IMPROVING THE DISPLAY OF WIND PATTERNS AND OCEAN CURRENTS

The theory of human pattern perception is applied to the portrayal of winds, waves, and

ocean currents, resulting in significant improvements in interpretation.

while a great deal of effort has gone into

building numerical weather and ocean

prediction models during the past 50 years,

less effort has gone into the visual representation

of output from those forecast models and many of

the techniques used are known to be ineffective. In

particular, the representation of vector fields (winds,

currents, or waves) is almost always done using grid

patterns of small arrows or wind barbs (Fig. 1) despite

studies showing other methods to be far superior

(Laidlaw et al. 2005; Pilar and Ware 2013). Other

methods such as streamlines have been available for

decades (e.g., Saucier 1955) but are rarely used. Yet

representation is critical; ultimately, visualization

is the only viable method for interpreting complex

patterns of winds and currents as well as for scalar

fields such as pressure and temperature. The need for

improved visualization methods becomes even more

significant with the continued increases in the spatial

resolution and data density of numerical ocean and

weather forecast models.

Most prior research into the visualization of

ocean currents and winds has focused on applying

new visualization techniques to the model output.

Examples include methods for creating equally

spaced streamlines (Turk and Banks 1996; Jobard

and Lefer 1997), applying the technique of line inte-

gral convolution (Jobard et al. 2002) whereby digital

noise patterns are advected in the direction of flow,

and applying volume rendering methods (Max et al.

1993; Kniss et al. 2002) to 3D flow model output.

Others have examined the technical problem of

dealing with nested grids commonly used with flow

models (Treinish 2000) or time-varying gridded data

(Doleisch et al. 2005). There have also been tour de

force designs such as Baker and Bushell’s (1995) care-

fully crafted, unique representation of a storm cloud

done in consultation with Edward Tufte (Tufte 1997).

In contrast, relatively little effort has gone into

formal evaluation aimed at comparing different por-

trayal methods. An exception is Laidlaw et al.’s (2005)

study of six different alternative representations of the

same flow pattern. Among other things, this revealed

equally spaced streamlines to be more effective than

arrow grids. Another study by Martin et al. (2008)

showed systematic biases in the perception of wind

direction when grids of wind barbs were used to show

spiral patterns around a hurricane center. These stud-

ies provide invaluable insights and are first steps in

placing flow representation on a scientific footing.

But their findings are difficult to generalize. There

are many ways that arrows can be used to show flow,

for example. The arrows can have different spacing,

lengths, widths, and colors. Perhaps the result of

Laidlaw et al.’s (2005) study would have been differ-

ent with different kinds of arrows. The solution to

this problem is to develop a theory of effective flow

representation. Such a theory can guide designs, and

it can be tested and refined by means of experiments

with human participants. We contend that such a

theory should be based primarily on the science of

human perception.

The effectiveness of a data display depends on

how well critical patterns can be perceived and vision

science can tell us something about this. In this

paper, we outline the perceptual principles for what

makes a good representation of a 2D vector field and

show how these principles can be used in design. We

present a number of displays that we have designed

according to these principles and evaluated in vari-

ous ways using forecast guidance from the National

Oceanic and Atmospheric Administration (NOAA)

and U.S. Navy operational numerical weather and

oceanographic forecast modeling systems.

PERCEPTUAL PRINCIPLES FOR REPRE-

SENTING VECTOR FIELDS. From the point of

view of understanding what makes a flow visualiza-

tion effective, the most important part of the brain

is the primary visual cortex (V1). Fortunately, more

than 50 years of neuroscience research has investi-

gated the operation and function of this part of the

brain. It is here that the incoming signal from the

optic nerves of the two eyes is processed in parallel by

several billion neurons (there are only a million fibers

in each optic nerve) (Fig. 2a). Figure 2b illustrates a

slice through a small section of cortical tissue in V1

showing the functional structure. This is a synthesis

from hundreds of experiments (Livingstone and

Hubel 1988) revealing regions that process the signal

in three distinct ways; some areas break down the

incoming information into local color difference in-

formation, other areas contain neurons that respond

preferentially to moving patterns, and a third type of

area breaks down the information into local orienta-

tion and size information providing the elements of

both form perception and texture perception. The

regions provide different visual channels that separate

different kinds of visual information. It is important

to emphasize the parallel nature of this processing,

whereby every part of the image falling on the retina

is simultaneously decomposed through these mecha-

nisms; the image is broken down in terms of color

differences, moving elements, and the basic form and

shape perception (local orientation and size).

