Digital Distribution Standard

for NOAA Trajectory Analysis Information

HAZMAT Report 96-4

January 1996

Hazardous Materials Response and Assessment Division

National Oceanic and Atmospheric Administration

and

Florida Marine Research Institute

Florida Department of Environmental Protection

Digital Distribution Standard

for NOAA Trajectory Analysis Information

HAZMAT Report 96-4

J. A. Galt and D. L. Payton

Hazardous Materials Response and Assessment Division

Office of Ocean Resources Conservation and ASsessment

National Oceanic and Atmospheric Administration

Seattle, Washington 98115

and

H. Norris and C. Friel

Florida Marine Research Institute

Florida Department of Environmental Protection

St. Petersburg, Florida 33701

Contents

Abstract..................................................................................1

1 Introduction...........................................................................1

2 Alternate Model Scenario Development................................4

3 Data Analysis Methods..........................................................9

4 Data Formats......................................................................17

5 Conclusions .........................................................................21

References ...........................................................................25

Appendices

A Trajectory Analysis Files 1 and 4 (MOSS Format) ...............27

B Trajectory Analysis File 2.....................................................30

C Trajectory Analysis File 3.....................................................33

D Trajectory Analysis File 5.....................................................35

E Pattern Samples...................................................................36

F Font Samples.......................................................................37

G Sample trajectory analysis files............................................38

Figures

1 Trajectory forecast with oil represented as Lagrangian Elements 5

2 Typical trajectory stepped forward in time ............................7

3 Example of oil distribution reported from overflight ...........12

4 Example of predicted distribution of heavy, medium, and light

concentrations of oil, shown as contours..............................13

5 Contours of predicted heavy, medium, and light oil concentrations

and associated uncertainty bound........................................14

6 Probability of impact graphic (extended outlook).................16

7 Summary of standard products............................................17

8 Composite trajectory analysis standard...............................22

i
Abstract

During emergency oil spill response efforts, estimates of the

movement and spreading of the pollutant provide critical

information. These estimates are typically obtained by analyzing a

series of models or database look-ups. The strategy described in

this paper for conducting this analysis can be likened to game

theory’s “minimum regret” approach.

We describe alternate model use techniques and identify a welldefined series of model runs required to support minimum-regret

trajectory analysis. This standard series of model runs is generic

and could be carried out by any of the oil spill trajectory models

commonly available for emergency response.

We describe specific algorithmic procedures that can be applied to a

minimum-regret set of model runs and define a standard digital file

format. The standard trajectory analysis files that make up a

“message” are in a public domain ASCII format easily transmitted

over any electronic mail system. The standard files contain all of

the data necessary to produce a simple, one-page graphic display

that provides the minimum-regret trajectory analysis for

operational emergency response planning. In addition, the standard

message contains geo-referenced topological information so that

the pollutant distribution information can be used in Geographical

Information Systems (GISs) with additional resource data to answer

geographic and thematic queries. We further describe plans for data

distribution and simple viewing and mapping import applications.

1 Introduction

During an oil spill, accurate information on the movement and

spreading of the pollutant is a factor in virtually every facet of the

spill response activities.

To predict pollutant movement you must understand a number of

different physical processes, computational procedures, and

observational techniques (Galt 1994). In general, the information

used to describe where the oil may be at some future time comes

from many sources and must be woven together from data that are of

questionable accuracy and often incompatible with other components

of the data set. A number of computer models solve the mass

balance distribution equation for oil, usually described as “oil

1
trajectory models” (ASCE in press). In addition, many model

developers and users falsely contend that these products present a

deterministic, realistic representation of the future distribution of

the oil. In fact, all of these formulations require external models

that are approximations controlled by numerous parameters, which

in turn are based on the subjective interpretation of sparse data.

Thus, the same trajectory model could give vastly different

answers, depending on how it is used. Two different trajectory

models are almost certain to produce differing estimates. Given the

uncertainty of this process, it is important to understand that,

regardless of how the estimates are obtained, the results are due to

analysis, not deterministic modeling (Galt 1995).

Defining standard-use protocols is an integral part of model

development since the output of any particular oil trajectory model

varies considerably, depending on how the model is applied. This,

after all, is a pretty normal thing to do. You could not buy an

airplane without directions describing it's safe operational use.

The same is true for much less complex products, such as

lawnmowers. It is likewise appropriate for spill modelers to use

standardized guidelines. Any use protocol must consider the context

in which the model is intended for use. Clearly, planning activities,

emergency response, and damage assessment phases of spill

response may require somewhat different use strategies and input

data. For example, emergency spill response is an extremely

complex, multidisciplinary activity. The information that is

developed will have to be used in a larger context. Modern “command

and control” or “integrated information systems” typically will

separate the production of the trajectory analysis from the team

that created it.

To better understand the context of the problem, we will focus on

trajectory analysis support for oil spill emergency response, since

this is where the interpretation of results is the most time-critical.

During emergency response operations, the fragments of information

that are available may be highly uncertain. Any forecasts of

environmental conditions or the arrival of needed response

equipment will thus also be uncertain. In the face of all this, the

response community must sort out what is known, grab what can be

had in terms of equipment, and get it to the places it might do some

good. While this is going on, hundreds of non-responders—in

government, industry, private groups and the press—are forming

opinions based on sparse, possibly wrong data. These opinions

2
rapidly become translated into advice, or demands on response

personnel, setting the stage for the general cacophony that

characterizes most large oil spills. In the face of this chaotic

activity, is there anything that an experienced investigation of

available information and trajectory analysis can do to help guide

the response? What can be done to ensure that any possible gains in

environmental protection are realized? How can responders keep

critical attention and resources from being deflected and focused on

false positives?

To answer these questions, we must consider the general standarduse strategies that would be appropriate and, specifically, the kind

of information that trajectory analysis should add to the

information stream that is supporting the emergency response

efforts. When evaluating various standard-use options for models it

is useful to consider some of the lessons derived from classical

“game theory” (Dresher 1961; Operations Analysis Study Group

1977; Mesterton-Gibbons 1992). In any game that is played with

some unknown, or chance, factors the player can use available

information to try to achieve a “maximum-win” result. An alternate

strategy could begin with the same information (and uncertainty)

and pursue a more conservative option to achieve a “minimumregret” result. In general, the more valuable the resources that the

player is using, the more preferable a minimum-regret strategy is.

As an example, if you had a dollar and wanted to become rich it

might make good sense to put it on the lottery with the chance of a

maximum win. If, on the other hand, you were in control of your

pension fund, a minimum-regret strategy based on bonds and bluechip stocks would seem more appropriate.

In emergency spill response, the inherent uncertainties in

understanding the spill situation and its potential to unfold into the

future, suggest that trajectory analysis should be aimed at

supporting a minimum-regret rather than a maximum-win strategy.

The argument becomes even more compelling when you consider the

valuable resources that can be threatened by spills. To put this into

context, a maximum-win strategy would be one where the very best

estimates of winds, currents, and initial distribution of pollutants

were collected with the resulting forecast taken as “the” threat

that needs to be responded to. This is where a trajectory modeler,

or analyst, would “give it their best shot” and come up with a most

probable scenario. This is, in fact, what most oil spill modelers and

builders of automated decision-support systems seem to think they

3
want. A minimum-regret strategy, on the other hand, would use

whatever analysis techniques are available to investigate the

sensitivity of various estimates of errors in the input data and

would explore the implications of alternate, plausible scenarios in

the geophysical forcing functions. For example, what would be the

significance of an atmospheric frontal passage arriving six hours

before the forecasted time of arrival? Or, if the coastal current is

known to reverse this time of year what would be the consequences

of such a reversal on the planned response options? Clearly, to

carry out this type of analysis, the modeler must understand not

only the capabilities of the models, but must also know what the

models cannot provide. This is obviously more difficult, but, once

done, it can be used to develop briefing material that can provide

response organizations with a “best guess” that covers alternate

possibilities that might present a significant threat. The major

difference between these approaches is that the minimum-regret

strategy can identify less likely, but extremely dangerous or

expensive, scenarios that may require the development of alternate

protection strategies.

The next section describes alternate ways that any response model

can be formulated to examine various facets of the trajectory

analysis problem. The third section briefly describes the postprocessor analysis procedures that could be used on any of the mixed

Eulerian/Lagrangian models to produce a standard trajectory

analysis data presentation that would support a minimum-regret

response activity. The fourth section describes specific digital

formats for electronically transferring both the pollutant

distribution and topological information associated with the

trajectory analysis products. Section 5 discusses the proposed

implementation of this standard by NOAA’s Hazardous Materials

Response and Assessment Division. This section concludes with a

description of NOAA’s cooperative effort with the Florida

Department of Environmental Protection, Florida Marine Research

Institute to develop data transfer protocols to facilitate using

trajectory products with Geographic Information System (GIS) based

spill response systems. File formats and examples are given in the

appendices.

