Mapping Atmospheric – Ocean Parameter Risk Index based on

Meteorological Review to Support the Operational Work of Sea Toll

Road Program

Anistia M. Hidayat

1,a

, Ayuna S. Putri

1,b

, Faqih Musyaffa

1,c

, and Ahmad Fadlan

2,d

Undergraduate Program of Meteorology, STMKG, Banten, Indonesia

Lecturer, Department of Meteorology, STMKG, Banten, Indonesia

Keywords: Atmosphere, Ocean, Safety, Sea Toll Road Project.

Abstract: Indonesia is the largest archipelagic state in the world, two-third of its total area is a marine region. It is such

enlightenment when the current government is aware of Indonesia's marine potential and prioritizes policies

that support maritime transport through a concept known as the Sea Toll Road project. According to this

project, safety factors such as environmental hazards need to be put under serious consideration. ECMWF

ERA-Interim Reanalysis for wind data, daily rainfall data from TRMM, and WaveWatch III model data in

2013 have been adjusted to determine and assess the safety risk threshold for each parameter. This information

is used as the basic data for mapping spatial distribution of wind, rainfall, and wave height profile in each

period of the months. The atmospheric–ocean parameter risk index map shows that in the active monsoon

period, the closed seawater over Indonesia generally tends to possess a higher risk level of sailing, especially

for a barge. During Asian Monsoon, high-risk levels exist at the range index 5-7 which occurs on the north

part of the Indonesian sea while in the Australian monsoon period that index happens on the south part of the

Indonesian sea. Therefore, this risk index within the map is important to be used as a sailing warning for

supporting the safety of the Sea Toll Road project and to reduce the accident rates during shipping activities.

1 INTRODUCTION

As the largest archipelagic country in the world,

Indonesia has a wider marine region than its land

area. Based on the United Nations Convention on the

Law of the Sea (UNCLOS) 1982, Indonesia's total

marine area is around 5.9 million km2, with coastline

reaching up to 95,161 km, second-longest after

Canada (Lasabuda et al., 2013). Realizing this

potential sector, the government has issued several

policies to actualize Indonesia's vision to become the

world's maritime axis. The concept of "Sea Toll" was

later introduced and became one of the most limelight

policies in recent years.

The concept of Sea Toll Road is likely to become

the most awaited policies, especially for those who

work in the marine transportation sector. Nearly

99.5% of the movement of the world's economy is

done through the sea (Kadarisman et al., 2016).

During hustle and bustle discussion about the

program, shipping safety has become an important

issue that has not received much attention. Based on

historical data, natural factors, both in the form of bad

weather and high waves, have the most influence on

the incidence of ship accidents (Rahman et al., 2017).

Furthermore, research related to the condition of the

Indonesian sea and its relationship to safety has not

been widely studied. Whereas, knowing the condition

of the sea waters is the main requirement for ship's

crew and fishermen before they go to sea.

Some of the atmospheric-ocean parameters that

affect shipping safety are surface wind conditions,

rainfall, and wave height. Heavy rainfall causes

visibility to decrease, while the direction and velocity

of wind have a strong relation to the variation of wave

height. Weather factors such as wind and waves are

very important for ship movement, especially related

to their safety (Pranowo et al., 2012; Wicaksana et al.,

2015).

Based on the explanation above, this research

aims to analyze three atmospheric-ocean parameters

to provide information about the risk profile of sailing

safety as an effort to support the Sea Toll Road

program. Recent research conducted in Surabaya-

Hidayat, A., Putri, A., Musyaffa, F. and Fadlan, A.

Mapping Atmospheric: Ocean Parameter Risk Index based on Meteorological Review to Support the Operational Work of Sea Toll Road Program.

DOI: 10.5220/0010863800003261

In Proceedings of the 4th International Conference on Marine Technology (senta 2019) - Transforming Maritime Technology for Fair and Sustainable Development in the Era of Industrial

Revolution 4.0, pages 19-29

ISBN: 978-989-758-557-9; ISSN: 2795-4579

Makassar shipping trajectory has concluded that

weather patterns, essentially for rainfall, the height of

the sea waves, and the speed of the ocean currents

could be used as fundamental elements to recognize

maritime weather patterns to determine the safety of

the ship (Lutfiana and Tirono, 2013). Therefore, the

final objective of this research is mapping the sea-

atmosphere parameter risk profile index of the

Indonesian marine region, so that areas that have

potential bad weather in certain months of normal

years can be informed as a warning to the public. In

the future, this map is expected to reduce the number

of transportation accidents over the Indonesian sea.