There is general consensus that the orientation

detectors in V1 form part of a contour detection

mechanism that is critical for detecting the boundar-

ies of objects. Neurons that are smoothly aligned tend

to mutually reinforce one another, whereas those that

are not aligned are mutually inhibitory (Field et al.

1993). The result is a kind of winner-take-all effect

for aligned contour segments: they stand out clearly

whereas nonaligned segments are deemphasized.

This mechanism both reinforces the perception of

smoothly varying continuous contours and sharpens

up orientation tuning as illustrated in Fig. 2c.

Central to our theory is the following principle:

to show flow orientation clearly, a display should be

designed so that, as far as possible, all neurons that

encode orientation should signal orientations that are

tangential to the flow direction. Responses that are

not tangential to the flow direction will lead to incor-

rect judgments of flow orientation (Ware 2008). If we

consider the use of short line segments to represent

a vector field, then this theory predicts that certain

arrangements of the lines will be more effective than

others. Many visualizations of flow use a simple grid of

arrows or wind barbs to show the vec-

tor field (e.g., Trafton and Hoffman

2007). Figure 3a suggests that this

will not be as effective in stimulating

tangential responses as other solu-

tions. Also, an arrow grid will cause

a neural response to the grid itself,

an irrelevant distraction. Arranging

arrows so that they are smoothly

aligned (Fig. 3b) will be better, but

best of all will be a visualization

consisting of continuous streamlines

(Fig. 3c). This theoretical proposition

has been supported both by experi-

ments with humans and by models

that computationally simulate the

processing of contours in the human

visual cortex (Pineo and Ware 2010).SHOWING THE VECTOR SIGN. It is common

to decompose vectors into components of direction

and magnitude (speed in the case of flow pattern).

When discussing the visualization of vector fields it

is useful to further decompose direction into orienta-

tion and sign as shown in Fig. 4.

A continuous contour, such as a streamline, tan-

gential to flow direction may provide the best way

of showing flow orientation but it is still ambiguous

with respect to direction. To resolve the directional

ambiguity some form of asymmetry along the con-

tour is needed. Arrowheads are a common way of

providing along-contour asymmetry, but they are

likely not the best way. A neural mechanism that can

encode directionality is a type of V1 neuron called an

end stopped cell, and such cells respond best to linesthat terminate in the receptive field of the cell from

a particular direction. While a conventional arrow

will provide some asymmetry of response, because

the head will provoke a stronger response than the

tail, a stronger asymmetrical response will come

from other patterns. Examples are given in Fig. 5b.

Such glyphs are not new; Tufte (1983) reproduces a

map of North Atlantic currents drawn by Sir Edmund

Halley in 1686, using elongated teardrop streamlets

arranged head to tail in streamlines. However, use of

these types of glyphs is almost unknown in modern

practice.

STREAMLETS TO SHOW OCEAN

CURRENT PATTERNS. The theory we have

outlined suggests that in order to show a vector field

map, the best solution will be to use a dense pattern

of streamlines and along each streamline place

elements that have a much stronger head than tail.

To put this theory into practice, we implemented

Jobard and Lefer’s (1997) algorithm to create equally

spaced streamlines. Along the streamlines are placed

streamlets, graphical elements that have a much more

salient head than tail.