2 Alternate Model Scenario Development

4
Any spill trajectory model that is robust enough to represent a

realistic range of geophysical processes is also flexible enough to

be used in a number of different ways, depending on the supporting

computational algorithms and scenario descriptors. This feature,

which clearly makes the models useful, also makes them produce

results that are, by themselves, vague with regard to their own

credibility. Since there are so many different ways to modify the

behavior of the models, it is impossible to understand the response

options they suggest by simply looking at the undocumented output.

To make model output more useful and understandable in an

emergency spill response, the standard-use protocols need to

internally check for algorithmic accuracy limitations and

uncertainty in the scenario descriptors. The output would not be the

result of a single model run, but rather the composite of a specific

set of runs that includes deterministic and statistical applications

of the same model (Galt 1995). This composite should result in a

technically sound trajectory analysis that provides enough

information to support minimum-regret decision-making. In

addition, the standard-use protocols should not be specific to a

particular trajectory model, but general enough so that all of the

models that are intended for use during a spill event can produce

results that can be compared and evaluated in the same context.

Such a standardized approach to trajectory analysis should go a long

way towards reducing the potential problem of having more than one

estimate on where the pollutant will go and no way to determine the

likelihood of either being correct or, for that matter, whether they

are even “significantly” different.

To develop a standard-use protocol, we will consider how oil

trajectory models have been developed and used. At the core of any

oil trajectory modeling procedure is a series of computational

algorithms, or numerical look-ups, into databases. Trajectory

models must be able to handle mixed-scale problems and, as a

result, all major models have gone to a mixed Eulerian/Lagrangian

formulation where the oil is represented by a series of pollutant

particles embedded in a series of vector fields representing the

regional dynamics and dispersive effects. Each of the oil particles

can have associated attributes that represent its state (age,

weathering condition, beached status, etc.). The distribution of the

oil as a function of time is given by the distribution of the particles

that represent the oil. The raw graphic output appears as a cluster,

5
or swarm, of particles (Figure 1). All known operational models

share these characteristics.

 

Figure 1. Trajectory forecast with oil represented as Lagrangian

Elements.

The potential for alternate model use strategies now becomes

important. Significant differences start to appear in how various

models are used.

When most people think of trajectory modeling, they assume that the

activity will forecast the future distribution of the oil based on its

initial or present distribution. In this case, the modeler sets the

initial distribution of the particles to quantitatively represent the

6
actual distribution of the oil at the beginning of the forecast period.

Next add the best available representation of the vector fields that

describe the regional currents and add them into the transport

modules of the model formulation. An additional vector field is

usually added to represent the effects of the forecast winds on the

surface oil particles. Finally, a random vector component is

typically added to represent sub-grid scale uncertainty associated

with turbulence or mixing processes that are not resolved by the

previous transport representations.

The model is now run forward in time, with all the pollutant

particles moved by the various process vectors, weathered to change

their state variables, and plotted out for various future times

(Figure 2). The result is the oil distribution forecast, or cumulative

model “best guess” of the oil’s movement over time. In this

formulation, the clustering of the oil particles actually represents a

quantitative measure of the amount of oil found in any particular

area. That is, the number of particles per square kilometer would be

proportional to the forecast number of tons per square kilometer.

From a game theory perspective, this is a maximum-win strategy.

The most probable future position of the oil is what most responders

expect from a trajectory model, but the problem with this output, by

itself, is that there is absolutely no indication of its accuracy.

Again, using a game analogy, if one were to throw a pair of dice, the

most probable result would be 7, but responding continually to that

expectation would find you wrong five out of six times. Some

trajectory models have used climatological components where the

most probable cases do not come close to representing a majority of

the potential scenarios and, as in the dice case, you would be wrong

more often than right. Another problem with unaccompanied model

forecast output is that two different models would give two

different results and the differences would be hidden in the set-up

assumptions. Once again, the end-users have no way to resolve

conflicts or estimate the reliability of the advice that is dependent

on the model results.

Although the model forecast is useful, it is necessary to identify

alternate model scenarios that could add the required additional

information. This can be done since virtually all of the mixed

Eulerian/Lagrangian trajectory models can be run in a statistical

mode such that each individual particle or fragment of the spill can

be assigned an independent set of vector fields representing

currents, winds, and dispersive processes. In this case, each of the

7
oil particles can be thought of as an individual piece or fragment of

an independent spill.

Obviously, model results depend on the statistical choices that are

made for the independent currents, winds, and dispersive vector

fields that are applied to each of the separate particles. The model

output presents the position of particles as a function of time and,

in most respects, will look like the results from a straight forecast

run, but the interpretation would be quite different. In this case,

clusters of particles will not represent higher concentrations of oil,

but rather areas that have a higher probability that the represented

spills would lead to oil in those locations.

8
Figure 2. Typical trajectory stepped forward in time.

More specifically, when choosing the independent vector fields used

to statistically represent the probabilistic spill distributions the

modeler should have some idea of the expected errors for all of the

model inputs that are taken as deterministic in the initial forecast.

For example, the hydrodynamic models that are used to approximate

the current patterns may have known error bounds or skill levels.

9
The weather forecast will certainly not be exact and the expectation

is that there will be errors in wind speed and direction as well as

the timing of frontal passages (Haltiner 1971). If all of the vector

transport and dispersion processes are represented by the

deterministic forecast values plus a random component that

realistically spans the expected errors, then the statistical

ensemble will cover the uncertainty that is likely in the forecast.

This approach can be recognized as a Monte Carlo representation of a

formal, first-order error analysis applied to the deterministic

forecast. An appropriate bound that surrounds this probability

distribution will provide the uncertainty analysis that was lacking

in the simple unaccompanied forecast. From a responder’s point of

view, this combination offers a major improvement over just the

forecast by itself.

Minimum regret strategies can now be implemented by looking at

both the forecast and at potentially high-cost problems appearing

within the uncertainty zone. For response to emergency situations

in which real uncertainties are present, adaptive management

methodologies are possible (Holling 1978). This information enables

responders to alter tactics as necessary and redistribute resources

or initiate additional field reconnaissance to empirically check

model uncertainties. Additionally, if each model comes packaged

with its own uncertainty bounds two different models can more

readily be compared. If the differences in the forecasts are simply

different realizations within the expected bounds of the model

reliability, then responders do not need to agonize over conflicting

advice. These are examples of practical response options that would

not be possible using stand-alone model forecasts.

A third strategy is often useful for major spills, even though it is

not technically required to support minimum-regret response

activities. This strategy is, again, based on a statistical

representation (achievable for any of the commonly used trajectory

models) where each individual particle or fragment of oil is subject

to an independent set of vector fields representing currents, winds,

and dispersive processes. The major difference between this and

the previous statistical approach (which developed the uncertainty

bounds) is that this method concentrates on a joint probability

distribution that considers where the oil is initially observed and

the likelihood that it will arrive at predesignated high-value or

environmentally sensitive target areas. This is referred to as

receptor-mode analysis (Gilbert 1983). The spill response problem

10
that receptor-mode analysis attempts to address is associated with

the fact that major spills will invariably remain a problem for

longer periods than any form of deterministic forecast is possible.

It is useful to develop an “extended outlook” estimate of the threat

to specific locations. This is the type of analysis that the National

Weather Service has developed using historical statistics to

estimate extended outlook threats from hurricanes as they move

through the formation areas in the inter-tropical convergence zone

of the Atlantic. These estimates will give at least relative threat

information. Tracking these esimates over time will indicate which

problem areas are subject to growing risk and may need to have

protection strategy resources moved to staging areas for

implementation if the probability of actual impact becomes

significant.

The easiest way to describe the formulation of a receptor-mode

analysis is to consider a statistical model where the spill

originates at the high-value target for which an extended outlook is

desired. The vector fields representing currents, winds, and

dispersive processes are all run backwards so that the oil moves to

where it would have come from, rather than in a forward direction,

as would be expected in a normal forecast model. Since the oil

elements are statistically independent, the resulting distribution is

a probability density map. The probability density at any point in

time is the likelihood that oil starting from that location could

impact the receptor site in the time given. Given any initial oil

distribution, the receptor joint-probability maps can be used to

calculate the cumulative threat. A statistically equivalent

formulation that has been used by the Minerals Management Service

formulates the problem in a computationally more efficient forward

algorithm (Smith et al. 1982). In this case, the model is run forward

for the desired “extended outlook” period and a region is defined to

represent the impact zone associated with each of the receptor

sites. The fraction of mass-weighted oil particles in the site's

region represents the probability that that region will be affected.

