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Investigation of typhoons by using ECMWF re-analysis data

Authors:

Abstract

In this paper the significant wave height (SWH) and the wind field products of ECMWF re-analysis data are used to derive the location of typhoon center, to analyze the temporal and spatial features of the SWH induced by typhoons, and to study the relationships between the SWH and wind speed. The results are compared with merged SWH data from several satellite altimeters (GFO, TOPEX/Poseidon, Jason-1 and Envisat) and wind vectors from QuikSCAT. Typhoon eyes are observed by using SAR and MODIS data. It is shown that (1) the spatial distribution of wind fields from ECMWF re-analysis data is almost in accordance with that of wind fields from QuikSCAT; (2) the spatial distribution of SWH from ECMWF re-analysis data is almost in accordance with that of SWH from merged SWH data; (3) the distribution of higher wind speed and higher wave height are consistent with the SWH and the wind field product of ECMWF re-analysis data; (4) the centers of typhoon waves lag behind the centers of typhoons; (5) the top of typhoon move faster that the bottom in the case of Saomai.
Investigation of typhoons by using ECMWF re-analysis data
Yufang Pan, Jingsong Yang, Juan Wang, Guangjun Xu, Gang Zheng, Junfang Chang, Biao Gong,
Lihong Li
State Key Laboratory of Satellite Ocean Environment Dynamics
Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China
ABSTRACT
In this paper the significant wave height (SWH) and the wind field products of ECMWF re-analysis data are used to
derive the location of typhoon center, to analyze the temporal and spatial features of the SWH induced by typhoons, and
to study the relationships between the SWH and wind speed. The results are compared with merged SWH data from
several satellite altimeters (GFO, TOPEX/Poseidon, Jason-1 and Envisat) and wind vectors from QuikSCAT. Typhoon
eyes are observed by using SAR and MODIS data. It is shown that (1) the spatial distribution of wind fields from
ECMWF re-analysis data is almost in accordance with that of wind fields from QuikSCAT; (2) the spatial distribution of
SWH from ECMWF re-analysis data is almost in accordance with that of SWH from merged SWH data; (3) the
distribution of higher wind speed and higher wave height are consistent with the SWH and the wind field product of
ECMWF re-analysis data; (4) the centers of typhoon waves lag behind the centers of typhoons; (5) the top of typhoon
move faster that the bottom in the case of Saomai.
Keywords: typhoon, ECMWF re-analysis data, data fusion
1. INTRODUCTION
The western North Pacific is the area of the most frequent tropical cyclones strikes all over the world. These severe
typhoons bring drastic impact for the coastal area and ship sailing through powerful winds and torrential rain, as well as
mix the ocean surface and cause upper ocean response along its passage. Cooling responses to a typhoon affect the upper
ocean environment and provide feedback to the typhoon itself [1-2]. In addition to the cooling response induced by a
typhoon, the passage of typhoon also plays a key role in influencing the upper layer marine ecosystem [3-4].
Field observations and numerical simulations are two main methods to monitor typhoons, which are very limited.
Traditional ways for monitoring wind, including field observations and buoy, could be significantly difficult as typhoons
could be affected by weather conditions in the ocean and sea conditions that could be in a complex. And limited buoys
usually failed under bad weather caused by typhoons. Today, all the information about the wave heights induced by
typhoon was collected through numerical simulations [5-6]. With the fast development of satellite remote sensing
technology, satellite could get the best visual observations of typhoons’ appearance and movement.
This paper studies the moving paths of the typhoons and the moving paths of the wave height by using the SWH and the
wind field products of ECMWF re-analysis data, and tries to study the spatial and temporal distribution of the wave
heights induced by typhoon, and the relationships between SWH and wind speed induced by typhoons. The results are
compared with merged SWH data and wind vectors. Some case studies for different typhoons have been investigated to
measure the center distance between the typhoon eye derived from SAR data and that typhoon eye-estimated from the
best-track analysis results from China Meteorological Administration (CMA).
