.16
57.31
48.62
44.50
43.96
86.06
Angle
(°)
65.4
80.8
46.4
300.9
298.4
156.58
202.54
27.3
13.7
28.2
14.5
239.56
52.23
60.75
19.30
31.19
30.92
30.51
32.02
19.29
35.75
19.27
36.79
57.10
30.86
33.96
35.00
23.41
47.90
47.90
38.59
42.84
74.60
18.12
44.43
44.18
20.34
59.32
45.31
50.37
48.52
22.60
27.00
36.82
41.04
22.81
34.73
39.58
30.03
34.80
34.19
35.53
ASAR WSPD JTWC WS_RA MODIS WSPD JTWC WS_RM Dradar WSPD
(m s−1)
(m s−1)
(m s−1)
(m s−1)
(m s−1)
MODIS
37.04
/
27.78
9.26
27.78
RMW
(km)
41.15↑
46.30↓
46.30↑
56.58↓
64.30↑
MWS
(m s−1)
JTWC
Note that Dradar refers to the Doppler radar, RMW is the radius of maximum winds, MWS is the maximum wind speed, WS_RA refers to the wind speed where tracking features
are located on the ASAR image, which is calculated using MWS from JTWC, and WS_RM is the wind speed where tracking features are located on the MODIS image, which is
calculated using MWS from JTWC.
Case 5
Case 4
Case 3
Case 2
1
2
1
2o
2i
1
2
1m
1r
2m
2r
1
Case 1
99.58
93.77
54.09
12.92
12.92
39.98
32.45
48.92
48.92
47.31
47.31
65.87
Feature No. ASAR
Radius (km)
Summary of estimated wind speeds by feature tracking based on SAR, MODIS, and radar observations.
Case No.
Table 2.
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S. He et al.
Figure 2. Two selected features on (a) ASAR, (b) and (c) MODIS images delineated by wavelet
analysis, for Typhoon Khanum collected on 11 September 2005. The overlay of the feature
skeletons for (b) and (c) is shown in (d) and (e), respectively.
The WS_RA and WS_RM are calculated correspondingly for features 1, 2o, and 2i, as
shown in Table 2. Relatively weak wind intensities estimated from feature 1, compared
with feature 2 and JTWC, are consistent with the radial position of this feature located
well outside of RMW (about 9 km). Note that feature 2i is located close to the eye’s
edge whereas feature 2o is located further away (cf. Figures 2(b), (c),). By comparing
the speeds using the MODIS radii of features 2i and 2o (59.32 m s−1 and 45.31 m s−1)
with the WS_RM from JTWC (34.73 m s−1 and 39.58 m s−1), we speculate that feature
2i with a lesser difference in wind speed should be used for feature 2 tracking. Similar
to the case of Typhoon Talim, wind speed differences between the ASAR and JTWC
estimations are observed for both features 1 (4 m s−1) and 2i (17 m s−1), which also
suggests that a more complicated vertical wind profile occurred for Typhoon Khanun.
3.3. Typhoon Sinlaku (2008)
Typhoon Sinlaku draped the island of Luzon in the Philippines with some of its rain
clouds on 10 September 2008 at the collection time of the images from ASAR and
MODIS (Figures 3(a) and (b)). Near to the time these images were acquired,
Sinlaku was reported to be a category 2 typhoon with wind speeds of about
51 m s−1. During the day, the typhoon continued to intensify and reached its
MWS of 65 m s−1 which made it a category 4 storm. Two rainband-related features
around the typhoon eye were selected and delineated by wavelet analysis for ASAR
and MODIS images, as shown in Figures 3(a) and 3(b), respectively. Again, their
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Figure 3. Two selected features for Typhoon Sinlaku on 10 September 2008, with (a) ASAR and
(b) MODIS images delineated by wavelet analysis, and (c) the overlay of feature skeletons for
features 1 and 2. The domain size is 256 km × 256 km.
overlay at the typhoon centre by the ASAR image is shown in Figure 3(c) for these
two features.