There are some questions as to the appropriate way to develop the

statistical vector fields to support an extended outlook, or

receptor-mode analysis. If, by “extended” you mean only slightly

beyond the forecast period, then an estimate of deterministic fields

with increased random-error components is probably appropriate.

If, on the other hand, the desired forecast time is longer than the

typical auto-correlation time for the geophysical processes that are

11
considered, then a statistical representation of the climatology is

more appropriate. In general, you would start with a deterministic

forecast that would degrade over time and smoothly transition into

climatology.

These model scenarios provide the framework for a set of

trajectory-analysis components that support minimum-regret

emergency response to spills. The complexity of spill response

organizations and the technical ability of computational modeling

components have gone beyond the point where a simple

unaccompanied model forecast is an acceptable product for the

demanding requirements of spill information management systems.

At a minimum, standards should require all trajectory forecasts to

be supported by sensitivity analysis that defines the uncertainty in

the forecast. Optionally, there should be a standard method of

including an extended outlook threat to high-value receptor sites.

3 Data Analysis Methods

Run in a forecast mode, the mixed Eulerian/Lagrangian trajectory

analysis models track a number of individual spill components that

represent actual quantities of oil with associated attributes, such

as mass, density, type, age, and coordinate information.

Alternatively, the models may be run in a statistical mode, where

the individual particles represent a probability element. In either

case, the actual model output is a list of particles and their

associated attributes. Although most models preserve this data in

files that are tabular images of these lists, virtually all of them

provide graphic displays to help the end user visualize the

distribution of the oil with respect to a background map. The

standard minimal result is a background map with an overplot of

particles that typically resembles a swarm of bees (Figure 1). Many

models have gone beyond this primitive representation and can

provide alternate views that include animated movie loops and

various color presentations. Given all of the possible alternate data

presentations, we will describe a minimum set of some relatively

simple analysis techniques that could be easily applied to any

trajectory model results. These techniques provide responders with

consistent, well-defined packages of information that directly

support minimum-regret decision logic. These procedures do not

preclude model developers from providing additional value-added

packages and presentations, but make results understandable and

provide a basis for comparison in real-time response environments.

12
We will focus first on the output from trajectory models run in a

forecast mode. The Lagrangian particle lists and their associated

data are the core model output. Although not the most compact form

of trajectory analysis output, any standard should at least support

the ability to transmit each particle's geographic and attribute data

so that the recipient can use it for additional analysis. A much

more compact and, in many respects, more useful representation of

the Lagrangian point data can be developed by converting the

distribution to an Eulerian density field, which can then be described

by a small number of vector contours. This is equivalent to going

from clusters of particles to a measure of particles per area. This

relates directly to a distribution of oil concentration or thickness,

which is a much more useful response parameter than particle

locations. This conversion can be done by dividing the region into

small boxes and counting the number of particles in each box.

Although this is computationally easy it has some defects, such as

depending on the size of the box that is selected. This method

would thus not be suitable for a standard.

A second, more robust, method of converting Lagrangian to Eulerian

data, which self-scales itself to the particle distribution, is to

partition the region occupied by the points into Thiessen polygons or

a “Voronoi diagram.” These polygons associate an area with each

point that defines its exclusive neighborhood relative to any other

point. Dividing the mass of each point by its associated area gives

the required Eulerian density. This technique is commonly used in

hydrology studies to analyze rainfall data (Linsley, et. al. 1982). An

alternate approach is to use the Lagrangian points as the vertices

for a Delaunay triangulation, the topological equivalent of the

Thiessen polygons. With either of these representations the other

can be quickly and easily calculated. Once the Eulerian density data

are obtained, any number of contouring routines can be used. Since

the generation process has produced all of the information necessary

for a triangular interpolation, any of the Triangulated Irregular

Network (TIN) routines should be fast and computationally efficient

(Green and Sibson 1978; Diggle 1983; Sedgewick 1992; Preparata

and Shamos 1988). A number of standard software graphics

libraries (such as Stanford Graphics and Harvard Graphics) include

Delaunay triangulation subroutines. Finally, most of the general GIS

packages provide a variety of TIN routines that include interpolation

and Voronoi diagrams or Delaunay triangles.

13
The proposed Thiessen analysis assumes that the distribution of

floating oil results from an individual spill in an essentially “singly

connected” domain. If there are multiple spill events on a single

map, or if there are complex channels or barrier islands that lead to

a bifurcation of the spill, a modified standard analysis should be

carried out. The distinct and disjoint segments of the floating oil

distribution should be analyzed separately and all the concentration

contours included in the final analysis.

To develop standard trajectory analysis results we need to define

the “relatively few” vector contours of the Eulerian particle density

that should be included in the basic presentation. Since these

considerations are for the models run in a forecast mode, the

Eulerian data correspond to an absolute number of particles per unit

area. In theory, you can translate these values into tons of oil per

square kilometer (one ton per square kilometer would average one

micron thick and appear as dull to dark rainbow colors under light

wind conditions (Fingas et al. 1979)). Experience suggests that

contouring absolute concentration values is not particularly useful.

During the emergency phases of a response the actual amount of oil

spilled is usually not known. Because of this, it is common for the

particles used in the model to be assigned a relative rather than an

absolute mass. That is, each particle will initially represent a

fraction of a percent of the entire spill. An additional factor

against absolute concentration estimates has to do with the way oil

appears when floating in the marine environment. Most observers,

even experienced observers, see relative concentrations. On major

spills hundreds of people will be involved in making observations of

the oil; virtually all of the distribution estimates tend to come back

with areas of heavy concentration, medium concentration, and light

concentrations (Figure 3). Over the life of the spill, as the oil

spreads and dissipates, the heavy concentrations lessen each day,

but they always look heavy compared to other areas.

For these reasons, the standard emergency response contours of oil

concentration should be based on relative rather than absolute

values. The easiest way to do this is to calculate the maximum

concentration value in the distribution and define the standard

contours as some fraction of this value. We need to rely on

observational experience to select the relative contour levels .

Observers’ understanding of “heavy,” “medium,” and “light” is nonlinear. Medium concentrations of oil are typically about

14
Heavy

bands

Medium

Light Sheen

Heavy beaching

Heavy

Light Sheen

Figure 3. Example of oil distribution reported from

an overflight.

15
four times as heavy as light concentrations, and heavy

concentrations are typically about four times as heavy as medium

concentrations. A practical set of contours that defines the density

distribution of the model trajectory forecast includes:

1) forecast areas of light oil concentrations at one percent of

the maximum value for the distribution;

2) forecast areas of medium oil concentrations at four percent

of the maximum value for the distribution; and

3) forecast areas of heavy oil concentrations at sixteen

percent of the maximum value for the distribution (Figure 4).

Figure 4. Example of predicted distribution of heavy,

medium and light concentrations of oil shown as

contours.

The next component of the standard trajectory analysis is the

forecast uncertainty. This, in addition to the most probable forecast

16
distribution, is essential for minimum-regret trajectory analysis.

For this case, the uncertainty in the vector fields representing

currents, winds, and dispersive processes produces a particle

probability distribution that gives the location of some component

from each member of an independent ensemble of spills. The

Eulerian representation of this Lagrangian distribution will be a

probability density. A useful measure of this distribution is the

contour of a relatively low probability density value. A conservative

value is obtained by using the Thiessen analysis described above and

contour the one tenth of a percent. You then add this uncertainty

boundary to the deterministic contours produced by the model runs

representing the forecast (Figure 5).

Figure 5. Contours of predicted heavy, medium and light oil

concentrations and associated uncertainty bound.

17
The contours produced as part of the standard trajectory analysis

forecast and uncertainty bound are derived directly from the

Lagrangian distribution data developed by the basic spill trajectory

model. These contours will scale to the distribution of particles and

show high concentrations wherever particles tend to accumulate.

Since no particular attention has been given to whether the particles

represent floating or beached oil, the contour lines will intersect

shorelines and, in some cases, may even cross peninsulas and small

islands. This is not an indication that concentrations of oil are

found on land or under islands, but rather that some segments of

land are relatively close to significant concentrations of pollutant.

