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Vol.11 (2021) No. 3
ISSN: 2088
-
5334
The Verification Significant Wave Height Technique in Indonesian
Waters and Analysis of Low Air Pressure
Eko Supriyadia,b,*, Sri Puji Rahayuc
aDepartment of Geophysics and Meteorology, IPB University, Darmaga, Bogor, 16680, Indonesia
bCenter for Marine Meteorology, Meteorology Climatology and Geophysical Agency (BMKG), Kemayoran, Jakarta, 10720, Indonesia
cSenior Engineering at Center for Research and Development, Meteorology Climatology and Geophysical Agency (BMKG), Kemayoran,
Jakarta, 10720, Indonesia
Corresponding author: *eko.supriyadi@bmkg.go.id
Abstract— A limited number of marine meteorological instruments for making observations in Indonesian waters are problems in
verifying the BMKG-OFS model. The satellite altimetry was selected as a verification tool due to its wide measurement range. The
verification was carried out by adjusting the coordinates, time, and grid of SWH obtained and orbit of the satellite path from the satellite
altimetry to the model and overlaying the models' results as a pattern analysis in July 2018 – June 2019. The next step was a statistical
analysis to determine the performance of the model. The analysis obtained 43% maximum SWH formed due to the low-pressure centers
in the Pacific Ocean. The remaining spreads across the South China Sea, Indian Ocean, Andaman Sea and the Gulf of Australia. This
study revealed that the SWH values from satellites were higher than the model. On every three hourly and monthly bases, the SWH of
the bias, RMSE, and correlation coefficient were equivalent. The lowest bias of 0.26 occurred at 9.00 UTC, the lowest RMSE of 0.48
occurred at 21:00 UTC, and the maximum correlation coefficient of 0.82 occurred at 18:00 UTC. Whereas on a monthly scale, the lowest
bias and RMSE, and the maximum correlation coefficient occurred in November. Based on these results, the BMKG-OFS model can
be used to predict SWH in Indonesian waters. Besides, this verification technique can be an alternative as a new tool to verify maritime
weather in the operational of BMKG.
Keywords— Altimetry; Indonesia waters; low pressure; OFS model; SWH; verification.
Manuscript received 26 Jun. 2020; revised 20 Jan. 2021; accepted 31 Jan. 2021. Date of publication 30 Jun. 2021.
IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
I. INTRODUCTION
Indonesia has a two-thirds area of water and the second-
longest coastline globally, creating the potential resource and
risk that change over time [1], [2]. One of the potentials and
risks is the Significant Wave Height (SWH). SWH is the
mean wave height of one-third of the measured waves [3]. A
good SWH observation is carried out on a ship in the middle
of the sea through the Voluntary Observing Ships (VOS)
program, which has the longest continuity from 1888-present
[4]. However, this method is considered inefficient to be done
daily, covering a wide range of oceans. Thus, other
approaches such as modeling and remote sensing were carried
out. Remote sensing in this study refers to the utilization of
Satellite altimetry.
Satellite altimetry has been used since the 2008s; the latest
use until now is Jason 3, after the termination of Jason-2 on
17 January 2016 [5], [6]. Although there are other satellites
such as SARAL and CryoSat-2, Jason 2 is relatively constant
in measuring and providing data. Satellite altimetry works by
selecting the area underneath based on cycles and passes,
producing location data (longitude and latitude) that is not
constant over time [7]. Therefore, further processing is
required for analysis. It is a challenge to develop a technique
for the data generated to be used further.
Another SWH observation method is modeling. A model
tends to use a mathematical approach and assumptions in the
predictions so that the results tend to be different from reality
[8]. In regard to the use of the model, the Marine
Meteorological Center (MMC) at the Meteorological,
Climatological and Geophysical Agency (BMKG) currently
has a marine weather model (OFS-models) implemented
since the end of 2016, including the SWH predictions. This
model refers to the Wave Watch III (WW3) model, which is
a third-generation wave model developed by the National
Centers for Environmental Prediction (NCEP), the part of the
National Oceanic and Atmospheric Administration (NOAA).
923
Very few satellite altimetry is currently used as verification
tools for the model [9]–[12].
Therefore, a technique is needed in verifying waves
between model-derived data and satellite altimetry
observations. Research conducted by Appendini et. al. [14]
presents a distinctive wave model verification concept
compared to the previous techniques. In the study, the model-
derived data is compared to the altimetry data that
corresponds to the satellite’s orbital position. However, its
application requires a long way in creating a physical
parameters time group as observed from the coordinates of the
grid used. It also requires more than one software.