We carried out a human-in-the-loop optimization

study to determine values for the

remaining free parameters, such as

how to represent flow speed, how to

space the streamlines, the head and

tail size, and the head and tail trans-

parency of the streamlets (Mitchell

et al. 2009). Our interface had a set

of interactive sliders enabling study

participants to adjust each of the 22

parameters controlling the map-

ping of the data to a display, starting

from random values. Participants

were instructed to produce the best

representation they could. Some

participants were designers and

others meteorologists. A portrayal

method based on the best results

from the study were integrated into our FlowVis2D software, a package written in C++

and OpenGL for rendering currents from ocean

model output or winds from weather model output.

The result is shown in Fig. 6, in this instance illus-

trating surface water currents from the U.S. Navy

Operational Coastal Ocean Model (NCOM). It is also

used to portray forecast guidance from the NOAA/

National Ocean Service’s (NOS) estuarine and Great

Lakes oceanographic forecast modeling systems

and has been available on NOS’ nowCOAST portal

(http://nowcoast.noaa.gov) since 2009.

MULTIVARIATE METEOROLOGICAL

DISPLAY. In meteorological displays, a major chal-

lenge is to simultaneously show scalar variables, such

as atmospheric pressure and surface air temperatures,

together with wind patterns. To meet this challenge,

we took advantage of the perceptual channel theory

outlined in our introduction. The key design idea

is to use different visual channels to show differ-

ent types of information and thereby reduce visual

interference between the layers. We began with the

following mappings:

Temperature  color channel

Atmospheric pressure  texture channel

Wind speed and direction  motion channel

Wind patterns are represented using a pattern of

10,000 animated streamlets. To represent pressure, we

used a series of graduated textures in addition to con-

tours. To represent surface air temperatures we used a

fairly conventional color sequence with different color

bands every 5 degrees. The result is shown in Fig. 7

(without animation). In our evaluation, we measured a subject’s ability to accurately judge temperature,

pressure, and wind speed and direction, comparing

our new solution with a more conventional alterna-

tive (Fig. 8a), a glyph-based alternative (Fig. 8b), and

a nonanimated version of Fig. 7 (Ware and Plumlee

2012). The results showed our animated design to

be perceptually more accurate than the others in

the representation of wind direction and speed. It

was also judged to be greatly superior in terms of

how well specific wind patterns could be seen, such

as weather fronts and the circulation around a low

pressure center.

A BETTER WIND BARB. A common graphical

device for showing wind speed and direction is the

wind barb. Wind barbs were originally designed to represent wind speed and direction at observing

platforms on a surface weather map in a way that

can be directly read by someone familiar with sta-

tion model symbology. However, winds barbs are

not well designed to show patterns of winds such as

those produced by meteorological models or analysis

systems. The perceptual problem with wind barbs is

that only the very tip of the barb is tangential to the

wind direction, and therefore most of the contours

in the glyph are oriented in nontangential directions.

We undertook a study to both design and evaluate

alternatives to the classic wind barb with the goal of

combining the best feature of the wind barb, display-

ing speed in a readable form, with the best feature

of streamlines, showing wind patterns clearly. Two

of our most successful designs are shown in Fig. 9.

Our first solution used the

Jobard and Lefer (1997)

algorithm to generate

equally spaced stream-

lines and placed wind barbs

with curved shafts along

streamlines. This improves

the ability to see patterns,

but the feathers of the barb

still produce significant

visual interference because

they are not tangential

to the flow direction. In

a more radical redesign

(Fig. 9c), arrowheads of

different sizes and styles

are used to replace the barb

bars, showing 5-, 10-, and

50-kt increments (1 kt =

0.51 m s –1 ). This has the ad-

vantage of symmetry about

the streamlines producing

less systemic visual bias to the flow direction. It also

allows for streamlines to be placed somewhat closer

together, allowing for more details to be shown. Our

evaluation showed the new designs to be superior in

their ability to accurately show the wind speed and

direction (Pilar and Ware 2013). We also evaluated

the new designs in their ability to represent wind

patterns. To do this, we artificially created six differ-

ent wind patterns (two are shown in Fig. 10) embed-

ded in a larger-scale smooth flow. Study participants

were required to guess which of the six they were

seeing under various display conditions, including

the four that are illustrated. The results showed errors

reduced by about 70% in comparison with the grid

of wind barbs.