From a graphic information or information content point-of-view,

there is no problem if the base map features are drawn after the

contours. In this way, the land features simply overprint the oil

distribution contour data, information covered up by the land is not

relevant.

The final components of the standard trajectory output relate to

receptor mode, or extended outlook, analysis. This would typically

be an optional addition that might not appear as part of the initial

trajectory analysis support. However, for most large spills, as

issues in the response become clearer and high-value targets are

identified, this type of analysis becomes more important. To

develop long-range planning and resolve potential competition for

available response equipment, information provided by an

extended-outlook analysis can help guide the formulation of

response options. The computational procedures for extended

outlook analysis are straightforward:

• The statistical model formulation used for the

uncertainty analysis continues beyond the forecast period;

• The probabilistic accumulation of particles in the

vicinity of a high-value target provides an estimate of the

relative threat to that resource; and

• The graphic output to support this analysis is simply the

vector polygon surrounding the area that defines the highvalue area’s threat region, and a numerical value that gives

probability of impact (Figure 6).

Figure 7 summarizes the model runs required to provide a standard

minimum regret analysis. These procedures can be applied to any of

the mixed Eulerian/Lagrangian trajectory model formulations

18
typically used for emergency oil spill response. The basic steps in

providing the necessary data to support the analysis are as follows:

1) Define the conditions that initialize the oil distribution;

2) Run a standard forecast (typically 24 hours) using bestavailable estimates for the vector fields representing

currents, winds, and dispersive processes;

3) Return the model to the initial state given in step 1 and run

an uncertainty analysis for the standard forecast period

(again, typically 24 hours) using vector transport and

dispersion processes represented by the deterministic

forecast values plus a random component that realistically

spans the expected errors; and

4) Beginning with the results from step 3, extend the model

another forecast period (out to 48 hours).

19
 82°39'W 82°36'W 82°33'W 82°30'W

27°42'N

27°45'N

27°48'N

27°51'N

Extended Outlook

2/16/96, 0000

17%

< 1%

7%

32%

13%

Figure 6. Probability of impact graphic (extended outlook).

20
Initial 

Conditions

Sensitivity Analysis

Bound

Statistical Area

Analysis

Forecast

Contour

Analysis

Trajectory Analysis Product

+24 hr

+24 hr

+24 hr +48 hr

Figure 7. Summary of standard products.

The standard trajectory analysis product digitally represents:

a) The 1%, 4%, and 16% filled contours from the results of step

2;

b) The 0.1% contour bound from the results of step 3; and

c) Optionally, using the results from step 4, a number of

polygons identifying high-value resource regions and the

numerical probability that they include oil particles.

There must also be a representation of map data, legends, caveats or

restrictions in the model formulation, and some sort of header that

describes the incident name, initialization times, forecast periods,

and originator of the analysis. These will be described in more

detail in the next section.

4 Data Formats

21
A number of data components are required to define a standard-use

trajectory analysis. NOAA is introducing a simplified, publicdomain format to present these data elements digitally. These data

can easily be subdivided into graphic and annotation entities.

The graphic entities will consist of polygons and sets of point data.

The polygon data will include:

1) A graphic representation of the “best available” forecast of

where oil will be at a particular time;

2) A graphic representation of uncertainty analysis so that the

reliability of the forecast can be evaluated;

3) A background map that localizes the graphic displays and

identifies recognizable land forms for reference to the

general position of the spilled pollutant; and

 4)An optional graphic of the statistical threat to specific

high-value resources for an extended forecast period.

The sets of point data consist of the geo-coordinates of parcels that

represent either the quantitative distribution of pollutant or the

probability density of finding pollutant in a particular region. Both

the polygon and point graphic data will typically have attributes

describing the graphic entities. This information will be included in

separate ASCII text files made up of line-feed delineated records

that consist of a fixed number of fields.

In addition to the graphic entities that make up the trajectory

analysis presentation, necessary annotation includes:

 1) A legend that defines the contours of the distribution that

are included as graphic objects;

 2) A scenario description and caveat block that describes the

limitations of the analysis as well as contact information;

and

 3) A title block that identifies the incident name, the sender

of the analysis and the time that it was transmitted as well

as the period for which it is valid.

22
The annotation information will be presented as polygons that define

their recommended position on the composite graphic product,

attributes that contain drawing recommendations, and a reference to

the actual text of the annotation.

The proposed standard-use trajectory analysis message will be

made up of either three or five files, depending on whether the

actual Lagrangian point data will be sent with the analysis. These

will be ASCII text files that can be sent over any electronic mail

system. They will include all of the information necessary to

produce a final screen copy or paper product in color, half-tone gray

scales, or black-and-white images. They will also contain all of the

topological information developed during the analysis so that they

can be loaded into Geographical Information Systems (GISs) that

support geographical analysis with other resource or response data

sets. With planned versions of display software the end user can

produce trajectory analysis results in a format that will support

specific secondary distribution needs. Typically, black and white

for facsimile, half-tone for photocopy, and color for Local Area

Networks (LANs) or direct presentation.

The five files that make up the standard-use trajectory

analysis will be:

1) A Map Overlay Statistical System (MOSS) exchange format

file of polygons that describes the graphic entities to draft

the trajectory analysis product. Table A1 lists the MOSS

polygon primatives that would be included in this file.

Appendix A describes the MOSS exchange format files;

2) A text file that is made up of one record for each of the

polygon primatives defined in the first file. Individual

records are keyed on the entries in File 1 and separated by a

line-feed character. Each record is made up of 15 fields,

separated by commas, that provide the drawing and

quantitative attributes associated with their keyed polygon.

Appendix B describes the data fields in the records of File 2;

3) A file of ordered ASCII text strings, each of which is

preceded by an index and terminated by a line-feed

character. The index of the string can be referenced by the

value field in the File 2 records for any annotation that is

23
recommended as part of the trajectory analysis product.

This file will also contain strings that are preceded by a

zero (0) index. These contain header information that

describes the complete set of files that make up the

trajectory analysis message. These zero (0) fields will not

be referenced by any of the records in File 2. Details of

these fields are described in Appendix C;

4) A MOSS exchange format file of points that describes the

positions of the Lagrangian particles that were generated

for the forecast portion of the standard-use trajectory

analysis. This could be a large file with up to 10,000 points.

Unless the recipient of the trajectory message intends to

carry out additional analysis, it should be considered as an

optional part of the information set; and

5) A text file made up of one record for each of the Lagrangian

particles in the fourth file. Individual records will be keyed

on the entries in File 4 and separated by a line-feed

character. Each record will be made up of fields, separated

by commas, that describe the attributes of the individual

particles. Appendix D describes the data fields in the

records of File 5 for the NOAA model.

The order in which the elements occur in Trajectory analysis File 1

is significant. This is the order in which they should be drawn to

ensure proper masking for the final product. If you were to think of

these as layers in a GIS representation, there would be a “forecast

layer” beneath a “map layer” which in turn would be under an

“extended outlook” layer. Finally, a legend, caveats, and a title

would be overprinted on the final presentation.

Each of the spatial elements listed in Trajectory Analysis File 1

will have additional information that describes its recommended

presentation characteristics found in Trajectory Analysis File 2.

The first field in the information record is the index-element name.

This field must be unique and link back to the graphic elements in

Trajectory Analysis File 1. In the first file, which is in MOSS

format, each graphic object is defined by an index which is 1 through

n where there are n total graphic objects represented in the file.

This provides a unique index. The additional fields in the

information record cover how the polygon boundary is to be

presented. Next are a series of fields that describe how the polygon

24
will be used to produce a color or a half-tone, gray-scale map and,

finally, how the information would be used to produce a black-andwhite map. The next three fields describe the text characters that

annotate the map in its legend, caveats, and title. The final two

fields in the information record present a numerical value

associated with the graphic object or, in the case of text, a record

number to the actual text record that will be contained in Trajectory

Analysis File 3.

Trajectory Analysis File 4 and Trajectory Analysis File 5 are

defined in the standard as optional, because they are not required to

develop the standard graphic minimum-regret trajectory analysis

product. This is because the data represented by the distribution of

the Lagrangian oil particles have been summarized in the Thiessen

analysis that is already included in the first three required files.

Under certain situations, there may be need for more detailed

spatial analysis and, thus, this information should be available

within the defined standard. Trajectory Analysis File 4 will be a

MOSS point file where each of the Lagrangian Elements or oil

particles will be represented by a header line containing an index, an

attribute name, and a coordinate pair count, followed by a line

containing the longitude-latitude coordinate pair in decimal degrees.