Reflecting upon the two methods above, this study verifies
the OFS-SWH model towards the satellite altimetry
measurements. By far, there has never been a detailed
verification of the OFS model for the whole year. In addition,
after the verification data is collected, the time the model
provides the best results is not provided. Therefore, this paper
presents a recap of the OFS-SWH model’s verification
towards the satellite altimetry measurements for one full year
(i.e., July 2018 – June 2019) and shows the model's time
shows its best performance from the statistical analysis.
II. MATERIALS AND METHOD
The verification was carried out July 2018 – June 2019 in
the coordinates 900 – 1450 east latitude, 150 north – 150 south
longitude. The data used are altimetry data retrieved from
ftp://eftp.ifremer.fr/, and OFS-model retrieved from
http://peta-maritim.bmkg.go.id/ render/. Both require login
accessed. The verification is limited to the SWH parameter.
The verification method was conducted by adjusting the
coordinates and time of the OFS model and satellite data. In
general, the adjustment was made by making intervals of
coordinates from satellite data to represent the model
coordinates. For satellite, time synchronization was done by
adjusting to the modeling time format with intervals of 3
hours. The next step was to overlay the OFS model results to
find out the similarity in value. Besides, the SWH plotting
time series were performed for each satellite path. It is worth
noting the altimetry data used was the along-track mono-
mission technique, the combination of Jason 2, Cryosat, and
Saral. This technique records all SWH along the satellite path.
Since the altimetry produced non-constant location data
every time result from the cycle and pass trajectory, a grid
structure is needed. In a grid structure, each coordinate range
is given a grid index. This grid-indexing method provides two
advantages: faster processing and a better contour for further
analysis. In this study, the grid size used was 0.50. After
determining the grid values, the coordinates and time
adjustment were performed in the following steps:
A. Satellite Coordinate Settings
At this stage, the longitude coordinate system setting uses
the equation as follows:
lon(n+1)= lonmin +
0.5n
a
n=0
(1)
with
a = lonmax
−
lonmin
0.5 (2)
n ranges from 0 to a-1. The values of the satellite altimeter
coordinate are transformed into a range of values with the
equation:
lon(n+1) ≤ x < lon(n+2)= n + 1 (3)
This method enables a faster and more efficient calculation,
especially over large areas. The same method is performed for
the latitude.
B. Satellite Data Timing Setting
As previously described, the BMKG-OFS time input is in
the interval of 3 hours for all model parameters. Thus, the time
adjustment was conducted by setting all satellite
measurements at 1.5 hours before and after the main time.
This is done to ensure that data from the satellite is following
the main time section. After the grouping, each main time was
given a sequence number from number 1 to finish, adjusting
the full version's main time. This is crucial since the satellite
altimetry only records the water's physical parameters without
an observation of the land.
C. Grid Settings on the Model
The grid settings in the altimetry data is intended to group
the coordinates and data into a particular grid value. The use
of the grid in the model adjusts to the grid generated from the
altimetry data processing. The model's grid settings are
intended to produce smoother images and plot data to be
overlapped with the satellite trajectories. The initial grid of
BMKG-OFS was 0.06250. It was then interpolated to a new
grid of 0.010. The selection of the 0.010 value is based on the
ability of the computer available for a quick calculation. It is
possible to reduce the value of the grid model in order to
obtain accurate data ranges and smoother map contours. The
interpolation equation is as:
f1(x) = f(x0) + f(x1) + f(x1)
x1
−
x0
(x
−
x0) (4)
where:
x and f1(x) is the point to be sought through interpolation.
x0 and f(x0) is the first known data point.
x1 and f(x1) is the second known data point.
D. Model Data Coordinate Setting
The main principle in this setting is similar to point II-A.
However, the calculated number of coordinates adjusts to the
time length of the model since the main time is multiplied by
3.
E. Model Data Timing Setting
In the BMKG-OFS system, the SWH data used was
updated every 00 UTC and 12 UTC. The distinction between
the two lies in the WW3 input with a time difference of 12
hours. In this study, the only data taken is at 00:00 UTC,
which provides the forecast for the next 7 days. Thus, eight
data observations were sufficient. Each observation was 3
hours, representing one day of observation. The remaining
were ignored, for they are the predictions for the next 6 days.
The time setting was not performed since the OFS system's
data will be compared to the 3 hours intervals.
924
F. Statistical Analysis
The previous steps resulted in two main SWH data: the
SWH data from the altimetry observation and that from the
OFS system, which is ready to be overlapped in one map. In
the overlapping process, a maximum of eight types of
verification contour maps will be produced. The types
represent the 3 hour-per-day observation. The results depend
on observations of satellite altimetry over Indonesian waters.