2. DATA AND DATA PROCESSING
The SAR images used in this paper are from the Canadian Space Agency’s (CSA) Radarsat-1, the European Space
Agency’s ERS-2 and ENVISAT. They are used to derive the location of typhoon center; The ScanSAR Wide product
has a pixel size of 50 m × 50 m with nominal image coverage of 500 km.
QuikSCAT Level 3 Daily Gridded Ocean Wind Vectors are from JPL. This dataset contains 0.25° gridded ocean surface
wind vector fields from the NASA SeaWinds scatterometer.
MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications,
edited by Jianguo Liu, Jinwen Tian, Hongshi Sang, Jie Ma, Proc. of SPIE Vol. 8006, 80062H
© 2011 SPIE · CCC code: 0277-786X/11/$18 · doi: 10.1117/12.902137
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ERA-Interim re-analysis data is from ECMWF. It was displayed in 1.5°×1.5° grid and data observation time was at 0, 6,
12 and 18 o’clock every day.
Merged SWH data from satellite altimeters used in this study to increase the temporal and spatial resolutions are
obtained by using Kriging method.
3. CASE STUDIES
3 cases of various typhoons in different years, locations, and condition have been examined for analyze the temporal and
spatial features of SWH induced by typhoons and the importance of directly measures the wind field on the ocean
surface.
3.1 Case studies on the temporal and spatial features of SWH induced by typhoons
The typhoon is warm and has low pressure, so cyclone type convergent field will be formed within the scope of the
ground. According to convergent airflow velocity, the mature typhoon, along the radial direction of the bottom, can be
divided into three areas: (1) the outer ring: from typhoon edge to the outer edge of maximum wind speed, wind speed
increased sharply towards the center, and the wind power reach at the level of 6 or above, with semi-diameter of 200 -
300 km; (2) the center ring: from the outer edge of maximum wind speed to typhoon eye wall, there is typhoon
convection and the most intense area of wind and rain, with semi-diameter of 100 km; (3)the inner ring: typhoon eye
area, wind speed diminishes quickly, with semi-diameter of 5-30 km.
3.1.1 Typhoon Matsa in July 2005
3.1.1.1 The temporal features of the SWH induced by typhoons
The figure 1 shows that on July 28, the ocean to the east of the Philippines was extremely calm, with average wave
height <2m, while small perturbations appeared in the South China Sea, with average wave height of approximately 3m,
which showed the trend of gradually expanding. From July 29 to July 30, due to the generation of the tropical low-
pressure, the average wave height in the South China Sea reached 4m and the ocean to the east of the Philippines was
still calm. On July 31, the perturbations disappeared near the South China Sea. Till 18:00, the average wave height of the
ocean to the east of the Philippines reached 4.8m and tropical low-pressure had formed and gradually evolved into
tropical storm. From August 3 to August 4, tropical storm evolved into a typhoon and the centre of SWH induced by
typhoons had been moving to the northwest. Until 18:00 on August 4, the center of the SWH induced by typhoons had
moved to the east of Taiwan, with average wave height of 9m. On August 5, the centre of typhoon waves moved to the
East China Sea and it is shown that the intensity of typhoon was decreasing, with average wave height of 9.5m. On
August 5, the typhoon had landed and the wave height decreases back to normal, with average wave height <2m. The
temporal features of typhoon waves could be shown by using SWH and products of ECMWF re-analysis data.
3.1.1.2 The spatial features of the SWH induced by typhoons
The figure 2 shows that the distribution of typhoon waves was sub-circular in general, with radius of influence of about
500-600km. Meanwhile, as shown in table 1, comparing the position of the typhoon centers at 00:00 and 12:00 to the
positions of the centers of the wave height, we found that on August 4 and later time, the position of the typhoon centers
are almost in accordance with the centers of typhoon waves where have the highest SWH. However, at other time, there
was greater deviation between them. As shown in table 1, the centers of typhoon waves were generally located on the
right of the typhoon centers. As shown in table 1, the typhoon centers were not the point of the highest wave height.