In this case, the wind speed for each feature around the typhoon eye can be calculated
by the time difference of 59.67 minutes. With such a large separation of time for
tracking, the rainband features have to be highly coherent. For feature 1, the estimated
wind speeds were found to be 30.51 m s−1 and 50.37 m s−1. For feature 2, the tracking
wind speeds were 32.02 m s−1 and 48.52 m s−1. By comparison, the wind speeds from
JTWC were 38.59 m s−1 and 30.03 m s−1 for feature 1, and 42.84 and 34.80 m s−1 for
feature 2. It is interesting that the larger differences between wind speeds by feature
tracking and those from JTWC are observed for MODIS (20 m s−1 and 14 m s−1) instead
of ASAR (8 m s−1 and 11 m s−1). This discrepancy indicates that uncertainty increases
along with the time difference. It may also have resulted from land effects such as when
Typhoon Sinlaku passed over the islands of Luzon in the Philippines, whose inland
topography could have complicated the typhoon’s wind profile and movement.
3.4. Typhoon Aere (2004)
As Typhoon Aere crossed the northern tip of Taiwan on 24 August 2004, it began to
weaken. The typhoon turned west-southwestward on the following day and made its
closest approach to Taipei, passing only 54 km to the city’s northern aspect. The typhoon
turned southwestward later that day, a trajectory that carried it past Xiamen, China early
the following day. Fortunately, the quasi-concurrent ASAR and MODIS images and
ground-based Doppler radar images for Typhoon Aere on 25 August were available and
were collected. Moreover, all of these had a short time interval of within 30 minutes, and
these images make a unique case for feature tracking in this study. Two rainband-related
features are selected and delineated from ASAR, Doppler radar, and MODIS images as
shown in Figures 4(a), 4(b), and 4(c), respectively. The overlays of their rainband-related
features without and with shifting are shown in Figure 4(d) and Figure 4(e), respectively,
to the typhoon centre of the ASAR image. SAR features and radius are indicated in blue,
those of MODIS in green, and those of Doppler radar in red. The analysis indicates a
slight shift in the location of the typhoon centre during the collection period of these
images (Figure 4(d)).
In this case, two features north and south of the typhoon eye are tracked by the same
method between SAR and MODIS, and also between SAR and Doppler radar (Dradar). It
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S. He et al.
Figure 4. Two selected features for Typhoon Aere collected on 25 August 2004. (a) ASAR, (b)
Doppler radar. and (c) MODIS images delineated by wavelet analysis. (d) The overlay without the
eye shifting, and (e) with eye shifting to the eye centre from the ASAR image.
should be noted that the features from SAR and Dradar are quite consistent since they are
both near the ocean surface. The radii are estimated from SAR and MODIS data for both
selected features. The wind speed for each feature can then be computed by the time
difference of 20.10 minutes for MODIS and 5.43 minutes for Dradar from the SAR
acquisition time. For feature 1 for SAR and MODIS, the estimated wind speeds are
found to be only 19.29 m s−1 and 22.60 m s−1, respectively. However, between SAR and
Dradar, the wind speed near the surface is about 35.75 m s−1. For feature 2 for SAR and
MODIS data, the wind speeds are 19.27 m s−1 and 18.12 m s−1 respectively, and for
feature 2 between SAR and Dradar, the wind speed is about 36.79 m s−1.
During the collection time of the satellite/radar images, the MWS from JTWC is
reported to be 46.30 m s−1, which is about 10 m s−1 larger than the maximum wind
estimates from the selected features. Note that, in this case, RMW is not available
from JTWC, so we use MWS for initial comparison. However, the low-level winds
observed by the Dradar located near the northern coast of Taiwan provide strong
evidence to support the reliability of feature-tracking speeds. For example, Figure 5
shows the 0.4° elevation low-level plan position indicator (PPI) scan of radial
velocity and radar reflectivity at 01.52 UTC on 25 August 2004 during feature
tracking. A dipole of large approaching and departing radial velocities across the
typhoon eye highlights the intense cyclonic winds associated with the typhoon
inner-core circulation. The maximum radial velocities adjacent to the eye (i.e. the
eyewall region, Figure 5(b)) were observed to be between 35 m s−1 and 40 m s−1
(Figure 5(a)), which agrees very well with the maximum estimates of winds (i.e.
