Weather on the Web

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Figure 1

Weather map of Europe,10th Dec, 1887



The weather is something that has a continued effect on the way we run our lives, from the

mundane decision of what clothes to pack for a holiday, to the severe conditions that can

destroy livelihoods and threaten lives.

The invention of the electric telegraph in 1885 allowed meteorological information to be

communicated much faster than preceding years (1). Previously, by the time a person had

received a physical copy of the weather using mail, conditions would have undoubtedly

changed; but new technology allowed information to be spread almost instantaneously.

With advancements in technology in current times, we are now able to observe conditions in

real-time and make forecasts all over the Earth by using the internet. This information was

only available for qualified meteorologists, but now every person connected to the network

can observe vast amounts of data. However, information to the masses can result in incorrect

interpretation due to issues of legibility and misunderstanding.

This report looks at a sample of web-based weather services and makes comments upon

visualisation clarity and information design. It looks at the challenges a weather graphic faces

before exploring graphic elements to aid, or hinder, usability and understanding. The

concluding statement gives recommendations on future work in this area, and author's

thoughts on the future of weather visualisation.

As meteorological data became available to scientists in the 1880s and 1890s, there was a

need to standardise graphics in order to convey information clearly (Figure 1). The format of

the early weather diagrams, and the icons they use, can still be understood by meteorologists

today.

In current times, newspapers, television and radio have traditionally been the means of

conveying weather information to the public, but increased internet use has allowed

information to be available at any time, any place, and in richer design formats. However, in its

raw form, weather information is complicated and enigmatic to the general public; only the

meteorologists can decipher it to make any worthwhile predictions (2). Weather diagrams for

the layperson must be redesigned, or re-worded, for understanding, but not at the cost of key

messages and probabilities.

This 'graphic excellence' can be defined using the words of Edward R. Tufte (3), by stating that

weather diagrams for the layperson will be:

Introduction

Oliver Tomlinson - Autumn term 2009 1

Weather on the Web

Meteorological Diagrams: Objectives and Challenges

Presenting interesting data focusing on quality of substance, statistics, and design;

Communicating complex ideas with clarity, precision, and efficiency;

Illustrating the greatest number of ideas in the shortest time;

Very often multivariate;

Comparable to allow interpretation of the whole story;

Accurate and portraying the truth.



Figure 2

Summer Temperature, MetOffice seasonal forecast

2009


http://slidepdf.com/reader/full/weather-on-the-web 5/20Oliver Tomlinson - Autumn term 2009 3

Along with the challenge of mass data communication for mixed needs, one of the greatest

difficulties encountered in weather portrayal is the issue of probability (4). The weather we

experience today can only affect weather 10 to 14 days ahead of us, so any predictions made

past this time period must look at anomalies in the current environmental climate to make

predictions. Therefore, as meteorologists make forecasts the probability of outcomes varies as

time increases.

The following case study illustrates how poor messaging, via language and diagrams, can

affect the public perception on meteorological forecast reliability.

In April 2009 the Met Office gave a presentation on its seasonal weather forecast for the UK

where it promised “odds on for a barbeque summer” (5). This statement received much

criticism from the press in following months as the weather did not meet public expectations.

As an article in the Guardian newspaper states, the weather announcement actually meant

that the summer prediction would be warmer than average, but this still encouraged millions

of 'staycationers' to book summer holidays in the UK (6). Philip Eden (Vice President of the

Royal Meteorological Society) comments in the article, “I think the general public realise there

is a margin of error” . An article in the Independent around the same time also contains

comments from a meteorological professional. Tom Tobler (forecaster at Meteogroup) states,

“if it was a 65 per cent chance it doesn't really tell you a lot as there is a 35 per cent chance that it

could go the other way,” and follows by saying, “part of the problem with long-range forecasts is

communicating with the public the uncertainty,” (7).