The next step is a validation performed by statistical analysis
through the following equations:
bias = 1
n
m
i
−
o
i
n
i=1
(5)
RMSE =
∑
m
i
−
o
i
n
i=1
n (6)
CC =
∑
o
i
−
o
m
i
−
m
n
i=1
∑
o
i
−
o
2
n
i=1
∑
m
i
−
m
2
n
i=1
(7)
where:
RMSE = Root Mean Square.
CC = Coefficient Correlation.
mi = BMKG-OFS model output.
oi = altimetry measurements.
= the mean of BMKG-OFS model output.
= the mean of altimetry measurements.
III. RESULTS AND DISCUSSION
A. Altimetry and OFS Model
Fig. 1 depicts an example of overlapping satellite altimeter
SWH with the OFS system in the east and west monsoon. The
model-derived data and satellite altimetry data were made
similar to the matrix resolution for the overlapping process.
The model resolution of 881x481 was adjusted to 3001x5501.
The resolution adjustment did not affect the contours
produced as the interpolation used was the linear
interpolation. The linear interpolation refers to the changes in
resolution at one point to be adjusted to the points around it
linearly up to the last point. The Indonesian waters in two
monsoons were generally calm, with an average wave height
below 1 meter. Meanwhile, the wave outside Indonesian
waters was higher. The Pacific Ocean is relatively calmer in
the east monsoon (Fig. 1a) than the west monsoon (Fig. 1b).
Further studies are needed to see the effect of monsoons on
the SWH values in Indonesian waters.
While the lower panel of Fig. 1a and 1b (graph) presents a
time series plot of the model output with satellite
observations. The model-derived output was higher than that
measured by the altimetry satellite. The high values of the
WW3 model are a separate note due to the uncertain factors
that influence it. Observed in detail, the plot from satellite
observations has highly fluctuated since the observations
were made every time; times, the value tended to fluctuate
extremely. This is due to the change in the satellite altimetry
trajectory measurement (maintaining zero compared to nan).
Figures 1a and 1b show 2 and 3 satellite trajectories,
respectively. On 15 July 2018, the fluctuations occurred on
the x-axis at the values of 800 and on 15 January 2019, the
fluctuations occurred on the x-axis at values of 180 and 900.
Fig.1a and 1b are just two examples of the 1358 satellite
trajectories in July 2018 – June 2019.
Appendini et. al. [13] compared the mean of SWH, and
standard deviation of altimetry data using the model obtained
the same location. However, when the pressure center was
low, the maximum SWH value obtained from the altimetry
and model showed different locations. In the next subsection,
the extreme value distribution of SWH due to the low-
pressure center can be obtained from the OFS model and can
be detected by the same altimetry trajectory. In addition, the
total altimetry trajectory that passes Indonesian waters in July
2018 – June 2019 is presented in Figure 2.
Fig. 1 (a) Result of the overlay SWH of the model (contour on the map) with
altimetry satellite (straight colored line) on the top panel and time series plot
of the comparison model with satellite in the west monsoon on the bottom
panel. (b) Same as part a, but in the east monsoon. T he red arrow indicates
change in satellite trajectory (marked by numbers)
It delineates that the total trajectories fluctuate each month,
with the most trajectories of 179 in December and the least of
73 in June. Normally altimetry data will be filled every 3
hours in one day. This means that there will be a minimum of
1 2
3
1 2 3
1
2
1 2
(a)
(b)
Among of data (corresponding to the path satellite)
Among of data (corresponding to the path satellite)
925
73
109 100 110
130
179
133 126 117
92
108
83
0
50
100
150
200
Jul Agt Sep Oct Nov Dec Jan Feb Mar Apr May Jun
240 trajectories per month. However, this rarely happens
since satellite altimetry never crosses at consecutive
observations. The calculation results obtained that higher
percentage SWH observation satellites are higher when
compared to the model results for a weak category (≤ 2 m).
However, for the strong category (>2 m) high percentage
SWH observation of satellite is lower when compared to the
model. This is the absence of territorial divisions based on the
bathymetry range because, as is known generally Indonesian
waters are shallow, and beyond, it is generally deep (see Fig.
1).
Fig. 2 Total number of SWH from satellite altimetry that crossed Indonesian
waters in July 2018 – June 2019
The SWH values at the low-pressure centers around
Indonesian waters. Throughout 2018, the maximum SWH
value detected from the satellite altimetry was 15 meters on
23 November 2018, at 18:00 UTC. On the other hand, the
maximum SWH value from OFS-model occurred in low-
pressure areas. There are also moments when satellite
altimetry crossed just above the low-pressure center, having
different SWH observation values (Table 1). One of the inputs
from the OFS-model is the influence of the wind. If there is a
cyclone (marked by a low pressure) in an area, the waters
traversed by the eye (cyclone) have a much higher SWH value
than the surrounding waters [15]. This can be explained by the
momentum balance and enthalpy exchange from the ocean's
evaporation and then transformed into energy dissipation to
form a low pressure in the atmospheric boundary layer [16],
[17].