The success of this study will give good impacts in

terms of practical meteorology and contribute some

benefits for infrastructure development, economy,

mobility, and most importantly shipping safety.

2 DATA AND METHODOLOGY

This study was conducted over the Indonesian sea and

used three atmospheric – ocean parameters, such as

wave height, rainfall intensity, wind direction, and

wind speed. The data then analyzed to obtain

atmospheric – ocean parameter risk index. The

research period in this study was determined in 2013

since it was the latest normal year, a year without any

atmospheric – ocean disturbances.

2.1 ECMWF Reanalysis Wind Data

One of the products of the European Centre for

Medium-Range Weather Forecast (ECMWF) model

is reanalysis data. ERA-Interim reanalysis data used

in this study consists of u-wind component (zonal)

and v-wind component (meridional), also the

parameters of wind speed at 10 m altitudes. ECMWF

reanalysis data is numerical prediction data with the

highest verification level in the world. In addition,

wind model data of ECMWF showed a relatively

similar pattern with wind data obtained from

observation by BMKG in September 2008

(Krisdiantoro, 2012).

2.2 WaveWatch III Model Data

Based on WMO guidelines (1998), for wave

climatology analysis, data can be obtained from two

main sources, namely: (a) measurement and

observation results, and (b) estimation results based

on wind data (wave hindcast). Data from

measurement and observation at sea are generally

very limited and not continuous. Therefore, in this

study, WaveWatch III model is used to obtain

significant wave height data. The WaveWatch III

model is a third generation (III) model developed by

National Centers for Environmental Prediction

(NCEP). This system has a global domain with the

resolution of 50 km. Suratno (1997) shows that the

results of verification between WaveWatch III model

with vessel data obtained a correlation above 0.6.

Based on research conducted by Kurniawan et al.

(2012), wave characteristics over the Indonesian sea

have patterns associated with monsoonal wind cycles.

The pattern of monthly variations in high-wave and

high-frequency waves in most of the Indonesian

territorial marine areas has two peaks that occurred in

the Asian monsoon period (December-January-

February (DJF)) and Australia monsoon period (June-

July-August (JJA)).

2.3 TRMM Rainfall Data

TRMM rainfall data is a joint space mission between

the National Aeronautics and Space Administration

(NASA) and Japan Aerospace Exploration Agency

(JAXA) designed to monitor and study rainfall

variations over the tropical region. Bowman (2015)

showed that when comparing land-based gauges with

TRMM, the correlations are substantially increased

by time-averaging the gauge data between the two

measurements for gauge-average periods of about 2

to 10 h. Maximum correlation coefficients are in the

range of 0.6 to 0.7. Moreover, Schumacher and

Houze (2000) assessed the performance of TRMM by

comparing the data with Kwajalein oceanic validation

radar, it showed that the data agree well within the

range of sensitivity of the precipitation radar.

2.4 Analysis Process

Three atmospheric – ocean parameters data then

spatially processed using the Grid Analysis and

Display System (GrADS) to understand the monthly

characteristics of each parameter. TRMM daily

rainfall data (mm/day) is available in .nc format and

then plotted as the monthly average. The GrADS

provides programming tools and an execution

environment to ease program development for the

grid (Cooper et al., 2004).

The increasing frequency of maritime

transportation accidents in Indonesia has recently

become increasingly alarming. Several accidents

occurred in the sea, both sinking of ships and

collisions between ships (Lutfiana and Tirono, 2013).

One of the factors that can cause collisions between

ships is the decrease of visibility due to rain events

senta 2019 - The International Conference on Marine Technology (SENTA)

occurred over the region. Categorization of rainfall

data was based on the press release of the National

Agency for Meteorology Climatology and

Geophysics (2010), as shown in Table 1.

Table 1: Criteria for rainfall intensity in Indonesia.

