PAJ Oil Spill Simulation Model for the Sea of OkhotskFuji Research Institute Corporation
Takashi Fujii

1. Introduction
In order to assist in remedial activities in the event of a major oil spill The Petroleum Association ofJapan (PAJ) has developed a model for forecasting the drift and dispersion of spilt oil. Using
meteorological forecast data, the model can be applied to any sea area around the coast of Japan, andenables a simulation to be created in a short time frame.
Following the commercialization of Sakhalin’s oil fields in July 1999, the necessity of building aresponse system to oil spills on routes other than the already established Middle East oil routes became
apparent. With this in mind we have developed a forecasting model covering the Okhotsk Sea,
independent of present models for Japanese waters.
We have also made the following modification on the present model to achieve more precise
forecasting in open seas:(1) Function to update ocean current data
(2) Longer term meteorological forecast data
Below we offer an overview and description of the new features of the oil spill dispersion and drift
forecasting models, focusing on the Okhotsk Sea model.

2. Outline of the Dispersion and Drift Forecasting Model
The model was developed to provide information to support control operations in the event of an oil
spill, using a PC-based simulation. The model combines the following features:(1) Able to create simulations in a short time frame(2) Simple interface(3) Able to use the latest online meteorological data
(4) Able to forecast on any area on the Japanese coastline(5) Supplemented with basic information about vulnerable areas

2.1 Processes Covered by the Forecasting Models
In order to forecast the behavior of spilt oil on the sea surface it is necessary to consider the drifting
process of an oil slick, and the weathering and dispersion processes which predict the physical changesof the oil itself. The model is programmed with the dispersion and weathering process indicated in Fig.
2.1, and the drifting process shown in Fig. 2.2 using current standard modelling methods.

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Wind

Evaporation

Emulsification

Horizontaldispersion

SpreadVerticaldispersion

Oil drops
Ascent caused by buoyancy

Figure 2.1 Dispersion and Weathering Process of Spilt OilWindRiver flow

Drift caused by wind

Oil slick

Ocean current
Tidal flow

Figure 2.2 Drift Process of Spilt Oil

2.2 Forecasting Database
It is essential to prepare beforehand data about the above-mentioned processes in order to makeforecasts within a short time frame. The model enables swift predictions through the use of aforecasting database that takes into consideration both seasonal and regional factors. The databaseincludes:
(1)
(2)
(3)
(4)

Topographical data (marine and terrestrial)
Data on tidal, ocean and river currents
Average monthly meteorological data
Data on the properties of oils (physical property data on crude oils classified into 8 andfour refined oils into 4, taking into account their weathering characteristics)

2.3 Use of Meteorological Data
The wind at sea is the factor exercising the greatest influence on the drift of oil. However, since thewind at sea tends to change every few hours, gaining a prior statistical grasp of this factor is somewhat
difficult. To optimize the precision of the model, we have built a system enabling the downloading of
up-to-date meteorological data via the Internet, updated every 12 hours for up to 51 hours in advance,
which can be used in forecasting as illustrated in Fig. 2.3.
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Japan Meteorological Agency

GPV data

Processed every 12 hours
Japan Weather Association◎ Data generation program◎ Wind speed forecast data
(12 sea areas, 3km grid)Web serverInternet connection
・Public line
・Dedicated line
Downloadable at anytime
Forecast model user
◎ Forecast model◎ Various basic data
◎ Wind speed forecast data
Forecasting model PC

Figure 2.3 System for Use of Meteorological Data

Though the meteorological data currently available is only available for 51 hours in advance, we areworking on incorporating 192 hours weekly forecasts (updated every 24 hours) in order to makelong-term predic tions in open seas feasible. And as this 192 hour data uses GPV’s GSM data, which
covers all the world’s sea areas, it can be applied anywhere in the globe.

2.4 Update of Ocean Current Data
As well as the wind, another major factor in the drift of oil in the open seas is currents, typified by theKuroshio Current (also known as the Japan Current). Since there is some degree of regularity in themovement of ocean currents, the model uses the database average values resulting from several
decades of data.
However, ocean currents sometimes meander, forms vortexes, and can behave irregularly; it isconceivable that the current at the time of an accident may be different from the average. To redress
this we have added to the model a function enabling the update of current-related information forforecasting, based on data from the Quick Bulletin of Ocean Conditions published by the Japan Coast
Guard (Fig. 2.4), and on-the-spot reports of accident sites. This forecasting is made possible bypartially inputting the vectorial data obtained from actual measurement or ocean current bulletins etc.,
and merging it with the average data, thereby replicating the currents at the time of an accident.
― 3 ―Figure 2.4 Quick Bulletin of Maritime Conditions, No. 6., 2002
Source: Japan Coast Guard（http://www1.kaiho.mlit.go.jp/）

The ocean currents are updated by adding the average current data shown in Fig. 2.5 to the latest
current data illustrated in Fig. 2.6, making a combined picture as in Fig. 2.7.