Generally, low-pressure centers are characterized by high
SWH in almost evenly manner in each month of 2018. 43%
(10 occurrences) of high SWH occurred in the Pacific Ocean
waters of the Eastern Philippines. The remaining spreads
across the South China Sea, Indian Ocean, Andaman Sea, and
Australia's Gulfs. An interesting incident occurred on 02 April
2019, in which a tropical cyclone named Wallace formed
around the Northern Waters of Australia (100 south latitude)
entered the Banda Sea, Indonesia, on 03 April 2019. It then
turned back to the south due to the Coriolis force [18]. This
cyclone reached its peak on 07 April 2019, marked by a height
of 8 meters SWH, then decayed on 09 April 2019 at a southern
latitude of 150 with an SWH interval of 3-4 meters. The
Wallace cyclone increased the intensity of rainfall in Maluku
and surrounding areas, in addition to causing high waves.
Data compiled from BMKG shows that on 05 April 2019,
moderate-heavy rainfall intensity occurred in Southeast
Sulawesi, Nusa Tenggara Timur (NTT), Maluku, and Papua.
In comparison, the wave height of 4.0-6.0 meters occurred in
southern Rotte Island, Timor Sea of southern NTT, and the
Indian Ocean of southern NTT. A tropical low pressure was
also formed in the Southern Java Sea on 24 January 2019, at
21:00 UTC. Yet, its power decayed five days later due to
insufficient energy.
The tropical cyclone Wallace was still in the safe category
because most of its path occurred in the ocean. Conversely,
the Cyclone Pabuk that occurred on 1 January 2019 had been
affected differently. This cyclone was initially formed in the
central South China Sea at southern latitudes 50 [19]. It then
strengthened and reached the Gulf of Thailand on 2-4 January
2019, with a maximum SWH of 8 meters. In 5 January 2019,
at 06:00 UTC, this cyclone crossed mainland Thailand. At
21:00 UTC, the SWH increased to 7 meters and decays on 06
January 2019 in the Andaman Sea on the same day. The
tropical cyclones crossing the mainland need special attention
since they impact economic losses and damage [20]. Data
collected from [21] revealed that Cyclone Pabuk caused rough
seas, damage to public facilities, and thousands of people
displaced. Referring to research [22], the Northern
Hemisphere (NH) has a higher cyclone frequency but with a
much lower trend than the Southern Hemisphere (SH). This is
because NH has much land as a form of heat exchange with
the ocean; it was the main ingredient for cyclone formation.
Reference [23] shows in more detail the heat transfer of the
sea to the north through the equator contributing to the
warming of the NH seas.
Referring to Table 1, the cyclone with the most tremendous
damage and loss in this study was the Mangkhut that occurred
in the Philippines' western waters with a total economic loss
of $3.7 billion and at least 130 fatalities [24]. It affected
Indonesia with an increase in rainfall in some areas crossed
by the cyclone's tail, such as Sumatra, Java, Kalimantan, to
Papua. This cyclone caused waves up to 4 meters in height in
Indonesia's eastern and southern waters. The occurrence of
tropical cyclones in the hemisphere appears with another
month. In the NH is generally form in June to November with
a peak in August-September (44%). The SH is from
November to April with a peak in January-March (66%).
However, there was no trend in the increase in the global
number of tropical cyclones from 1985-2014, with around 80
tropical cyclones each year worldwide [25].
To add, Table 1 delineates a satellite altimeter trajectory
recorded just above the low-pressure center on 24 November
2018 at 18:00-21:00 UTC. The obtained SWH value for
satellite altimetry measurement was in accordance with the
model, which was 4.5 meters. Whereas on the second
occurrence on 19 January 2019, at 18:00 UTC, the SWH value
of the satellite altimetry measurement was 1 meter, while in
the model, it was 6 meters. The difference suggests the
importance of validation using the altimetry data. The
technique is often applied in validating a model [26] or
validating it with other observation tools [27]. Besides,
Indonesian waters have a relatively low SWH (<1m)
compared to their outer waters. This has generally been in
accordance with forecasts issued by the Indonesian Marine
Meteorological Center, BMKG, except these waters are
affected by tropical cyclones.