Categor

Rainfall intensity (mm/day)

ht 5

–

Moderate 20

–

Heav

–

100

Very Heav

>100

The wind parameter is also categorized based on

its risk level on shipping safety, especially over a

barge (Table 2). The barge was selected to be a

standard model of transportation modes since the

progress in infrastructure development in the last two

years has been increased significantly. Many barges

serve the distribution of infrastructure materials such

as cement, stone, and sand for infrastructure

development. Barge business also uplifted

significantly, followed by an increase in demand for

coal shipments (Simorangkir, 2017). Surface wind

data is the result of ECMWF models in .nc format and

then processed to determine the monthly

characteristics of wind direction and speed using

GrADS.

Table 2: Criteria for wind parameter in Indonesia.

Categor

Win

speed (knots)

Very low <7

Low 7

–

Moderate 10

–

h >16

Table 2 shows that if the wind speed is less than 7

knots, it possesses a very low-risk level for the

shipping activities of the barge. If the wind speed

reaches 7 – 10 knots, then the cruise risk level is

relatively low. Meanwhile, if the wind speed ranges

from 10 – 16 knots, the cruise risk level is categorized

to be moderate. Whereas if the wind speed reaches

more than 16 knots, the risk level of the shipping

activities is said to be high.

Significant wave height data used in this study is

available in .nc format. The data is then processed to

understand the monthly characteristics of the wave

height using GrADS. In marine practical meteorology,

significant wave height terminology is often used to

express ocean wave height. Based on data records,

significant wave height is defined as the average height

of 1/3 of the highest waves, which is equivalent to the

wave height of visual observations (WMO, 1998). The

equation for calculating the significant wave height is

explained by Wara (2019) as follows:

𝐻







𝑁

𝐻











(1)

Interpretation of wave height variations towards

shipping safety based on information provided by the

Indonesian Agency of Meteorology Climatology and

Geophysics. Table 3 shows that the wave height

which is less than 0.75 m has a very low-risk level

(safe) for the shipping activities of the barge. If the

wave height reaches 0.75 - 1.0 m, then the cruise risk

level is relatively low. Meanwhile, if the wave height

ranges from 1 - 1.5 m, the fishermen should be

vigilant because the risk level of sailing is said to be

moderate. Whereas if the wave height reaches more

than 1.5 m, the risk level of the sailing activities is

said to be high and fishermen are generally advised

not to go to sea. In every coordinate, the value of each

parameter is then categorized into 4 index values and

summarized in Table 4.

Table 4: Quantification of index values on each parameter.

Paramete

value Index Value

Wave height < 0.75

0.75

–

1.0

–

1.5

>1.5

Wind speed < 7 knots 1

–

10 knots 2

–

16 knots 3

> 16 knots 4

Rainfall intensity 5

–

20 mm/da

–

50 mm/da

–

100 mm/da

>100 mm/da

After defining the index value for each parameter,

the risk profile of atmospheric-ocean parameters is

then determined based on the sum of these three

parameters, with the maximum total index for these

parameters is 12 and the minimum value is 1. The

determination of the criteria will be adjusted

according to Table 5 below:

Table 3: Criteria for significant wave height in Indonesia.

Categor

Wave height (meter)

Very low <0.75

Low 0.75

–

1.0

Moderate 1.0

–

1.5

h>1.5

Mapping Atmospheric: Ocean Parameter Risk Index based on Meteorological Review to Support the Operational Work of Sea Toll Road

Program

Table 5: Risk index determination based on total index.

No Total Risk profile

1 1

–

3 Low

2 4

–

6 Moderate

3 7

–

9 High

4 10

–

12 Ver

Risk profile naming is adjusted to the risk matrix

product issued by BMKG (2018). The total index is

the value obtained from the sum of the index values

of the three parameters used. This value is then used

as a basis for determining the atmospheric-ocean

parameter risk index as information about the risk

profile of weather conditions on shipping safety. The

total index value or herein after referred to as the risk

matrix level is mapped using Q-Geographic

Information System (QGIS) to analyse the monthly

risk profile of shipping security. QGIS is open-source

software that lets users visualize, question, analyze,

and interpret geographical data (Shaira, et al., 2020).