After passing through the typhoon, the wave heights would continue to grow, of which height became higher and
position changed. As shown in Table 1, both of the centers of typhoon and the centers of typhoon waves moved to the
northwest, only different in the slopes of the direction of movement.
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave hei ght2005/07/28/12
0
1
2
3
4
5
6
7
8
9
10
108oE 117oE 126oE 135oE 144oE
0o
9oN
18oN
27oN
36oN
45oN
longitude
latitude
Significant wave height2005/07/29/12
0
1
2
3
4
5
6
7
8
9
10
108oE 117oE 126oE 135oE 144oE
0o
9oN
18oN
27oN
36oN
45oN
longitude
latitude
Significant wave height2005/07/30/06
0
1
2
3
4
5
6
7
8
9
10
108oE 117oE 126oE 135oE 144oE
0o
9oN
18oN
27oN
36oN
45oN
longitude
latitude
Significant wave height2005/07/31/12
0
1
2
3
4
5
6
7
8
9
10
(a) (b) (c) (d)
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108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave hei ght2005/08/01/00
0
1
2
3
4
5
6
7
8
9
10
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave height2005/08/02/00
0
1
2
3
4
5
6
7
8
9
10
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave height2005/08/02/06
0
1
2
3
4
5
6
7
8
9
10
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave he ight2005/08/02/12
0
1
2
3
4
5
6
7
8
9
10
(e) (f) (g) (h)
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave height2005/08/02/18
0
1
2
3
4
5
6
7
8
9
10
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave height2005/08/03/12
0
1
2
3
4
5
6
7
8
9
10
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave height2005/08/04/12
0
1
2
3
4
5
6
7
8
9
10
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
Significant wave height2005/08/05/12
0
1
2
3
4
5
6
7
8
9
10
(i) (j) (k) (l)
Figure 1. The SWH products of ECMWF re-analysis data during typhoon Matsa at different time.
(a) 2005/07/28/12 (b) 2005/07/29/12 (c) 2005/07/30/06 (d) 2005/07/31/12
(e) 2005/08/01/00 (f) 2005/08/02/00 (g) 2005/08/02/06 (h) 2005/08/02/12
(i) 2005/08/02/18 (j) 2005/08/03/12 (k) 2005/08/04/12 (l) 2005/08/05/12
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/07/28/12
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/07/29/12
0
5
10
15
20
25
30
108oE 117oE 126oE 135oE 144oE
0o
9oN
18oN
27oN
36oN
45oN
longitude
latitude
wind speed 20 05/07/30/06
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/07/31/12
0
5
10
15
20
25
30
(a) (b) (c) (d)
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/01/00
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/02/00
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/02/06
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/02/12
0
5
10
15
20
25
30
(e) (f) (g) (h)
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/02/18
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/03/12
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/04/12
0
5
10
15
20
25
30
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
wind speed 2005/08/05/12
0
5
10
15
20
25
30
(i) (j) (k) (l)
Figure 2. The wind field products of ECMWF re-analysis data during typhoon Matsa.