926
TABLE I
THE RECAPS OF HIGH SWH DUE TO LOW PRESSURE DETECTED BY THE BMKG-OFS MODEL. THE LIGHT GRAY COLOR SHOWS THE SWH ALTIMETER
MEASUREMENTS THAT HIGHER THAN THE MODEL, WHILE THE DARK GRAY COLOR INDICATES THE ALTIMETER MEASUREMENT THAT WAS JUST ABOVE THE LOW-
PRESSURE CENTER TO THE MODEL
B. Model Performance
A cumulative stacked bar graph is presented to find out the
performance of the SWH-derived model toward the altimetry
data (Fig. 3a). The lowest to highest statistical analyses in a
row are bias, RMSE, and correlation coefficient for all
observations. The lowest bias of 0.26 occurred at 9:00 UTC;
the lowest RMSE of 0.48 occurred at 21:00 UTC, and the
highest correlation coefficient of 0.82 was obtained at 18.00
UTC. The smaller the bias value, the better the output of the
model. The RMSE values indicate the distance or proximity
of the distribution of the model results to satellite
observations. The lower the value, the better the results of the
model used. While the correlation coefficient describes the
closeness of the two results: the greater the value, the closer
the relationship. Thus, it can be summarized that bias, RMSE,
and correlation do not occur at the same time. This is due to
the fluctuation of the SWH value as presented in Table 1. The
correlation coefficient of 0.69-0.82 was categorized as
moderate until a strong positive relationship [28] explained
the diversity of satellite data. In Indonesia, water shows
similar results that during one month (January) also resulted
in a correlation of 0.69 [29]. The verification recapitulation in
July 2018 – June 2019 is presented in Fig. 3b. The lowest to
highest statistical analysis results in a row are bias, RMSE,
and correlation coefficient for the month. This pattern is
exactly the same as the observation time of Fig. 3a. The
lowest bias and RMSE occurred in November. In the same
month, the highest correlation occurred.
Fig. 3 (a) A cumulative stacked bar graph of the statistical analysis of SWH
model verification toward satellite altimetry data every three hour s of
observation. (b) The plotting line of statistical analysis of SWH model
verification of altimetry satellite data in July 2018 – June 2019.
Start End Location
SWH
Height
(m)
Cyclone
name Peak classification Pressure
(hPa)
04/08/2018 18:00 - 9 - - -
10/09/2018 09:00 15/9/2018 18:00 Philippines, Pacific
Ocean
20 Mangkhut Typhoon 905
01/10/2018 06:00 05/10/2018 15:00 Philippines, Pacific 9 Kong-rey Typhoon 900
24/10/2018 00:00 31/10/2018 21:00 Philippines, Pacific 10 Yutu Typhoon 900
01/11/2018 00:00 01/11/2018 21:00 Philippines, Pacific 5 Yutu Typhoon 900
07/11/2018 21:00 08/11/2018 09:00 Flores Sea 10 - -
18/11/2018 06:00 18/11/2018 21:00 Philippines, Pacific 8 Man-yi Typhoon 960
21/11/2018 18:00 23/11/2018 12:00 South Sea China 14 Man-yi Typhoon 960
23/11/2018 18:00 - South Sea China 15 - - -
24/11/2018 18:00 24/11/2018 21:00 Philippines, Pacific 4.5 - - -
09/12/2018 15:00 - 9 - - -
23/12/2018 21:00 31/12/2018 21:00 Philippines, Pacific -
South Sea China
9 Usman Tropical depression 1000
01/01/2019 06:00 06/01/2019 09:00 Gulf of Thailand-
Andaman Sea
9 Pabuk Tropical storm 996
08/01/2019 09:00 - Pacific Ocean 12 - -
17/01/2019 06:00 20/01/2019 21:00 Philippines, Pacific 7 Amang Tropical depression 1004
19/01/2019 18:00 - 6 - - -
24/01/2019 21:00 29/01/2019 03:00 South Sea Java 7 n/a Tropical low 1004
28/01/2019 06:00 30/01/2019 09:00 South Sea Java 6 Riley Severe tropical cyclone 974
21/02/2019 18:00 27/02/2019 06:00 Philippines, Pacific 16 Wutip Typhoon 920
09/03/2019 18:00 - 8 - - -
14/03/2019 03:00 17/03/2019 18:00 Hindia Ocean 8 Savannah Severe tropical cyclone 951
18/03/2019 03:00 23/03/2019 03:00 Australia Bay 9 Trevor Severe tropical cyclone 950
02/04/2019 03:00 08/04/2019 03:00 Banda Sea 8 Wallace Severe tropical cyclone 980
0,30 0,36 0,32 0,26 0,28 0,29 0,29 0,27
0,55 0,56 0,56 0,52 0,52 0,53 0,51 0,48
0,76 0,69 0,76 0,73 0,81 0,75 0,82 0,73
0,00
0,20
0,40
0,60
0,80
1,00
0 3 6 9 12 15 18 21
Time (UTC)
Bias RMSE Correlation
0,00
0,20
0,40
0,60
0,80
1,00
Juli Agt Sept Okt Nov Dec Jan Feb Mar Apr May Jun
Bias RMSE Correlation
(a)
(b)
927
The best combination of statistical analysis was in
November. On the contrary, less ideal statistical results
occurred in June (Fig. 3b). Compared to the total satellite
trajectory (Fig. 2) in the two months, the performance of the