Trajectory Analysis File 5 is a list of records separated by linefeed characters. The individual records are made up of eight

required data fields and may be followed by additional custom

fields. The intention here is to make sure that the standard provides

a way for the transmission of essential information that any mixed

Eulerian/Lagrangian formulation would create and at the same time

provide for modelers to present custom enhanced data that

specialized models or sub-models may develop. For example, in the

future advanced weathering models may wish to present distillation

data or toxicological potential for various stages of aging oil. These

extended records would still fit within the standard and they could

still be analyzed using present versions of GIS development

software. In addition, the new data would be available, although the

end user would have to communicate with the model developer to

obtain documentation for use and use strategies. Appendix D

describes the fields that are required in Trajectory Analysis File 5.

Appendix G gives examples of the standard-use trajectory analysis

message files. Figure 8 is a black-and-white version of the

associated graphic product.

25
During the emergency phase of spill response there is additional

distribution information that may be of interest. For example,

observational oil-distribution maps, typically drawn from overflight

reconnaissance by trained observers, are useful to a number of

different responders for planning and evaluation of ongoing

activities. This is information that must be rapidly distributed in

an intelligible format. A digital version or message sent out over an

electronic mail system would be useful, particularly if it can be

easily downloaded as a graphic or integrated into a larger GIS

system. These are clearly the same kinds of issues that are

important in developing the standard-use trajectory analysis

procedures. A small addition to the list of general polygon objects

shown as the last group in Table A1 means that the digital standard

and any specific display packages can be used to distribute the

standard-use trajectory analysis products, the observational

overflight information, and other operational distribution

information that may be generated.

5 Conclusions

We have outlined the standard-use trajectory analysis procedures

that can be used with any mixed Lagrangian/Eulerian model to

produce an informative spill response product that can effectively

support minimum-regret decision-making. Minimum-regret decision

support is essential for trajectory analysis results that are

intended for integration into general “command and control” or

“information management” systems. The NOAA/HAZMAT spill

response team experimented with this standard-use trajectory

analysis product in 1995 and has tested its use in a number of spill

drills and regional planning efforts. The majority of end users in the

response community who have had the opportunity to view the

product conclude that the resulting graphic product is both compact

and informative. NOAA is now moving to make the standard-use

trajectory analysis message a routine part of our emergency

response

26
 82°39'W 82°36'W 82°33'W 82°30'W

27°42'N

27°45'N

27°48'N

27°51'N Relative

Distribution

Light

Medium

Heavy

Confidence

limit

Extended Outlook

2/16/96, 0000

17%

< 1%

7%

32%

13%

Barge 294 Spill MASS Trajectory Analysis

Estimate for: 0600, 2/15/96

Prepared: 1315, 2/14/96 NOAA/HAZMAT/MASS (206) 526-6317

These estimates are based on the latest available information. Please refer to 

the trajectory analysis briefing and your Scientific Support Coordinator (SSC) for 

more complete information. This output shows estimated distributions of heavy, 

light, and medium concentrations as well as an outer confidence line. The 

confidence line is based on potential errors in the pollutant transport processes.

Figure 8. Composite Trajectory Analysis Standard

support. The digital message format that is described in this report

will provide a uniform way to distribute the group’s trajectory

analysis results over the “Hotline” electronic mail system that is

part of the NOAA spill information management system. This

27
information will thus be available to the U.S. Coast Guard, NOAA,

industry, and state responders as quickly as it is produced. In

addition, it will also be available to various managers through their

connections to Regional Response Team representatives. Testing is

underway to introduce observational overflight analysis into the

same message stream, using the same digital format protocols.

Responders, planners, and managers can obtain immediate access to

the digital messages that contain pollutant distribution data in the

form of observational overflight analysis and trajectory analysis

information. It will be necessary for them to have an application

that will take the message as input and produce the graphic screen

image, or paper product, that the information is intended to

represent. Thus, the second part of the information distribution

system will be the development and transmission of a viewing

application. This product should be simple and user-friendly to the

point of requiring no training. A graphical user interface would

provide functionality to allow the user to select a digital message

for display. Options would be available to select a color, half-tone,

or black-and-white product. If the optional Files 4 and 5 are

included in the message, an additional option will make it possible

to display beached oil elements. The product would be displayed on

the screen with a final option to send the image to a printer. NOAA

will develop these simple viewing engines for both WindowsTM and

MacintoshTM personal computer systems. We will provide

consultation for anyone who would like to develop a viewer for some

alternate system. In addition, the proposed file format lends itself

to importation to a variety of commercially available mapping

packages.

The digital message formats described in this report contain a good

deal more information than what is presented in the standard-use

trajectory analysis product. The Lagrangian point data and the

topological relationships in the MOSS files can provide new insights

when used with additional map-based resource data and precompiled response plans. In general, responders and planners who

have access to these additional information resources will be using

GIS-based programs that deal with spatial data in a more structured

format that is capable of both topological and thematic queries.

Working together, the Florida Department of Environmental

Protection and NOAA/HAZMAT are developing translators and

application specific scripts to import the standard-use trajectory

analysis results with all of the associated topological data into

28
NOAA’s Mapping Application for Response, Planning, and Local

Operational Tasks (MARPLOTTM, a display mapping program) and ARC

INFOTM based systems, including Florida’s ArcView2 based Marine

Spill Analysis System (FMAS).

Any of the view engines, translators, or ARC Macro Language (AML)

scripts developed as part of this project will be freely distributed

to responders, planners, and managers who need the oil-spill

distribution observations and trajectory analysis estimates of

future pollutant distributions. The system is based on a publicdomain message format so that other model developers can package

their information in the standard-use trajectory analysis format and

take advantage of the same sort of distribution network.

29
References

ASCE Task Committee. In press. State-of-the-art review of

modeling transport and fate of oil spills. Accepted for publication in

Journal of Hydraulic Engineering.

Diggle, Peter J. 1983. Statistical Analysis of Spatial Point

Patterns. London: Academic Press. 148 pp.

Dresher, Melvin. 1961. Games of Strategy Theory and Applications.

Englewood Cliffs, New Jersey: Prentice-Hall, Inc. 186 pp.

Fingas, Mervin F., Wayne S. Duval and Gail B. Stevenson. 1979. The

Basics of Oil Spill Cleanup. ISBN 0-660-10101-7. Ottawa, Ontario:

Enviornment Canada. pp 22-24.

Galt, Jerry A. 1994. Trajectory analysis for oil spills. J. Adv. Mar.

Tech. Conf. 11: 91-126.

Galt, J.A. 1995. The integration of trajectory models and analysis

into spill response information systems. Presented at the

International Maritime Organization Second International Oil Spill

Research and Development Forum, 23-26 May 1995, London, England.

Gilbert, John T.E., Ed. 1983. Technical Guidelines for Offshore Oil

and Gas Development, Tulsa: PennWell Books. 330 pp.

Green, P.J. and R. Sibson. 1978. Computing Dirichlet tesselations in

the plane. Computer Journal 21: 168-173.

Haltiner, G. J. 1971. Numerical Weather Prediction. New York: John

Wiley and Sons, Inc. 317 pp.

Holling, C.S., 1978. Adaptive Environmental Assessment and

Management. New York: John Wiley and Sons. 377 pp.

Linsley, Ray K., Max A. Kohler, and Joseph L.H. Paulhus. 1982.

Hydrology for Engineers. New York: McGraw-Hill, Inc. 507 pp.

Mesterton-Gibbons, Michael. 1992. An Introduction to GameTheoretic Modelling. Redwood City, California: Addison-Wesley

Publishing Company, Inc. 237 pp.

30
Operations Analysis Study Group. 1977. Naval Operations Analysis.

Annapolis, Maryland: Naval Institute Press. 372 pp.

Preparata, Franco P. and Michael Ian Shamos. 1988. Computational

Geometry An Introduction. New York: Springer-Verlag. 398 pp.

Sedgewick, Robert. 1992. Algorithms. Reading, Massachusetts:

Addison-Wesley, Inc. 551 pp.

Smith, R.A., J.R. Slack, T. Wyant, and K.J. Lanfear. 1982. The Oilspill

Risk Analysis Model of the Geological Survey. United States

Geological Survey Professional Paper 1227. Alexandria, Virginia:

United States Geological Survey.

31
Appendix A: Trajectory Analysis Files 1 & 4 (MOSS format)

Trajectory Analysis Files 1 and 4 use the MOSS standard input file

format. Unlike the formats for Files 2, 3, and 5, the MOSS standard

format uses fixed-length records.