3 MONTHLY AVERAGE OF

ATMOSPHERIC – OCEAN

CONDITIONS

3.1 Wind Profile over Indonesian Sea

The wind parameter is a wave generator factor in the

free ocean. Kisnarti (2012) in her research on

meteorological-oceanography studies for shipping

operations said that wind is the main generator of

waves. The wind that blows above the surface of the

water will move its energy into the water. Wind speed

will cause stress on the surface of the water which is

initially calm and will be disturbed and there will be

ripples or waves above the surface of the water. If the

wind speed increases, the ripples become larger and

when the wind blows continuously a wave will

eventually form. The longer and the stronger the wind

blows, the larger the waves form. The height and

period of the wave generated by the wind are

influenced by wind speed, wind duration, wind

direction, and fetch (Wibisono, 2005).

Generally, the characteristics of waves in

Indonesian sea waters have patterns associated with

the monsoonal wind cycle. The waters of the high

seas that are directly related to the oceans generally

have higher sea waves compared to closed waters

between islands. In the Asian monsoon period (DJF)

in 2013 (Figure 1), wind speed generally starts to

increase in December and gets stronger in January

then begins to weaker in February. January possesses

the strongest wind distributed around the South China

Sea and the Pacific Ocean with velocity ranges from

13 up to more than 17 knots. This condition is really

dangerous for a barge to sail. The wind speed around

Karimata Strait ranges from 9 – 13 knots, becomes

stronger when the seawater flows over the Java Sea

region, and again decreases over the Banda Sea,

Arafura Sea, and the Timor Sea along with the

general direction flow of the wind that from Asia to

Australia region. The weakest wind region was

distributed around Makassar Strait and the Molucca

Sea with ranges from 5 – 7 knots.

3.2 Significant Wave Height

Variability

Waves are an important factor in marine

meteorological information services (WMO, 2001).

Frequent high waves can cause disruption on fishing

activities, inter-island sea transportation which can

impact lives, scarcity of foodstuffs on several islands,

and various types of work due to constrained supply

of construction materials. In comparison to other

types of waves, waves due to wind are the most

dominant waves occurring at sea level, both in terms

of frequency of occurrence and energy (Hutabarat and

Evans, 2008). The existence of waves due to wind on

the sea surface affects almost all marine activities and

therefore information about these waves is an

important part of marine meteorological information

services.

Kurniawan et al. (2012) stated that the

characteristics of the wave over the Indonesian sea

have patterns associated with monsoonal cycles.

Figure 2 shows the variation of the significant wave

from month to month in 2013. In the Asian monsoon

period, the wave height around the strait region in the

Indonesian sea commonly ranges from 0 – 1 meter. In

January 2013, the wave height around the Java Sea,

some parts of Banda Sea, and Timor and Arafura Sea

can reach up to 1 – 1.5 meter, which is dangerous for

a barge to sail. When it comes to the transition period

of MAM, Indonesian seas dominantly calm because

the wave height was around 0 – 0.75 m. However,

some parts of Arafura and Timor Sea start to increase

senta 2019 - The International Conference on Marine Technology (SENTA)

(

)

Januar

(

)

Februar

(e) Ma

(f) June

(g)

Jul

(

)

ust

(i) Septembe

(j) Octobe

(k) Novembe

(l) Decembe

Figure 1: Wind speed and direction monthly profile over Indonesian sea in 2013.

Mapping Atmospheric: Ocean Parameter Risk Index based on Meteorological Review to Support the Operational Work of Sea Toll Road

Program

in height from March to May. The maximum wave

height reaches around this marine area in May 2013.

Once Australian monsoon comes, this maximum

wave height areas expand up to Banda Sea with a

value of around 1 – 2 meters. The maximum areas of

high wave in Australian period occur in July 2013,

and the wave height starts to decrease in August.

However, there is an increase in wave height around

Java Sea in August. The second transition phase

period shifted the area of high wave around South

China Sea and spacious marine area over Pacific

Ocean. The maximum wave height happens in

November with a value around 1.25 – 1.5 meters. In

December 2013, where Asian monsoon dominates,

the wave height over South China Sea region reaches

its maximum up to 2.5 meters and remains stable until

the next month.