model was not affected by the number of trajectories. Thus,
the model is said to be quite reliable compared to satellite
data. As a comparison, similar results were also conducted on
Jason 2 satellite every month [26]. Obtained the mean bias is
lower than RMSE, with the mean bias almost equal to zero
and RMSE about 0.2-0.4. Furthermore, several recent studies
have compared altimetry satellites with various models, such
as study [30] comparing the ECMWF IFS (European Centre
for Medium-Range Weatyher Forecasts Integrated
Forecasting System) model with Cryosat-2 obtained SWH
correlation in NE Atlantic and Pacific by 0.98 and 0.95,
respectively. In Indonesia, Sulawesi (Celebes) waters
especially, also conducted verification with WW3 model
against Jason-2 by determining several types of Jason-2 data,
namely WIW19, ALES (Adaptive Leading Edge
Subwaveform), and SGDR (Gephysical Data Records Sensor)
of 0.72, 0.74, and 0.55, respectively [31]. The low correlation
in Sulawesi waters is due to low wind speed, which produces
low waves and stronger echo intensity than the surrounding
area [32].
The further processing results showed the filtering
technique resulted in the least difference in coefficient values
obtained. The difference was 1/100 from the initial correlation
coefficient ranging from 0.68 to 0.81. Besides, the method's
application, by ignoring some of the minimum values of
SWH, did not show significant differences in RMSE values
and correlation coefficient [33]. This means that satellite
altimetry is suitable to be used as a real observation of SWH
data. However, it does not cover all areas of water in one
measurement.
IV. CONCLUSION
This study has successfully verified the OFS-SWH model
of satellite altimetry for one the full year (i.e., July 2018 –June
2019). The technique was performed by adjusting the position
and time of the altimetry data to the model. The total
trajectories fluctuated every month and were obtained with
the most trajectories of 179 in December and the lowest of 73
in June. From the whole set of observations, three important
pieces of information was obtained: a high SWH is associated
with the low-pressure center, a high SWH measurement was
detected more through satellite altimetry, and in some cases,
satellite altimetry that passes just above the low-pressure
center has different SWH value to the model. A total of 17
low-pressure centers (July 2018-June 2019) were formed, in
which the Mangkhut tropical cyclone (10 September 2018)
had the greatest damage and loss.
The statistical analysis results were analyzed on a three
hourly and monthly basis. On the three-hourly basis, the
lowest bias 0.26 occurred at 9:00 UTC, the lowest RMSE 0.48
occurred at 21:00 UTC, and the most significant correlation
of 0.82 occurs at 18:00 UTC. While on the monthly scale, the
lowest bias and RMSE were found in November, and the
largest correlation coefficient was in November. In general,
these results are not much different from the verification stage
carried out every month. In conclusion, this technique can be
an alternative as a new tool to verify maritime weather in the
operation of BMKG.
ACKNOWLEDGMENT
A profound appreciation is addressed to MMC, Pusat
Meteorologi Maritim (Pusmetmar), BMKG, Indonesia, and
ifremer, France, to offer the data OFS model altimetry
respectively so that this research can be completed. We are
grateful to the Center for Education and Training, Pusat
Pendidikan dan Pelatihan (Pusdiklat) BMKG, Indonesia to
fund this research with a scholarship scheme.
REFERENCES
[1] I. Alifdini, N. A. P. Iskandar, A. W. Nugraha, D. N. Sugianto, A.
Wirasatriya, and A. B. Widodo, “Analysis of ocean waves in 3 sites
potential areas for r enewable energy development in Indonesia,”
Ocean Eng., vol. 165, pp. 34–42, 2018, doi:
https://doi.org/10.1016/j.oceaneng.2018.07.013.
[2] A. Ribal, A. V Babanin, S. Zieger, and Q. Liu, “A high-resolution
wave energy r esource assessment of Indonesia,” Rene w. Energy, vol.
160, pp. 1349–1363, 2020, doi:
https://doi.org/10.1016/j.renene.2020.06.017.
[3] A. M. Durán-Rosal, J. C. Ferná ndez, P. A. Gutiérrez, and C. Hervás-
Martínez, “D etection and prediction of segments containing extreme
significant wave heights,” Ocean Eng., vol. 142, pp. 268–279, 2017,
doi: https://doi.org/10.1016/j.oceaneng.2017.07.009.