35.75 m s−1 and 36.79 m s−1) from the feature tracking. Lower tracking speeds
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Figure 5. The 0.4° elevation low-level PPI scan of (a) radial velocity (m s−1), and (b) radar
reflectivity (dBZ) from a coastal Doppler radar of northern Taiwan at 01:52 UTC on 25 August
2004 as the satellite images of the studied rainbands for Typhoon Aere were collected.
obtained from SAR and MODIS may be due to the limitation of applying satellite
measurements to feature tracking when the upper-level cloud patterns are significantly modified by the Taiwanese topography, as in the present case (Figure 4(c)).
Besides, uncertainties may also be introduced by the comparison of features on
SAR and MODIS images that are at different heights.
3.5. Typhoon Matsa (2005)
Typhoon Matsa strengthened to attain peak winds of 46 m s−1 on 4 August 2005. Shortly
after passing over the Japanese island, Matsa began to weaken steadily as it approached
the coast of China. In this study, ASAR and MODIS images collected on this day for
Typhoon Matsa are examined for a rainband-related feature around the typhoon eye. The
overlay of the selected rainband feature for tracking is shown in Figure 6. The radius and
skeleton delineated from the SAR image are shown in blue, and those from MODIS data
in green. The red contour in the figure is the eye boundary delineated from the MODIS
image.
In this case, the wind speed for the feature around the typhoon eye can be calculated
by the time difference of 80.35 minutes. A single rainband feature was selected for
tracking with a relatively large separation time. The estimated wind speeds were found to
be 57.10 m s−1 and 74.60 m s−1. The MWS from JTWC was reported to be 41.15 m s−1,
and the WS_RA and WS_RM were 30.86 m s−1 and 27.00 m s−1, respectively, which is
much smaller than the estimation from the feature tracking. This result is interesting and
in contrast to other typhoon cases with relatively weaker feature-tracking speeds compared to the JTWC wind data. However, the large time interval between the ASAR and
MODIS data (80.35 minutes) makes this result somewhat implausible. A possible
explanation for this result will be elaborated on in the next section.
12
S. He et al.
Figure 6. Overlay of the selected rainband-related feature of ASAR and MODIS images collected
on 4 August 2005 for Typhoon Matsa. The radius and skeleton delineated from SAR images are in
blue, and those from MODIS are in green. The red contour is the eye boundary delineated from the
MODIS image.
4. Discussion and conclusion
In this study, quasi-concurrent satellite-based SAR and MODIS data were collected for
five typhoon cases, and ground-based Doppler radar images were also used for one
special case near Taiwan. By tracking the rainband features around typhoon eyes
delineated using wavelet analysis, local wind speeds have been estimated for five
typhoon cases, as summarized in Table 2. It should be noted that the wind speeds
estimated by feature tracking are really rotational speeds of the selected rainband feature
only, and the typhoon rainbands would probably move more slowly than their embedded
wind speed (Anthes 1982). Despite this uncertainty, the feature-tracking speeds appear
generally consistent with the JTWC-reported wind speeds or the Doppler radar observations (i.e. Case 4). It is suggested that the extreme wind speeds around typhoon eyes may
be retrieved from rainband feature tracking using quasi-concurrent multi-sensor images,
although these sensors measure parameters at different heights.
Moreover, it should be noted that the uncertainty seems to be much smaller when
similar types of features at similar levels are considered. Features from the SAR and
Dradar are quite consistent with each other since they are both near the ocean surface, as
shown in Case 4. However, Dradar can only measure data over the coastal area, while
only satellite-derived data are currently available over the open ocean. SAR and Vis
13
images with high spatial resolution (several to hundreds of metres), cannot usually be
acquired with high temporal repetition.
Because the JTWC data are available for 00:00 UTC, there is a time difference of
1.5–2 h from the ASAR, MODIS, and Doppler radar collection times. Rapid evolution of
typhoon intensity, if any, may also represent some uncertainties in the interpretation of
the comparison. In addition, MWS from JTWC is presumably the wind speed near the
typhoon eyewall (i.e. close to the eye), while the estimated wind speeds by feature
tracking depend on the locations of features that are usually a little further from the
typhoon eye. Thus the estimated wind speeds would most likely be different from MWS,
and the relation between them can only be approximately qualitative. Accordingly, the
estimated wind speeds are expected to be typically lower than those from JTWC at about
the same time (Table 2). However, the estimated wind speed in Case 5 is higher than that
from JTWC. This result might be implausible, but this typhoon was developing with
increasing wind speed as seen from the JTWC wind data reported at 6-hour intervals, as
indicated in Table 2. The increasing and decreasing trends of MWS are indicated with
downward and upward arrows, respectively, in this table.