(a) 2005/07/28/12 (b) 2005/07/29/12 (c) 2005/07/30/06 (d) 2005/07/31/12
(e) 2005/08/01/00 (f) 2005/08/02/00 (g) 2005/08/02/06 (h) 2005/08/02/12
(i) 2005/08/02/18 (j) 2005/08/03/12 (k) 2005/08/04/12 (l) 2005/08/05/12
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Table 1. Typhoon center and the center of typhoon waves during typhoon Matsa
date
typhoon center center of typhoon waves
wave height
LAT°N LON°E LAT°N LON°E
2005080100 13.7 132.7 12.0 136.5 3.93
2005080112 15.5 130.7 15.0 136.5 4.599
2005080200 16.9 129.4 16.5 133.5 4.006
2005080212 19.0 128.0 19.5 132.0 4.094
2005080300 20.6 126.6 21.0 130.5 4.757
2005080312 21.8 125.7 24.0 127.5 6.078
2005080400 23.1 124.9 24.0 127.5 6.731
2005080412 24.6 124.2 24.0 126.0 6.825
2005080500 25.6 123.4 27.0 124.5 7.486
2005080512 27.4 122.2 28.5 124.5 7.7
3.1.1.3 The relationships between SWH and wind speed induced by typhoons
During typhoon Matsa, the average wind field products of ECMWF re-analysis data at 10m above the sea surface every
6 hours was shown in figure 2. On July 28, the average wind speed near the East China Sea was 13m/s or so. On July 30,
the maximal wind speed near the East China Sea reached 20m/s and then gradually weakened. At this time, the average
wind speed to the east of the Philippines was 15m/s or so and then increased to about 20m/s on August 1, tropical low-
pressure was formed. On August 2, the typhoon centre had moved to the east of Luzon Island and typhoon eye was
clearly visible. After that, the typhoon continued to move to the northwest. Until August 5, the average wind speed had
reached to more than 30m/s. As shown in figure 1 and figure 2, there were some corresponding relationships between
wind field and wave field. From July 28 to 30, the average wind speed was smaller and the corresponding wave height as
well. From August 1 to 2, the average wind speed changed slightly and the corresponding wave height as well. From
August 4 to 6, the average wind speed was larger and the corresponding wave height as well.
3.1.1.4 The comparison of spatial distribution of ECMWF SWH and fusion SWH
Merged SWH during Masta was shown in Figure 3. Comparing figure 1 with figure 3, observed on July 29 and 30, the
average wave height in the South China Sea reduced from 4m to 2.5m. The average wave height and the distribution of
ECMWF SWH and Merged SWH were consistent. From July 31 to August 2, tropical low-pressure appeared to the east
of the Philippines and moved to the east of Luzon Island, which also displayed in figure 1 and figure 3 by the same way.
The average wave height in this area was also generally accordant. Figure 3 shows that the wave height in the
intermediate section of typhoon was 0 on August 2, inconsistent with the reality, which may be caused by the fact that
SWH data in this area were not obtained by satellite altimeter that day. As shown in figure 3, the average wave height
induced by typhoon reached maximum on August 4 and then declined. The variation tendency of the wave height in
figure 1 was same as the above mentioned.
3.1.1.5 The comparison of spatial distribution of ECMWF wind fields and QuikSCAT wind fields
According to figure 2 and figure 4, from July 29 to 30, the average wind speed in the South China Sea and to the east of
the Philippines became higher. From July 31 to August 1, the average wind speed to the east of the Philippines reached
about 20m/s and tropical low-pressure generated, which were consistent with show in figure 2 and figure 4. Then
typhoon had been moving to the northwest, landing on Zhejiang on August 5. They are still consistent with those shown
in figure 2 and figure 4.
(a) (b) (c) (d)
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(e) (f) (g) (h)
Figure 3. Merged SWH during typhoon Matsa
(a) 2005/07/29 (b) 2005/07/30 (c) 2005/07/31 (d) 2005/08/01
(e) 2005/08/02 (f) 2005/08/03 (g) 2005/08/04 (h) 2005/08/05
(a) (b) (c) (d)
(e) (f ) (g) (h)
Figure 4. QuikSCAT Wind Vectors during typhoon Matsa
(a) 2005/07/29 (b) 2005/07/30 (c) 2005/07/31 (d) 2005/08/01
(e) 2005/08/02 (f) 2005/08/03 (g) 2005/08/04 (h) 2005/08/05
3.2 Case studies on typhoon eye observations using SAR and MODIS
Conventionally, the typhoon eye observation is from weather satellite using infrared (IR) or visible (VIS) wavelengths,
which is the observation at cloud height about 12 km. However, the location of typhoon eye on the ocean surface may be
quite different from that on the top of clouds due to the vertical wind shear tilt.