MOSS STANDARD INPUT FILE FORMAT

 FILE TYPE: STANDARD MOSS INPUT

 FILE LENGTH: VARIABLE

 RECORD TYPE: HEADER (REPEATING SERIES OF HEADER RECORDS PRE-

 CEEDING ASSOCIATED COORDINATE PAIR DATA)

 (READ IN I5,10X,15A2,5X,I5 FORMAT)

 RECORD LENGTH: 56 CHARACTERS

 RECORD POSITION: (1) BEFORE START OF ASSOCIATED COORDINATE DATA

 *WORD BYTE FIELD FIELD FIELD

 NUMBER NUMBER LENGTH TYPE DESCRIPTION

 ------ ------ ------ ----- -----------------------

 1-3 1-5 5 CHAR Item Number(NEGATIVE IF

 THE COORDINATES ARE 

LON/LAT)

 3-8 6-15 10 CHAR Blanks

 8-23 16-45 30 CHAR Attribute Name

 23-25 46-50 5 CHAR Blanks

 26-28 51-55 5 CHAR Number of coord. pairs

 28 56 1 CHAR ASCII NEW LINE CHARACTER

 FILE TYPE: STANDARD MOSS INPUT

 FILE LENGTH: VARIABLE LENGTH

 RECORD TYPE: COORDINATE PAIRS (SERIES OF X,Y COORDINATES 

FOLLOWING HEADER RECORD)

 RECORD LENGTH: 23 CHARACTERS

 RECORD POSITION: AFTER HEADER RECORD (NUMBER OF COORDINATE RECORDS

 EQUALS THE VALUE INDICATED BY BYTES 51-55 IN THE

 HEADER RECORD) (READ IN 2F11.2 FORMAT)

 *WORD BYTE FIELD FIELD FIELD

 NUMBER NUMBER LENGTH TYPE DESCRIPTION

 ------ ------ ------ ----- -----------------------

 1-6 1-11 11 CHAR X Coordinate

 6-11 12-22 11 CHAR Y Coordinate

 12 23 1 CHAR ASCII NEW LINE CHARACTER

 IF COORDINATES ARE LONGITUDE/LATITUDE (READ IN 2F10.5,I2 FORMAT)

 1-5 1-10 10 CHAR LONGITUDE

 6-10 11-20 10 CHAR LATITUDE

 11 21-22 2 CHAR FLAG 0-NORMAL 1-

INDICATES FIRST POINT OF

ISLAND POLYGON

 12 23 1 CHAR ASCII NEW LINE CHARACTER

 *NOTE: Assumes 2 Alpha characters (2 bytes) to one word.

32
File 1 & 4 record names and order

In the MOSS standard input file format each each object in Files 1

and 4 start with a header record line. The header includes a 30-

character “attribute name,” i.e., the name of the object that starts

with the given header.

In File 4, each Lagrangian Element (LE) point object is given the

name LE POINT. The points are not assumed to be in any particular

order; they are not sorted by type, location, status, etc.

In File 1, however, objects are named according to their function in

the overall trajectory analysis graphic, and the order in which the

objects appear in the file is important. In particular, the objects

are listed in draw order, so that the first object in the file should be

drawn first, and the last object should be drawn last. This is

important in case two or more objects overlay each other. For

example, if the legend overlaps part of the map, it is drawn over tha

map and partially obscures it.

The table below lists the names of the objects in File 1 in the order

they are written by NOAA’s standard trajectory analysis program.

(Note, however, that any given file written by the trajectory

analysis might not contain every type of object listed in the table).

A check in the multiple column means that more than one object

with the given name can appear sequentially. For instance, the file

may start with a run of several objects with the name BACKGROUND.

The check in the rectangle column means that the object is a

rectangle, as opposed to a general polygon. (Note that a rectangle is

just a special case of a polygon and thus all records in File 1

represent polygons, as they must to fit the MOSS standard.)

For information on the display column, see Appendix B. F

represents FILLED. B represents BOUNDED. B+F represents

BOUNDED+FILLED. T represents CLIPTEXT.

33
Table A1. File 1 objects.

attribute (object) name multiple rectangle display

BACKGROUND F

FORECASTLIGHT B (+F)

FORECASTMEDIUM B (+F)

FORECASTHEAVY B (+F)

FORECASTUNCERTAINTY B

MAPLAND (B+) F

MAPBOUND B

EXTENDEDOUTLOOKTHREAT B+F

LEGENDBOUND B+F

LEGENDTEXT T

LEGENDBAR (B+) F

ARROW B

CAVEATBOUND B

CAVEATTEXT T

TITLETEXT T

OVERFLIGHTFEATURE F

ANNOTATIONNOTE T

OBSERVATIONALTRACKLINE B

The lines for the different types of contour polygons (BACKGROUND ...

FORECASTUNCERTAINTY) are blocked off and contain only a single

check in the multiple column to indicate that, due to the complex

nature of the contour graphics, these objects may appear in any

order within the group.

Also, the lines of the table for LEGENDBOUND, LEGENDTEXT, and

LEGENDBAR are blocked off to indicate that this sequence of three

types of objects can be repeated any number of times. That is, you

might see one sequence (a LEGENDBOUND followed by some number of

34
LEGENDTEXT objects followed by a LEGENDBAR) followed by one or

more other such sequences.

35
Appendix B: Trajectory Analysis File 2

This file is a list of text records, one per line, that specify the

extended attributes of the objects in File 1. Each record consists of

the 15 fields listed below. The fields are separated from each other

by the comma character (ASCII 44), and the line is terminated with a

LINEFEED character (ASCII 10).

• the item number (corresponding to the item number for the

given object in File 1)

• the item name (corresponding to the attribute name for the

given object in File 1)

• one of the following keywords specifying the overall display

characteristics of the polygon:

BOUNDED: draw a solid line around the border

of the object

FILLED: fill the interior of the object with

the given pattern

BOUNDED+FILLED: both BOUNDED and FILLED

CLIPTEXT: the object is a rectangle into which

a line of text is to be drawn (the

object is neither bounded nor filled

with a pattern)

• the penwidth in points to use when drawing the object;

for CLIPTEXT objects, specify 1

• one of the following keywords specifying the type of line

to use when drawing the boundary of the object: SOLID,

DOTTED, DASHED, NONE; use NONE for any object that is not

BOUNDED

NOTE: The boundary should only be drawn when the object

is filled with one of the patterns in appendix E or when

the object is filled with the color white. In all other

cases, treat the object as if the boundary value were

NONE. When drawing a boundary, draw it in black, unless

36
the object is not filled, in which case draw it using the

object’s color or gray scale value (black when in black

and white mode).

• a number giving the red component of the RGB

specification of the color of the object, expressed as a

percent value from 0 (no red) to 100 (full red).

• the green component of the RGB value

• the blue component of the RGB value

• a percent value (0 - 100) specifying the grayscale

“darkness” of the object (how dark the object would appear

when viewed in black-and-white)

• one of the following keywords specifying the pattern to

use when filling the interior of the object (see Appendix E for

pattern samples): LIGHTOIL, MEDIUMOIL, HEAVYOIL, MESH,

LAND, SOLID, NONE; use NONE for any object that is not FILLED

• the name of the font to use when drawing the text of a

CLIPTEXT object; use a standard font name such as COURIER

(see Appendix F for font samples); for non-text objects,

specify NONE

• the font size in points to use when drawing the text of a

CLIPTEXT object; for non-text objects, specify 0

• the font style to use when drawing the text of a

CLIPTEXT object; use one of the following keywords: PLAIN,

BOLD, ITALICS ; for non-text objects, specify NONE

• a number specifying the value of the object; this value

varies depending on the type of object

• one of the following keywords specifying the units or

“type” of the value in the preceding field:

TEXTINDEX: use this keyword for all CLIPTEXT

objects. In this case, the value field

specifies the index number of the

object’s text in File 3

37
RELATIVE: use this value for oil contour

polygons, where the contoured

Eulerian density value has been

computed as falling a certain percent

of the distance between the lowest

and highest density among all LE

points in the data set. In this case,

the value field specifies that percent

value

TONSPERSQKM: use this value for oil contour

polygons, where the contoured

Eulerian density value is an absolute

number of metric tons per square

kilometer. In this case, the value

field specifies that density

PROBABILITY: use this value for oil contour

polygons that represent probabilities

of finding oil in a given region. In

this case, the value field specifies

the probability as a percent value of

finding oil in the given region

NONE: use this value for all other objects.