[4] V. G. Grigorieva, S. K. Gulev, and V. D. Sharmar, “Validating Ocean
Wind Wave Global Hindcast with Visual Observations from VOS,”
Oceanology, vol. 60, no. 1, pp. 9–19, 2020, doi:
10.1134/S0001437020010130.
[5] H. Yu et al., “A global high-resolution ocean wave model improved
by assimilating the satellite altimeter significant wave height,” Int. J .
Appl. Earth Obs. Geoinf., vol. 70, pp. 43–50, 2018, doi:
https://doi.org/10.1016/j.jag.2018.03.012.
[6] N. Tran, D. Vandemark, E. D. Zaron, P. Thibaut, G. Dibarboure, and
N. Picot, “Assessing the effects of sea-state related errors on the
precision of high-rate Jason-3 altimeter sea level data,” Adv. Sp. Res.,
2019, doi: https://doi.org/10.1016/j.asr.2019.11.034.
[7] R. Roscher, B. Uebbing, and J. Kusche, “STAR: Spatio-temporal
altimeter waveform retracking u sing sparse representation and
conditional random fields,” Remote Sens. Environ., vol. 201, pp. 148–
164, 2017, doi: https://doi.org/10.1016/j.rse.2017.07.024.
[8] J. Sommer, E. Chassignet, a nd A. Wallcraft, “Ocean Circulation
Modeling for O perational Oceanography: Current Status a nd Future
Challenges,” 2018.
[9] Y. Quilfen and B. Chapron, “Ocean Surface Wave-Current Signatures
From Satellite Altimeter Measurements,” Geophys. Res. Lett., vol. 46,
no. 1, pp. 253–261, Jan. 2019, doi:
https://doi.org/10.1029/2018GL081029.
[10] M. Jiang, K. Xu, and Y. Liu, “Calibration and Validation of
Reprocessed HY-2A Altimeter Wave Height Measurements Using
Data from Buoys, Jason-2, Cryosat-2, and SARAL/AltiKa,” J. Atmos.
Ocean. Technol., vol. 35, no. 6, pp. 1331–1352, Jul. 2018, doi:
10.1175/JTECH-D-17-0151.1.
[11] R. Schneider, P. N. Godiksen, H. Villadsen, H. Madsen, and P. Bauer-
Gottwein, “Application of CryoSat-2 altimetry data for river a nalysis
and modelling,” Hydrol. Earth Syst. Sci., vol. 21, no. 2, pp. 751–764,
2017, doi: 10.5194/hess-21-751-2017.
[12] J. Yang, J. Zhang, Y. Jia, F. Chenqing, and W. Cui, “Validation of
Sentinel-3A/3B and Jason-3 Altimeter Wind Speeds a nd Significant
Wave Heights Using Buoy and ASCAT Data,” Remote Sens., vol. 12,
p. 2079, Jun. 2020, doi: 10.3390/rs12132079.
[13] C. M. Appendini, V. Camacho-Magaña, and J. A. Breña-Naranjo,
“ALTWAVE: Toolbox for use of satellite L2P altimeter data for wave
model validation,” Adv. Sp. Res., vol. 57, no. 6, pp. 1426–1439, 2016,
doi: https://doi.org/10.1016/j.asr.2015.12.015.
[14] C. M. Appendini, A. Torres-Freyermuth, P. Salles, J. López-González,
and E. T. Mendoza, “Wave Climate and Trends for the Gulf of Mexico:
A 30-Yr Wave Hindcast,” J. Clim., vol. 27, no. 4, pp. 1619–1632, Feb.
2014, doi: 10.1175/JCLI-D-13-00206.1.
[15] S. Yang and J. Oh, “Long-Term Changes in the Extreme Significant
Wave Heights on the Western North Pacific: Impacts of Tropical
928
Cyclone Activity and ENSO,” Asia-Pacific J. Atmos. Sci., vol. 54, no.
1, pp. 103–109, 2018, doi: 10.1007/s13143-017-0063-y.
[16] F. Zhang and K. Emanuel, “On the Role of Surface Fluxes and WISHE
in Tropical C yclone Intensification,” J. Atmos. Sci., vol. 73, p.
160309141253007, Mar. 2016, doi: 10.1175/JAS-D-16-0011.1.
[17] L. Zhang and L. O ey, “An Observational Analysis of O cean Surface
Waves in T ropical Cyclones in the Western North Pacific Ocean,” J.
Geophys. Res. Ocean., vol. 124, no. 1, pp. 184–195, Jan. 2019, doi:
10.1029/2018JC014517.
[18] J. Nakamura et al., “Western North Pacific Tropical Cyclone Model
Tracks in Present and Future Climates,” J. Geophys. Res. Atmos., vol.