Figure 2.5 Average Ocean Current Data (original data)― 4 ―Figure 2.6 After Input of Current Data (may be input as a vector in the desired location)

Figure 2.7 Updated Combined Current Vectors― 5 ―2.5 Information about Vulnerable Areas
Whilst it is not directly related to forecasting itself, it is of great assistance in planning response
operations to have geographical data on surrounding land areas relevant to the results of the forecast.
Information about the vulnerability of beaches to oil pollution is particularly important. Bearing this inmind, the forecasting model has prepared data in vulnerable areas such as shorelines, fisheries, harbors
and industrial districts), in order that they may be identified as the necessity arises. The data display
pattern follows NOAA’s symbols as its norm, but can be altered. The name display can also bechanged, and the details of information are to be displayed upon clicking.

Figure 2.8 Example of vulnerable areas display― 6 ―2.6 Forecast Coverage Area
Fig. 2.9 shows the original nationwide model covering the Japanese coast, and the forecast area of theOkhotsk Sea covered by the newly developed model. As the original program of the model had
multi-purpose capabilities, providing data for the region in question enabled it to be applied to any sea
area, and the Okhotsk Sea model too was basically developed through this data building process. There
are more details of the Okhotsk Sea model in the next section of this paper .

? Japanese Nationwide Edition? Area Covered:

20°N120°E – 50°N150°E? Topographical Resolution:

3km grid cells? Okhotsk Sea Edition? Area Covered:

39.2°N135°E – 60.8°N160.5°E? Topographical Resolution:

3km grid cells

Okhotsk SeaJapaneseNationwide

Figure 2.9 Forecast Coverage Area

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3. The Okhotsk Sea Area Model

3.1 Outline of the Model
The Okhotsk Sea model basically has the same functions as the Japanese nationwide model. The
forecast coverage area is indicated in Fig. 3.1 (39.2°N135°E - 60.8°N160.5°E), which enables to locate
freely the area (within the limits of the overall area covered) to make calculation of the drifts.
We also intend to enable the model to use 192 hour meteorological forecast data, though, as the modelis the same as the Japanese version, it will not be able to forecast during the winter periods in which
icebergs form.

Figure 3.1 Area Covered by the Okhotsk Sea Model― 8 ―3.2 Ocean Current Data
Unfortunately there is hardly any data available in Japan about currents in the Okhotsk Sea. The maincurrents are described in the Japan Coast Guard’s East Coast Of Siberia Pilot, regarded as the standard
work on this subject, and the model has made use of this data to create the database shown in Fig. 3.2.
However, as we have already mentioned, it is likely that the data will be different from the real current
at the time specified. It is recommended that, should an accident occur, the most recent data is collated,
updated and used to make a forecast.

Figure 3.2 Summertime Current Patterns in the Okhotsk Sea Area
Source: Japan Coast Guard’s East Coast Of Siberia Pilot― 9 ―3.3 Sample Calculation
Fig. 3.3 shows a forecast calculation example featuring a section of the area near the oil fields ofnorthern Sakhalin. It is possible to display a grid with a resolution of 3km, and compute a two to three
day forecast within a couple of minutes like the nationwide version. Fig. 3.4 shows a sample forecast
result.

Figure 3.3 Area Selected for Calculation by the Okhotsk Sea Model

Figure 3.4 Okhotsk Sea Model Forecast Sample (drift trajectory and distribution of slick at three stages )― 10 ―

4. Conclusions
By providing both the nationwide and Okhotsk Sea versions of the PAJ’s oil spill dispersion and drift
forecasting model, we have achieved coverage of the sea around Japan in the event of an oil spill
accident a possibility. Furthermore, through the extension of forecast data and the addition of functions
enabling the update of ocean current data, we have optimized the precision of forecasts for theOkhotsk Sea and Japanese waters, and made long-term forecasting a reality.
As for the creation of the database on the factors exerting the greatest influence on the drift of oil – thewind at sea and ocean currents, PAJ’s simulation model can be applied to any sea area in the world if
the Okhotsk Sea model incorporating GSM (Global Spectral Model) meteorological data via theInternet and the ocean current creation function are used.
In preparation for an actual accident we intend to carry on upgrading the model and make it even more
useful through the addition of new features including: an accumulation of forecasting data; improved
support for response actions through the provision of comprehensive data on vulnerable areas; andcompilation of a reference manual to reduce operator mistakes in an emergency.

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