In this case, specify 0 in the value

field

38
Appendix C: Trajectory Analysis File 3

This file is a list of text records, one per line. Each line contains a

non-negative integer index number followed by a comma character

(ASCII 44) followed by a string of text. Each line is terminated with

a LINEFEED character (ASCII 10).

There are actually two types of records in this file. The first six or

more lines have zero as their index number, as described below. The

remaining lines have positive index numbers counting up

incrementally from one. Each corresponds to the “value” field of a

CLIPTEXT rectangle in Files 1 and 2, and which represents a line of

text to be drawn on the trajectory analysis graphic picture. Thus,

Files 1, 2, and 3 work together to define a text object. A record in

File 1 gives the bounding rectangle into which the text is to be

drawn. A corresponding record in File 2 (which has the same item

number as the File 1 record) gives extended graphical attributes and

contains a positive integer index in its “value” field. Finally, File 3

contains a line whose positive index matches the value, and which

contains the actual text to be written into the rectangle.

Because the text of each object must appear on its own line in File

3, each rectangular text object can only contain a single line of text.

Thus, to represent multiple lines of text, you must include one text

object for each line. In addition to keeping the File 3 format

simpler, this one-line-per-object policy allows for a greater degree

of consistency in the look of multiple-line text across platforms.

Returning now to the first several lines at the top of File 3, these

records all have zero (0) as their index number. For these records,

the string of text following the comma is not meant to be drawn on

the analysis picture (although there may be a text object in the

picture that contains the same text as one of these records), but

rather represents general information that applies to the set of all

five files as a whole. Each of these strings starts with a keywordcolon sequence, where the keyword specifies type of information

provided on the given line. Any File 3 must start with the following

six lines:

0, SPILLID: <the name of the spill/trajectory>

0, FROM: <person or organization that generated the output>

0, CONTACT: <name and phone number of person or

organization>

39
0, ISSUED: <date and time that files were generated>

0, VALIDFOR: <date and time represented by trajectory in the

file>

0, ADDLEDATA: <tags for additional LE data (see below)>

The text in <brackets> would be replaced with text appropriate for

the given set of files. Date and time information is specified in the

following format: mm/dd/yy, tttt. For example, if the file was

generated January 10, 1996, at 3:30 pm, the fourth line of File 3

would read

0, ISSUED: 1/10/96, 1530

Optionally, any number of additional zero-keyword-colon records

(beyond the six required ones) can be provided, using any userdefined keywords. These fields can hold user-defined information

that applies to the set of all five files as a whole. However, only

the six keywords above are guaranteed to be recognized by any

particular reader of the files.

The ADDLEDATA field is used to indicate the presence of userdefined LE-specific data. File 5 contains eight standard data fields

associated with each LE (in addition to the latitude/longitude fields

in File 4). Suppose that, in addition to these fields, you also wanted

to keep values representing toxicological potential and distillation

information for each LE point. You could do this by specifying two

tags on the ADDLEDATA line (separated by commas):

0, ADDLEDATA: TOXPOTENTIAL, DISTILLINFO

This is a flag that two additional fields (following the eight

standard ones) are expected on each line of File 5. Like the other

fields of File 5, these additional fields are separated by commas.

If you do not have additional LE data, do not list any tags on the

ADDLEDATA line.

40
Appendix D: Trajectory Analysis File 5

This file lists text records, one per line, that specify the extended

attributes of the points in File 4 . Each record consists of the eight

fields listed below, plus optional user-defined fields (see Appendix

C). The fields are separated by the comma character (ASCII 44), and

the line is terminated with a LINEFEED character (ASCII 10).

• the item number (corresponding to the item number for the

given point in File 4)

• one of the following keywords specifying the type of the LE:

ABSOLUTEMASS, RELATIVEMASS, RELATIVEPROBABILITY

• one of the following keywords specifying the pollutant

substance that the point represents: GAS, JP4, JP5, DIESEL,

IFO, BUNKER, LIGHTCRUDE, MEDIUMCRUDE, HEAVYCRUDE, LAPIO,

CONSERVATIVE

• a number specifying the depth (z-coordinate) of the LE, in

meters

• a number specifying the mass of the LE in kilograms

• a number specifying the density of the LE in grams/cubic

centimeter

• the age of the LE since release, in seconds

• one of the following keywords specifying the status of the

LE: INWATER, ONBEACH, OFFMAP

41
Appendix E: Pattern Samples

LIGHTOIL: a sparse pixel pattern giving a “light gray” look

MEDIUMOIL: a medium pixel pattern giving a “gray” look

HEAVYOIL: a dense pixel pattern giving a “dark gray” look

MESH: a box pixel pattern giving a “mesh” look

LAND: diagonal stripes

42
Appendix F: Font Samples

Courier is a fixed-width, serif font.

Courier PLAIN:

ABCDEFGHIJKLMNOPQRSTUVWXYZ

abcdefghijklmnopqrstuvwxyz

0123456789

!@#$%^&*()-+[]{}=_<>,./?;:

Courier BOLD:

ABCDEFGHIJKLMNOPQRSTUVWXYZ

abcdefghijklmnopqrstuvwxyz

0123456789

!@#$%^&*()-+[]{}=_<>,./?;:

Courier ITALICS:

ABCDEFGHIJKLMNOPQRSTUVWXYZ

abcdefghijklmnopqrstuvwxyz

0123456789

!@#$%^&*()-+[]{}=_<>,./?;:

43
Appendix G: Sample Trajectory Analysis Files

Trajectory Analysis File 1

 -1 FORECASTLIGHT 199

 -82.55445 27.78205 0

 -82.55524 27.78013 0

 -82.55535 27.77981 0

 -82.55520 27.77967 0

 -82.55577 27.77876 0

 ...

 ...

 -82.56727 27.77258 0

 -82.56712 27.77260 0

 -82.56663 27.77284 0

 -2 FORECASTMEDIUM 359

 -82.55576 27.77926 0

 -82.55614 27.77864 0

 -82.55634 27.77745 0

 -82.55797 27.77791 0

 ...

 ...

 -82.57507 27.77873 0

 -82.57459 27.77916 0

 -82.57455 27.77960 0

 -3 FORECASTMEDIUM 7

 -82.56663 27.77284 0

 -82.56712 27.77260 0

 -82.56727 27.77258 0

 -82.56732 27.77287 0

 -82.56736 27.77346 0

 -82.56689 27.77307 0

 -82.56663 27.77284 0

 -4 FORECASTHEAVY 221

 -82.55889 27.78050 0

 -82.55902 27.78032 0

 -82.55960 27.77986 0

 -82.55982 27.77980 0

 ...

 ...

 -82.56995 27.77759 0

 -82.56975 27.77745 0

 -82.56915 27.77708 0

...

...

 -16 FORECASTUNCERTAINTY 52

 -82.52065 27.78998 0

 -82.52280 27.78676 0

 -82.52868 27.76246 0

 -82.52784 27.75668 0

 -82.52905 27.75800 0

 ...

 ...

 -82.53584 27.80169 0

 -82.52071 27.79122 0

 -82.52065 27.78998 0

 -17 MAPLAND 532

44
 -82.66626 27.86255 0

 -82.66626 27.70407 0

 -82.66601 27.70400 0

 ...

 ...

 -82.65968 27.86255 0

 -82.66170 27.86255 0

 -82.66374 27.86255 0

 -82.66578 27.86255 0

 -82.66626 27.86255 0

...

...

 -33 MAPBOUND 5

 -82.67815 27.68012 0

 -82.46128 27.68012 0

 -82.46128 27.86496 0

 -82.67815 27.86496 0

 -82.67815 27.68012 0

 -34 EXTENDEDOUTLOOKTHREAT 5

 -82.60910 27.80966 0

 -82.61135 27.79272 0

 -82.60290 27.79272 0

 -82.60290 27.80966 0

 -82.60910 27.80966 0

 -35 EXTENDEDOUTLOOKTHREAT 5

 -82.62824 27.74640 0

 -82.62936 27.73445 0

 -82.62148 27.73495 0

 -82.62205 27.74640 0

 -82.62824 27.74640 0

 -36 EXTENDEDOUTLOOKTHREAT 5

 -82.62767 27.76334 0

 -82.62767 27.74742 0

 -82.62148 27.74742 0

 -82.62148 27.76135 0

 -82.62767 27.76334 0

...

...