Kisnarti (2012) explained that the high-wave

month period occurs in January, February, July, and

August and therefore, categorized as dangerous

months of the year. The monthly pattern of high-wave

variations and high-frequency waves in most of

Indonesia's territorial sea has two peaks, which

occurred in DJF period and JJA period. Marine areas

that relate to the South China Sea (Karimata Strait,

Natuna Sea) and the Pacific Ocean (Sulawesi Sea,

Maluku Sea, and the marine areas on the northern

Papua), the Java Sea, Flores Sea, and Makassar Strait

reach their highest peaks in the Asian monsoon

period.

In contrast, the Banda Sea, Arafura Sea, and

marine areas around Indian Ocean (Timor Sea, Savu

Sea) reach their highest peaks in the Australian

monsoon period. The study also explains that the

high-wave prone areas during Asian monsoon period

were wider than when Australian monsoon period.

Whereas in the transition season between the two

monsoons most of Indonesia's waters are not

categorized as dangerous areas.

3.3 Rainfall Intensity Variation over

Indonesian Sea

Rainfall is one of the weather and climate elements

that have a dominant influence in the tropics such as

Indonesia, compared to other elements. Therefore,

understanding the characteristics of rainfall, both its

variability and extreme conditions is very important

to recognize the characteristics in order to support

human activities (Harijono, S. W. B., dan Junaeni, I.,

2008), including assisting shipping activities.

Lutfiana and Tirono (2013) further studies show

that rainfall has a significant influence in determining

the safety levels of shipping activities along Surabaya

- Makassar shipping lane.

The area where the rainfall intensity is quite high

is located around Java Sea and some part of Pacific

Ocean in January with a value around 10 – 40

mm/day, while in February the highest rainfall

intensity region was around the small part of the

South China Sea with a value around 15 – 30 mm/day.

Based on categorization made by Indonesian Agency

of Meteorology Climatology and Geophysics, this

categorized as moderate rain, however, until now

there is no particular rainfall categorization (impact-

based forecast) for each type of marine transportation.

Therefore, we assume that moderate rain can cause a

hazard for the barge to sail.

In the next months of the first transition period

(MAM), rainfall intensity over Indonesian sea was

quite lower than the intensity overland region. In

March 2013, rainfall intensity in all parts of

Indonesian sea ranges from 5 – 15 mm/day. While it

starts to slightly increase April and May. In

Australian monsoon period, rainfall intensity tends to

be slightly higher over marine regions than land areas.

Its distribution was varied and random, but it starts to

significantly decrease in August, especially in the

south part of Indonesia. This condition persists until

couple of months later. Rainfall intensity on the south

part of Indonesia, both marine and land areas,

significantly decrease in the second transition period,

except in November. Rainfall intensity increases

significantly in December 2013, especially on the

north part of Indonesian seas such as South China

Sea, Java Sea, and some part of Indian Ocean near

Sumatra islands.

3.4 Atmospheric – Ocean Parameter

Risk Index Variation

Three atmospheric–ocean parameters have been

evaluated and quantified based on Table 4 in order to

get the risk index of sailing safety over the Indonesian

sea (Table 5). This total index can be used as a

reference for the government, especially the Ministry

of Transportation, to assess the safest sea lanes and

inform the public. When the total index reaches a

value between 1 and 3, then a barge has a low-risk

level to sail over the Indonesian sea, in other words,

it is safe for a barge to sail over a marine region that

possesses the index value within the range. While if

the total index value ranges from 4–6, the risk level is

said to be moderate, it means that a barge must be

vigilant during sailing over this region. If the total

index value ranges from 7–9, the risk level is said to

be high for a barge to sail. It is recommended for a

senta 2019 - The International Conference on Marine Technology (SENTA)

(a) Januar

(b) Februar

(

)

March

(

)

ril

(e) Ma

(f) June

(g)

Jul

(

)

ust

(i) Septembe

(j) Octobe

(

)

Novembe

(

)

Decembe

Figure 2: Month to month variation wave height in meter over Indonesian sea region in 2013.