REPRESENTING WAVE PATTERNS. Our

final example is an extension of the alternative wind

barb design. As illustrated in Fig. 11, we developed

a quantitative glyph to show wave height and the

direction of travel as forecast by a NWS wave model.

This encodes information in a manner similar to a

wind barb using symbolic bars and triangles. Wind

information is also shown using our redesign of wind

barbs. Two perceptual methods are used to minimize

the visual interference between wave information

and wind information. Because mariners are often

interested in the angle of wave fronts, as opposed

to the direction of travel, we draw contours that are

tangential to wave fronts, and orthogonal to direction

of travel. This tends to minimize visual interference between wind and wave

patterns because, most of

the time, wave fronts are

roughly orthogonal to wind

direction. Also, perceptual

research shows that graphi-

cal elements that are coun-

terphase to the background

in terms of lightness can be

easily separated (Theeuwes

and Kooi 1994), so we use

black for the wave informa-

tion and white for the wind

information.

CONCLUSIONS AND

RECOMMENDATIONS.

Our experience suggests

that an understanding of

basic perceptual processes

can help in the design of

clear and effective visual-

ization of meteorological

and oceanographic analyses

and forecast model guid-

ance. But perceptual theory

can only motivate promis-

ing approaches; it cannot

be used to specify detailed

design solutions. A kind

of cognitive task analysis

is required for a successful

design. This involves determining the goals of the

visual data representation. What patterns are likely

to be most important for the user? Visualizations are always tradeoffs. If only wind patterns were

important, then a much denser mesh of animated

particle traces could have been used in Fig. 7. If it were only necessary to see atmospheric pressure and not

temperature too, then color might have been used to

represent pressure. The relative salience of different

features must be carefully tuned in the design process

to meet the design goals.

Ideally evaluation will also be part of the design

process. Both formal and informal experiments with

human participants are useful both to set parameter

values for the mapping from data to display and to

compare a new design against existing alternatives.

For most of our studies we took the most basic tasks to

be judgments of wind orientation, direction, or speed.

Additional research is needed to discover the optimal

way of bringing out patterns such as wind shear at a

front, or the branching of the jet stream. However, so

long as tasks can be understood and defined, percep-

tual theory can be applied to the problem.

One of the more difficult problems in designing

effective wind, current, or wave visualizations is

dealing with the scale of the map. A great advantage

of color coding values such as wind speed or surface

temperature is that color tends to scale well. This

is because with a well-chosen color scheme, even if

certain details cannot be seen, their colors will blend

in the visual receptors to something approximating

an average. This is not the case for vector representa-

tions, whether conventional arrows are used or one

of the methods advocated here. There is an optimal element spacing for show-

ing the greatest amount of

detail; if the spacing is too

small, patterns will become

invisible, if too large, detail

will be lost. The representa-

tion must therefore change

with scale.

The development, main-

tenance, and operation of

weather and oceanographic

forecast modeling systems

and their underlying nu-

merical three-dimensional

models is a hugely expen-

sive undertaking but the

cost is justified by the high

value of the data. Some of

the resulting products are

only viewed by specialists,

whereas others are seen by

millions who have a more

casual interest. In either

case the visual portrayal of

the output of these models

deserves substantial effort because this is usually the

only way model results can be interpreted. With a

poor visualization, much of the information may be

lost and a proportionate amount of modeling effort

and operational costs wasted. Designing and evaluat-

ing representations for perceptual efficiency is not a

trivial undertaking, but it is worth the effort.

ACKNOWLEDGMENTS. Funding for this project was

provided by NOAA Grant NA05NOS4001153 and by NSF

ITR Grant 0324899. The authors thank Jason Greenlaw,

Matthew Plumlee, and Roland Arsenault for their help

in improving FlowVis2D software in NOS’ nowCOAST

GIS-based web mapping portal. We also thank Matthew

Plumlee, Peter Mitchell, and Daniel Pineo, who partici-

pated in the research.