122, no. 18, pp. 9721–9744, Sep. 2017, doi:
https://doi.org/10.1002/2017JD027007.
[19] C. D. M. Safuan, N. H. Roseli, Z. Bachok, M. F. Akhir, C. Xia, and F.
Qiao, “First record of tropical storm (Pa buk - January 2019) damage
on shallow water reef in Pulau Bidong, south of South China Sea,”
Reg. Stud. Mar. Sci., vol. 35, p. 101216, 2020, doi:
https://doi.org/10.1016/j.rsma.2020.101216.
[20] M. Lenzen, A. Malik, S. Kenway, P. Daniels, K. L. Lam, and A.
Geschke, “Economic damage and spill-overs from a tropical cyclone,”
Nat. Hazards Earth Syst. Sci. Discuss., pp. 1–28, Jan. 2018, doi:
10.5194/nhess-2017-440.
[21] P. Pavara ngkoon et al., “Development of international mirroring
system for real-time web of meteorological satellite data,” Earth Sci.
Informatics, vol. 13, no. 4, pp. 1461–1476, 2020, doi: 10.1007/s12145-
020-00488-z.
[22] J. Song, P. J. Klotzbach, J. Tang, and Y. Wang, “The increasing
variability of tropical cyclone lifetime maximum intensity,” Sci. Rep.,
vol. 8, no. 1, p. 16641, 2018, doi: 10.1038/s41598-018-35131-x.
[23] S. M. Kang, R. Seager, D. M. W. Frierson, and X. Liu, “Croll revisited:
Why is the northern hemisphere warmer than the southern
hemisphere?,” Clim. Dyn., vol. 44, no. 5, pp. 1457–1472, 2015, doi:
10.1007/s00382-014-2147-z.
[24] H.-S. Lo et al., “Impacts of Typhoon Mangkhut in 2018 on the
deposition of marine debris and microplastics on beaches in Hong
Kong,” Sci. Total Environ., vol. 716, p. 137172, Feb. 2020, doi:
10.1016/j.scitotenv.2020.137172.
[25] H. Ramsay, “The Global Climatology of Tropical Cyclones.” Oxford
University Press, 2017, doi:
10.1093/acrefore/9780199389407.013.79.
[26] X. Ye, M. Lin, and Y. Xu, “Validation of Chinese HY-2 satellite radar
altimeter significant wave height,” Acta Oceanol. Sin., vol. 34, no. 5,
pp. 60–67, 2015, doi: 10.1007/s13131-015-0667-y.
[27] A. Carret, F. Birol, C. Estournel, B. Zakardjian, and P. Testor,
“Synergy between in situ and altimetry data to observe and study
Northern Current variations (NW Mediterranean Sea),” Ocean Sci.,
vol. 15, pp. 269–290, Mar. 2019, doi: 10.5194/os-15-269-2019.
[28] N. Gogtay and U. Thatte, “Principles of Correlation Analysis,” J.
Assoc. Physicians India, vol. 65, pp. 78–81, Mar. 2017.
[29] E. Supriyadi, “Verification of Significant Wave Height Ocean
Forecast System (OFS)-BMKG using Altimetry Satellite,” J.
Meteorol. dan Geofis., vol. 19, no. 2, pp. 93–102, 2019, doi:
http://dx.doi.org/10.31172/jmg.v19i2.586.
[30] S. Abdalla, S. Dinardo, J. Benveniste, and P. A. E. M. Janssen,
“Assessment of CryoSat-2 SAR mode wind and wave data,” Adv. Sp.
Res., vol. 62, no. 6, pp. 1421–1433, 2018, doi:
https://doi.org/10.1016/j.asr.2018.01.044.
[31] K. Ichikawa, X.-F. Wang, and H. Tamura, “Capability of Jason-2
Subwaveform Retrackers for Significant Wave Height in the Calm
Semi-Enclosed Celebes Sea,” Remote Sens., vol. 12, p. 3367, Oct.
2020, doi: 10.3390/rs12203367.
[32] X. Wang, K. Ichikawa, and D. Wei, “Coastal Waveform Retracking in
the Slick-Rich Sulawesi Sea of Indonesia, Based on Variable Footprint
Size with Homogeneous Sea Surface Roughness,” Remote Sens., vol.
11, p. 1274, May 2019, doi: 10.3390/rs11111274.
[33] S. A. Myslenkov and A. Chernyshova, “Comparing wave heights
simulated in the Black Sea by the SWAN model with satellite data and
direct wave measurements,” Russ. J. Earth Sci., vol. 16, pp. 1–12, Nov.
2016, doi: 10.2205/2016ES000579.
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