Figure 5 shows the images of typhoon Meari from Radarsat SAR collected on Sep 28, 2004 and from MODIS/VIS
collected on Sep 27, 2004.
Figure 5. Typhoon seen by SAR (left) and by MODIS (right)
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3.2.1 Case of typhoon Meari in September 2004
Figure 6 shows the centers of typhoon Meari derived from SAR (blue dot), MODIS/VIS (red dot) images and the best-
track of typhoon Meari from CMA (green line). Typhoon locations are tracked every 6 hours.
The three squares in figure 6 from north to south represent three locations of typhoon eye derived from Radarsat
ScanSAR (collected on Sep 28, 2004) and AQUA MODIS (collected on Sep 27, 2004 and Sep 26,2004 respectively).
The motion of the typhoon is regards as uniform motion when it is between every two dots from the best-track analysis
results from CMA, so we could estimate the approximate distance which typhoon passed by from the time when SAR or
MODIS collected its data. We suppose that the distance between the three dots which is mentioned above and the
location of typhoon at the same collected time are D1D2 and D3. We find that D2 is the longest among them, and D3
is the shortest. At this stage the typhoon intensity from the ocean surface pressure field was steady, and typhoon was also
changing the direction approached to Japan from the South and speeding up. The typhoon eye derived from SAR data
was lagging and dragging behind that the center of typhoon eye from CMA at the same time.
3.2.2 Case of typhoon Saomai in August 2006
Figure 7 shows the centers of typhoon Saomai derived from SAR (blue dot), MODIS/VIS (red dot) images and the best-
track of typhoon Saomai from CMA (green line). Typhoon locations are tracked every 6 hours.
In this case, typhoon Saomai was headed-on to China on August 10, 2006 as shown in the typhoon track map from CMA
data. At this stage, typhoon Saomai is likely to land in east China. Notice that the location of typhoon eye which was
recorded by SAR is exactly on the best-track analysis results from CMA. The landing time observed by SAR is 35
minute later than that from CMA. This means the top of typhoon move faster that the bottom.
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
108
o
E 117
o
E 126
o
E 135
o
E 144
o
E
0
o
9
o
N
18
o
N
27
o
N
36
o
N
45
o
N
longitude
latitude
2006081002
Figure 6. Typhoon Meari Figure 7. Typhoon Saomai
4. CONCLUSIONS
It is concluded from above analysis that (1) the spatial distribution of wind fields from ECMWF re-analysis data is
almost in accordance with that of wind fields from QuikSCAT; (2) the spatial distribution of SWH from ECMWF re-
analysis data is almost in accordance with that of SWH from merged SWH data; (3) the distribution of higher wind speed
and higher wave height are consistent with the SWH and the wind field product of ECMWF re-analysis data; (4) the
centers of typhoon waves lag behind the centers of typhoons; (5) the top of typhoon move faster that the bottom in the
case of Saomai.
Proc. of SPIE Vol. 8006 80062H-6
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Proc. of SPIE Vol. 8006 80062H-7
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... The ozone and water vapor data employed in this research are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Pan et al., 2011), and the ERA5 Reanalysis Data (0.25 × 0.25 • ) (Hersbach et al., 2020;Urban et al., 2021). ERA5 is the fifth-generation ECMWF reanalysis and provides an extended temporal sequence of atmospheric fields featuring both high spatial and temporal resolution. ...