Mapping Atmospheric: Ocean Parameter Risk Index based on Meteorological Review to Support the Operational Work of Sea Toll Road

Program

barge not to sail over these sea lanes. Meanwhile, if

the total index value reaches up to 10 – 12, the risk

level is said to be very high, which means it is really

dangerous for a barge to sail over the region. But

figure 4 shows that the conditions where the risk level

is categorized very high rarely happen over closed sea

waters regions over Indonesia in normal years.

Therefore, it assumes that this condition can occur

when a cyclone or other atmospheric–ocean

disturbance phenomenon happened.

Based on the risk index variation map in January

(Figure 4a), the risk level is very high over the South

China Sea and some parts of Pacific and Indian Ocean

near Sumatra and the south part of Java Island. The

high-risk level region occurred around Java Sea, a

small fraction of Banda, Arafura, and Timor Sea.

While moderate-risk level region mostly occurred

over Karimata Strait. The lowest risk level region was

located around Makassar Strait, Molucca Sea, and the

northern part of Banda Sea. This low-risk level region

is commonly located over small sections of closed sea

waters between some islands surround it as a great

barrier and obstacle when the wind blows over it.

In February, the risk shipping level over the

Indonesian sea was mostly moderate, such as over

Karimata Strait, Java Sea, Banda Sea, Arafura Sea,

and Celebes Sea. The other region among closed

seawater regions over Indonesia possesses a lower

risk level, such as along some parts of Makassar Strait

and some part of Molucca Sea. While, since it is in

the Asian monsoon period, the risk level over South

China Sea and along the Pacific Ocean is categorized

as very dangerous. The atmospheric–ocean parameter

risk level variability tends to get lower in March, low-

risk level region expands over several regions,

including Karimata Strait, a small fraction of Java

Sea, Makassar Strait, and Molucca Sea. While the rest

areas possess a moderate risk level of sailing. In

April, the lower risk level region tends to get wider

and covers almost all part of closed sea waters in

Indonesia. The high-level risk region starts to occur

over Indian Ocean, it moves from Pacific Sea in DJF

period to Indian Ocean as it is approaching JJA

period. In the last month of the first transition phase

period of MAM, the atmospheric–ocean parameter

risk index over the southern part of Indonesian sea

start to increase significantly, especially over Indian

Ocean near Australia.

In Australian monsoon period in June, the

atmospheric–ocean parameter risk level elevated,

especially over Arafura, Banda, and Timor Sea along

with the strengthening of the Australian Monsoon.

This high-risk level region then expands and reaches

its maximum in July 2013 and starts to slightly

decrease in August. In the second transition period in

September, the atmospheric–ocean parameter risk

level generally moderate over all parts of Indonesian

sea, except in a small fraction of Celebes Sea,

Makassar Strait, and Molucca Sea. In October, the

lower risk level region starts to get wider and covers

almost all parts of the Indonesian sea. While, both

Pacific and Indian Oceans possess moderate to high-

risk levels. In the last month of SON period,

November, the low-risk level region became wider

and covered all parts of the closed seawater in

Indonesia. This condition changed significantly in

December when the Asian monsoon began to be

strengthened. The high-risk level region observes

over the dominant part of South China Sea and along

the Pacific Ocean. Karimata Strait and Java Sea

possess moderate risk level of sailing safety, while the

other generally tend to have the low-risk level of

sailing safety.

4 SUMMARY AND CONCLUSION

The wind speed over Indonesia has a distinctive

pattern where the strongest wind region is commonly

located near the ocean, both in Pacific and Indian

ocean, while it starts to decrease as it approaches the

Indonesia sea region, closed waters between the

islands. The significant wave height pattern generally

follows the surface wind pattern as it is the wave

generator factor in the free ocean. Generally, the

characteristics of waves in Indonesian sea waters

have patterns associated with the monsoonal wind

cycle. In Asian monsoon period (DJF), wind speed

generally, starts to increase in December and gets

stronger in January but then tends to weaken in

February. January possesses the strongest wind

distributed around South China Sea and Pacific

Ocean with velocity ranges from 13 up to more than

17 knots.