 -39 LEGENDBOUND 5

 -82.51990 27.79999 0

 -82.46754 27.79999 0

 -82.46754 27.85489 0

 -82.51990 27.85489 0

 -82.51990 27.79999 0

 -40 LEGENDTEXT 5

 -82.50507 27.84638 0

 -82.46842 27.84638 0

 -82.46842 27.85025 0

 -82.50507 27.85025 0

 -82.50507 27.84638 0

 -41 LEGENDTEXT 5

 -82.51030 27.84213 0

 -82.46842 27.84213 0

 -82.46842 27.84599 0

 -82.51030 27.84599 0

 -82.51030 27.84213 0

...

...

45
 -65 CAVEATBOUND 5

 -82.67815 27.86496 0

 -82.46128 27.86496 0

 -82.46128 27.89583 0

 -82.67815 27.89583 0

 -82.67815 27.86496 0

 -66 CAVEATTEXT 5

 -82.67815 27.88966 0

 -82.46128 27.88966 0

 -82.46128 27.89583 0

 -82.67815 27.89583 0

 -82.67815 27.88966 0

...

...

 -71 TITLETEXT 5

 -82.65646 27.91267 0

 -82.48297 27.91267 0

 -82.48297 27.92109 0

 -82.65646 27.92109 0

 -82.65646 27.91267 0

 -72 TITLETEXT 5

 -82.65646 27.90425 0

 -82.48297 27.90425 0

 -82.48297 27.91267 0

 -82.65646 27.91267 0

 -82.65646 27.90425 0

 -73 TITLETEXT 5

 -82.65646 27.89584 0

 -82.48297 27.89584 0

 -82.48297 27.90425 0

 -82.65646 27.90425 0

 -82.65646 27.89584 0

46
Trajectory Analysis File 2

-1, FORECASTLIGHT, BOUNDED+FILLED, 1, SOLID, 0, 100, 100, 10, LIGHTOIL, NONE, 0, NONE, 1, RELATIVE

-2, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-3, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-4, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-5, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-6, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-7, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-8, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-9, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-10, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-11, FORECASTMEDIUM, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 4, RELATIVE

-12, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-13, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-14, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-15, FORECASTHEAVY, BOUNDED+FILLED, 1, SOLID, 0, 0, 82, 50, HEAVYOIL, NONE, 0, NONE, 16, RELATIVE

-16, FORECASTUNCERTAINTY, BOUNDED, 1, SOLID, 0, 0, 0, 100, NONE, NONE, 0, NONE, 0.100000, RELATIVE

-17, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-18, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-19, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-20, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-21, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-22, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-23, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-24, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-25, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-26, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-27, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-28, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-29, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-30, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-31, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-32, MAPLAND, BOUNDED+FILLED, 1, SOLID, 56, 44, 22, 75, LAND, NONE, 0, NONE, 0, NONE

-33, MAPBOUND, BOUNDED, 1, SOLID, 0, 0, 0, 100, NONE, NONE, 0, NONE, 0, NONE

-34, EXTENDEDOUTLOOKTHREAT, BOUNDED+FILLED, 1, SOLID, 0, 0, 0, 100, MESH, NONE, 0, NONE, 0, NONE

-35, EXTENDEDOUTLOOKTHREAT, BOUNDED+FILLED, 1, SOLID, 0, 0, 0, 100, MESH, NONE, 0, NONE, 0, NONE

-36, EXTENDEDOUTLOOKTHREAT, BOUNDED+FILLED, 1, SOLID, 0, 0, 0, 100, MESH, NONE, 0, NONE, 0, NONE

-37, EXTENDEDOUTLOOKTHREAT, BOUNDED+FILLED, 1, SOLID, 0, 0, 0, 100, MESH, NONE, 0, NONE, 0, NONE

-38, EXTENDEDOUTLOOKTHREAT, BOUNDED+FILLED, 1, SOLID, 0, 0, 0, 100, MESH, NONE, 0, NONE, 0, NONE

-39, LEGENDBOUND, BOUNDED+FILLED, 2, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-40, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 1, TEXTINDEX

-41, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 2, TEXTINDEX

-42, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 3, TEXTINDEX

-43, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 4, TEXTINDEX

-44, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 5, TEXTINDEX

-45, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 6, TEXTINDEX

-46, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 7, TEXTINDEX

-47, LEGENDBAR, BOUNDED+FILLED, 1, SOLID, 0, 100, 100, 10, LIGHTOIL, NONE, 0, NONE, 0, NONE

-48, LEGENDBAR, BOUNDED+FILLED, 1, SOLID, 0, 45, 100, 25, MEDIUMOIL, NONE, 0, NONE, 0, NONE

-49, LEGENDBAR, BOUNDED+FILLED, 1, SOLID, 19, 19, 19, 50, HEAVYOIL, NONE, 0, NONE, 0, NONE

-50, LEGENDBAR, BOUNDED, 1, SOLID, 94, 3, 51, 100, NONE, NONE, 0, NONE, 0, NONE

-51, LEGENDBOUND, BOUNDED+FILLED, 1, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-52, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 8, TEXTINDEX

-53, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 9, TEXTINDEX

-54, LEGENDBAR, BOUNDED+FILLED, 1, SOLID, 0, 0, 0, 100, MESH, NONE, 0, NONE, 0, NONE

-55, LEGENDBOUND, BOUNDED+FILLED, 1, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-56, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 10, TEXTINDEX

-57, LEGENDBOUND, BOUNDED+FILLED, 1, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-58, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 11, TEXTINDEX

-59, LEGENDBOUND, BOUNDED+FILLED, 1, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-60, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 12, TEXTINDEX

-61, LEGENDBOUND, BOUNDED+FILLED, 1, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-62, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 13, TEXTINDEX

-63, LEGENDBOUND, BOUNDED+FILLED, 1, SOLID, 100, 100, 100, 0, ERASE, NONE, 0, NONE, 0, NONE

-64, LEGENDTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 14, TEXTINDEX

-65, CAVEATBOUND, BOUNDED, 1, SOLID, 0, 0, 0, 100, NONE, NONE, 0, NONE, 0, NONE

-66, CAVEATTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 15, TEXTINDEX

-67, CAVEATTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 16, TEXTINDEX

47
-68, CAVEATTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 17, TEXTINDEX

-69, CAVEATTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 18, TEXTINDEX

-70, CAVEATTEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 19, TEXTINDEX

-71, TITLETEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 12, BOLD, 20, TEXTINDEX

-72, TITLETEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 21, TEXTINDEX

-73, TITLETEXT, CLIPTEXT, 1, NONE, 0, 0, 0, 100, NONE, COURIER, 9, PLAIN, 22, TEXTINDEX

Trajectory Analysis File 3

0, SPILLID: Barge 294 Spill

0, FROM: MASS Trajectory Analysis

0, CONTACT: NOAA/HAZMAT/MASS (206) 526-6317

0, ISSUED: 1315, 2/14/96

0, VALIDFOR: 0600, 2/15/96

0, ADDLEDATA:

1, Relative

2, Distribution

3, Light

4, Medium

5, Heavy

6, Confidence

7, limit

8, Extended Outlook

9, 2/16/96, 0000

10, 17%

11, < 1%

12, 7%

13, 32%

14, 13%

15, These estimates are based on the latest available information. Please refer to

16, the trajectory analysis briefing and your Scientific Support Coordinator (SSC) for

17, more complete information. This output shows estimated distributions of heavy,

18, light, and medium concentrations as well as an outer confidence line. The

19, confidence line is based on potential errors in the pollutant transport processes.

20, Barge 294 Spill -- MASS Trajectory Analysis

21, Estimate for: 0600, 2/15/96

22, Prepared: 1315, 2/14/96 -- NOAA/HAZMAT/MASS (206) 526-6317

Trajectory Analysis File 4

 -1 LE POINT 1

 -82.56188 27.78173 0

 -2 LE POINT 1

 -82.56647 27.78349 0

 -3 LE POINT 1

 -82.57030 27.77765 0

 -4 LE POINT 1

 -82.56454 27.77574 0

 -5 LE POINT 1

 -82.56149 27.78466 0

 -6 LE POINT 1

 -82.56712 27.78156 0

 -7 LE POINT 1

 -82.56226 27.78638 0

 -8 LE POINT 1

 -82.56493 27.78469 0

 -9 LE POINT 1

 -82.56196 27.78148 0

48
 -10 LE POINT 1

 -82.56775 27.77937 0

 -11 LE POINT 1

 -82.56625 27.78047 0

...

...

Trajectory Analysis File 5

-1, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-2, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-3, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-4, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-5, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-6, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-7, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-8, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-9, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-10, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

-11, RELATIVEMASS, MEDIUMCRUDE, 0.000000, 1.000000, 0.950000, 18.000000, INWATER

...

...

49