The accuracy of the wavelet feature-tracking method is limited by several factors,
such as the persistence of the features and the spatial resolution and navigational
accuracy of satellite data. The geometric accuracy of the MODIS and SAR data are
less than 250 m and 100 m, respectively. Because the ASAR image has a resolution of
150 m, there is no serious issue of accuracy. However, the MODIS image has a spatial
resolution of 250 m and so the extracted rainband features may have a geo-location error
of approximately 250 m due to sensor resolution. Because most of the feature contours
are roughly elongated as a rainband type, the skeleton location of the rainband will not be
shifted significantly using the wavelet detection method and the threshold technique to
delineate feature contours. For an average rainband feature of 10 km in length, the
maximum error of the feature centre location is estimated to be around 2 km. The
difficulties of feature tracking using a pair of satellite images are the co-registration of
images and the low signal-to-noise ratio due to the different imaging mechanism for
different sensors. For an acquisition separation of 30 minutes, the uncertainty of wind
speed estimates is only about 1 m s−1.
For the feature-tracking method proposed herein, the rainband features are initially
selected subjectively and visually. Therefore the selection and tracking procedures also
depend on the researchers’ experience and knowledge of typhoon structures and
dynamics. Owing to the lack of field measurements, we usually select the rainbandrelated features from multi-sensor images according to their time intervals, locations, and
shapes. Sometimes we may have more than one option, and in this situation we need to
judge which is more appropriate. Thus the processing, including both the selection of
feature and the estimation of wind speed, must be done carefully with a priori experience
and knowledge. Meanwhile, it should be noted that there may be an additional uncertainty that is caused by our underlying assumptions. For example, the features depicted in
the ASAR and MODIS images could not be the exact ones although we assume that they
are the same in terms of phenomenology and storm-related location. The features
depicted in the ASAR and MODIS images are at different altitudes (implying different
rotational speed), and they might have different evolution (different time-scales and/or
lag times of best correlation).
Finally, it is worth noting that in this study, the feature tracking-estimated wind
speeds are rainband-implied mean wind speeds at different radial distances from the
14
S. He et al.
typhoon eye centres. The rainband-implied mean wind speeds may not be exactly the
same as the mean wind speeds at the rainband locations, as features may rotate at
different speeds with mean wind speed. Further study is needed to justify this and
calibrate the uncertainty with further cases and model results. However, this is the first
study to demonstrate the potential use of data from different sensors to track persistent
features for movement velocity. As satellite data have become increasingly abundant
and computational power has been significantly advanced, there is an urgent need for,
and a rising interest in, the design of an algorithm that integrates sequential data from
multiple sensors. The results of this study are preliminary but have demonstrated the
potential of rainband feature tracking for real-time application of typhoon wind speed
monitoring, which may provide valuable reference information for typhoon simulations
and forecasts.
Acknowledgements
The authors are grateful for the Envisat ASAR data provided by the Dragon-3 project, and
MODIS data by the NASA Goddard Space Flight Center. The Doppler radar data used in this
study were provided by the Taiwan Central Weather Bureau. We would like to thank Mr. Yu
Long for help with programming. This work wass partially performed during AKL’s stay at the
Zhejiang University as a Qiushi Chair Professor.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was partially supported by the National Basic Research Program of China [Nos.
2013CB430300, 2011CB409803, 2011CB403503), National Natural Science Foundation of
China (Nos. 41276028, 41321004, 41306192, 41306035], the State Key Laboratory of Satellite
Ocean Environment Dynamics [No. SOED1206], Zhejiang Natural Science Foundation
[LR16E090001, LY15D060001] and the Ministry of Science and Technology of Taiwan under
Research [Grant MOST103-2111-M-002-011-MY3].
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Rainband feature tracking for wind speeds around typhoon eyes

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