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The boundary layer structure of Tropical Cyclone Kerry (1979) is investigated using composite analysis of research aircraft, surface ship, and automatic weather station observations. The boundary layer was moist, convective, and strongly confluent to the east of the tropical cyclone center but was dry, subsident, and diffluent to the west. The vertical momentum transport in the eastern convective sector of Kerry was around two to three times the surface frictional dissipation. In contrast, the stable boundary layer in the western sector consisted of a shallow mixed layer capped by an equivalent potential temperature minimum and a low-level jet, which underwent a marked diurnal oscillation. Three mechanisms appear to have contributed to the observed asymmetry: 1) a general, zonal distortion arose from cyclonic rotation across a gradient of earth vorticity; 2) a westerly environmental vertical shear produced forced ascent on the east side of the storm and subsidence on the west side throughout the ...
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1] The physical effects of hurricanes include deepening of the mixed layer and decreasing of the sea surface temperature in response to entrainment, curl-induced upwelling, and increased upper ocean cooling. However, the biological effects of hurricanes remain relatively unexplored. In this paper, we examine the passages of 13 hurricanes through the Sargasso Sea region of the North Atlantic during the years 1998 through 2001. Remotely sensed ocean color shows increased concentrations of surface chlorophyll within the cool wakes of the hurricanes, apparently in response to the injection of nutrients and/or biogenic pigments into the oligotrophic surface waters. This increase in post-storm surface chlorophyll concentration usually lasted 2–3 weeks before it returned to its nominal pre-hurricane level.
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1] Clear skies, subsequent to Hurricane Ivan's passage across the Gulf of Mexico in September 2004, provided a unique opportunity to investigate upper ocean responses to a major hurricane. Oceanic cyclonic circulation was rapidly intensified by the hurricane's wind field (59 – 62 m s À1), maximizing upwelling and surface cooling (3 – 7°C) in two large areas along Ivan's track. Upward isothermal displacements of 50– 65 m, computed from wind stress and sea surface height changes, caused rapid ventilation of thermoclines and nutriclines, leading to phytoplankton blooms with peak concentrations 3 – 4 days later. Wind speed changes along Ivan's track demonstrated that the cool waters (20 – 26°C) provided immediate negative feedback to the hurricane's intensity. Although our study focused on a relatively small ocean area, it revealed that mesoscale cyclones, in addition to warm anticyclones, may play an important role in producing along-track hurricane intensity changes. Citation: Walker, N. D., R. R. Leben, and S. Balasubramanian (2005), Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico, Geophys. Res. Lett., 32, L18610, doi:10.1029/ 2005GL023716.
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A coupled hurricane--ocean model was constructed from an axisymmetric hurricane model and a three-layer ocean model. If the hurricane moves at constant speed across the ocean a statistically steady state (in a reference frame moving with the storm) is reached after a few days of simulation time. The steady-state intensity of the hurricane is strongly affected by the interaction with the ocean. This interaction with the ocean can be described as a negative feedback effect on the hurricane's intensity and is called "SST feedback." A large set of numerical experiments was performed with the coupled model to deduce systematically the dependence of the amplitude of the SST feedback effect on a set of model parameters. In the coupled model the SST feedback effect can reduce the hurricane's intensity by more than 50%. Only in cases of rapidly moving storms over deep oceanic mixed layers is the SST feedback effect of minor importance. These results cast a new light on the role of the ocean in limiting hurricane intensity. 1.
A Hindcast Study of Waves Generated by Typhoon 9914
  • Caixin Wang
  • Zhanhai Zhang
  • Keguang Wang
Caixin Wang, Zhanhai Zhang, Keguang Wang, "A Hindcast Study of Waves Generated by Typhoon 9914,"marine science bulletin, 22(1):8-16, (2003).
Numerical Simulation of Typhoon Krovanh and Typhoon-induced Ocean Waves
  • Xiaolin Wei
  • Jiangnan Li
  • Cai Yao
  • Wenshi Lin
  • Anyu Wang
  • Suikun Fong
Xiaolin Wei, Jiangnan Li, Cai Yao, Wenshi Lin, Anyu Wang, Suikun Fong, "Numerical Simulation of Typhoon Krovanh and Typhoon-induced Ocean Waves,"journal of tropical meteorology,23(6):673-678,(2007).