Compared to wind profile and wave height

variation, rainfall intensity pattern possesses a higher

variability, both in land and marine region. In Asian

monsoon period, the rainfall intensity reaches its

maximum in December 2013 with heavy rainfall

areas distributed around the South China Sea and Java

Sea region with range values from 20–50 mm/day. It

starts to decrease in January, more often significant in

February. In transition periods, rainfall intensity tends

to have a higher value over land area rather than over

marine region. In Australian monsoon period, rainfall

intensity tends to be slightly higher over marine

regions than land areas. Its distribution was varied

and random, but it started to significantly decrease in

senta 2019 - The International Conference on Marine Technology (SENTA)

(

)

Januar

(

)

Februar

(

)

(

)

June

(g) Jul

(h) August

(

)

tembe

(j)

Octobe

(k) Novembe

(l) Decembe

ure 3: Rainfall intensit

(

mm/da

variabilit

over Indonesian sea from month to month in 2013.

Mapping Atmospheric: Ocean Parameter Risk Index based on Meteorological Review to Support the Operational Work of Sea Toll Road

Program

August, especially in the south part of Indonesia. This

condition persists until a couple of months later.

Rainfall intensity on the south part of Indonesia, both

marine and land areas, significantly decreased in the

second transition period, except in November.

Rainfall intensity increased significantly in

December, the beginning period of Asian monsoon,

especially on the north part of Indonesian seas such

as South China Sea, Java Sea, and some part of Indian

Ocean near Sumatra Island.

As summarised in Table 6, the atmospheric–ocean

parameter risk index map shows that in the active

monsoon period, the closed seawater over Indonesia

generally tends to possess a higher risk level of

sailing. In Asian monsoon, the marine area that

possesses moderate – high-risk levels commonly

located on the northern part of Indonesian seas, such

as South China Sea and Java Sea. While in the

Australian monsoon period, the marine areas that

possess moderate to the high-risk level generally

observe on the southern part of Indonesian seas, such

as over Banda Sea, Arafura, and Timor Sea. Several

marine areas in Indonesia possess low – moderate risk

levels of sailing throughout the normal year, such as

Makassar Strait, Molucca, and Celebes Sea. For

further research, this atmospheric-ocean parameter

risk index can be improved to gain variations of the

results by using a longer time series of data under the

consideration of another climate phenomenon, such

as El Nino and La Nina. Enhancing collaboration

between agencies is also required to support this

project and to obtain some feedback from many

stakeholders.

ACKNOWLEDGEMENTS

We acknowledge National Aeronautics and Space

Administration (NASA) and PPS, which develop and

compute TRMM file data and also European Centre

for Medium-Range Weather Forecasts for reanalysis

wind data. Finally, Geospatial Information Agency of

Indonesia for providing WaveWatch III model data.

REFERENCES

Badan Meteorologi Klimatologi dan Geofisika. (2010).

Extreme weather and climate condition in 2010 – 2011,

press release 12 Oktober 2010, Jakarta.

Bowman, K.P. (2005). Comparison of TRMM Precipitation

Retrievals with Rain Gauge Data from Ocean Buoys. J.

of Climate, Vol. 18, pp. 178-190.

Cooper, K., Dasgupta, A., Kennedy, K., Koelbel, C.,

Mandal, A., Marin, G., Mazina, M., Mellor- Crummey,

J., Berman, F., Casanova, H. and Chien, A., 2004,

April. New grid scheduling and rescheduling methods

in the GrADS project. In 18th International Parallel and

Distributed Processing Symposium. (2004).

Proceedings. (p. 199). IEEE.

Harijono, S. W. B., & Juaeni, I. (2008). Dampak Variasi

Temperatur Samudera Pasifik dan Hindia Ekuatorial

Terhadap Curah Hujan di Indonesia, Jurnal Sains

Dirgantara, Vol. 5 (2), pp. 83-95.

Hutabarat, S. and Evans S. M. (2008). Pengantar

Oseanografi, Jakarta: UI-Press.

Kadarisman, M., Yuliantini, Y. and Majid, S.A. (2016).

Formulasi kebijakan sistem transportasi laut. Jurnal

Manajemen Transportasi & Logistik, Vol. 3 (2), pp.

161-183.

Kisnarti, E.A., (2012). Kajian meteo-oseanografi untuk

operasional pelayaran Gresik-Bawean.

https://dspace.hangtuah.ac.id/xmlui/handle/1234567

89/139.

Kurniawan, R., Habibie, M. N., Permana, D.S. (2012).

Study of High Wave Prone Areas in Indonesian Sea

Region, Jurnal Meteorologi dan Geofisika, Vol. 13 (3),

pp. 201 – 212.

Lasabuda, R. (2013). Pembangunan wilayah pesisir dan

lautan dalam perspektif Negara Kepulauan Republik

Indonesia. Jurnal Ilmiah Platax, Vol. 1 (2), pp. 92-101.

Lutfiana, R. and Tirono, M. (2013). Introduction of maritime

weather patterns (rainfall, wave height, and current

velocity) with the Adaptive Neuro Fuzzy Inference

System (ANFIS) method on the sea lanes along Surabaya

- Makassar, Jurnal Neutrino, Vol. 6 No. 1.

Table 6: Summary of Indonesia’s atmospheric-ocean risk index.

Rank Marine Region Risk Value Month

1 Indian Ocean to the Southwest of Java 10 July

2 Indian Ocean to the South of Java 9 Au

ust

3 Arafura Sea 8 June

4 Banda Sea 7 Au

ust

5 Indian Ocean to the Southwest of Java 6 Novembe

6 Western Indian Ocean of Sumatera 5 March

7 Seram Sea 4 Februar

8 Maluku Sea 3 Februar

9 Indian Ocean to the East of Kupang 2 Februar

10 Makassa

Strait 1 Ma

senta 2019 - The International Conference on Marine Technology (SENTA)

Pranowo, W.S., T.R. Adi, S. Makarim, and N.N. Hasanah.

(2012). Marine & Climate Research Contributions to

the National Program on Climate Change Adaptation &

Mitigation. Proceed. The International Workshop on

Climate Information Services in Supporting Mitigation

& Adaptation to Climate Change in Transportation &

Tourism. 15-16 May 2012. ISBN: 978-602-19508-3-8.

p.76-79.

Rahman, H., Satria, A., Iskandar, B. H., Soeboer, D. A.

(2017). Determination of Dominant Factors Cause

Accidents in the Main Kesyahbandaran of Tanjung

Priok, Albacore. vol. 1 (3), pp. 277 – 284.

Schumacher, C and Houze, R A. (2000). Comparison of

Radar Data from the TRMM Satellite and Kwajalein

Oceanic Validation Site. J. of Applied Meteorology, vol

39, pp. 2151 – 2164.

Shaira, H., Naik, P.R., Pracheth, R., Nirgude, A.S., Nandy,

S., Hiba, M.M. and Karthika, S. (2020).

Epidemiological profile and mapping geographical

distribution of road traffic accidents reported to a

tertiary care hospital, Mangaluru using quantum

geographic information system (QGIS). Journal of

family medicine and primary care, Vol. 9 (7), p.3652.

Simorangkir, E. (2017). Pembangunan Infrastruktur Genjot

Kinerja Industri Pelayaran [online],

https://finance.detik.com/infrastruktur/d-

3736632/pembangunan-infrastruktur-genjot-kinerja-

industri-pelayaran (accessed on 7 August 2019).

Suratno. (1997). Numerical models for estimating sea level

waves for Indonesian sea region and its surroundings.

Tesis, Program studi Fisika, Fakultas MIPA,

Universitas Indonesia, Jakarta.

Wara, A. D. (2019). Coastal Protection, International

Journal of Offshore and Costal Engineering, vol. 1 (1).

Wicaksana, S., Sofian, I., dan Pranowo, W. Karakteristik

gelombang signifikan di Selat Karimata dan Laut Jawa

berdasarkan rerata angin 9 tahunan (2005- 2013).

(2015). Omni-Akuatika, [S.l.], Vol. 11 (2).

World Meteorological Organization (WMO). (1998). Guide

to wave forecasting and analysis, WMO No. 702,

Secretariat of the World Meteorological Organization,

Geneva-Switzerland: Author.

World Meteorological Organization (WMO). (2001). Guide

to marine meteorological services, WMO No. 2.

Secretariat of the World Meteorological Organization,

Geneva-Switzerland: Author.

Mapping Atmospheric: Ocean Parameter Risk Index based on Meteorological Review to Support the Operational Work of Sea Toll Road

Program