Figure 2 is a slide from the presentation; it attempts to illustrate the probability of above

average temperatures in the UK. There are no data values shown so the observer cannot

ascertain by how much the temperature will be above average, or compare with any other

information on past years. The choice of colour and smoothing could indicate a 'heat-wave'

flowing across the middle of the graphic; this feature is likely to be misinterpreted. Another

graphic from the presentation is mentioned in a later section of this paper.

“Barbeque Summer 2009”

Weather on the Web



Meteorological data is collected from all over the World, twice daily, 365 days a year.

Information may be recorded by weather stations, buoys and ships at sea, aeroplanes

(specialist and commercial), satellites, and weather balloons. This mass of data is

programmed into specialist 'super computers' to run predictions for accurate forecasting and

probability analysis. Figure 3 is a collection of diagrams taken from the European Centre for

Medium-Range Weather Forecasts; the diagrams illustrate data collection points (8).

It is interesting to note that Fig 3.3 and 3.4 are actually presenting multivariate data, as

opposed to only data collection sites. Because weather data is transmitted during flights on

aircraft, Fig 3.3 is also displaying flight paths. Fig 3.4 is interesting as it is the only diagram to

be portraying actual weather information in the form of cloud cover.

These diagrams carry a large amount of 'data-ink'; this is the non-erasable core of a graphic.

Correct proportions of data-ink are key to Tufte's principles of graphic excellence (3). To

improve these further it would be necessary to tone-down the grid by grey-scaling so it

becomes less of a design element; Tufte refers to overbearing elements as 'chartjunk'.

Once algorithms have been processed, the raw data is converted into graphics to illustrate

current meteorological conditions and predicted forecasts; these are discussed in the next

section.

The 'barbeque summer' case study illustrates the challenges of weather prediction and

probability; this section shall examine methods of displaying these uncertainties.

Forecasts taken from the raw data are often shown in 'plumes'. Figure 4 shows examples of a

plume taken from the ECMWF seasonal forecast (9), and one provided to the author by Ross

Reynolds of Reading University (4).

Plumes start from a known data point before 50 different data lines are shown to predict

probability of future conditions. These 50 alternatives take into account such elements as

Chaos Theory and weather anomalies; where they follow a similar pattern/line this would

indicate a high probability (4). The plume in Figure 4.1 has a high data-ink percentage but,

due to the closeness of lines, is difficult to take an accurate reading. A lack of key assumes the

reader understands what the diagram is attempting to explain. Figure 4.2, with a key and

colour coding, is easier to understand; however, due to the parallelism of the diagrams the

observer may compare them to each other when they are actually providing visualisations of

very different data sets (10).

Raw Data Collection and Visualisation

Prediction and Probability


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Figure 5

EPS Meteogram providedby ECMWF via Ross

Reynolds

Figure 6

Weather predictions using

Vaisala IceCast software,provided by Amey plc to

the author

Figure 7

Monthly forecast (Reading)

from the Weather Channel

Figure 8

Daily Forecasts: Chiba,

Japan MeteorologicalSociety (English translation)

Figure 9

UK temperature

probabilities, Met Officeseasonal forecast 2009

6



Another method used to illustrate probability is a box plot as shown in Figure 5. It requires a

more detailed analysis by the reader but enables a deeper understanding of probability by

illustrating percentiles and data spreads. Similarly to the diagrams in Figure 4.2, this diagram

has an issue with parallelism causing the reader to compare each chart with the others in the

set, even when they are at different scales on the y-axis. Another issue is the information

'spilling' off the chart causing graphical distortion of the box plot and potential

misinterpretation by the reader.

Figure 6 is an example of a forecasting diagram used by an infrastructure management

company (Amey plc), produced by Vaisala IceCast software. Amey will use this data to make

decisions on road gritting in the UK during cold periods, so user understanding of this

visualisation is vital. Wrong decisions could incur financial losses for Amey if they grit

needlessly, or more importantly, can cost lives of motorists if gritting has not been performed

in the correct location, at the correct time.

Unlike the plumes, the Vaisala diagram does not show multiple possibilities, but just one line

for surface temperature prediction, and one for dew point temperature; the user is expected to

understand how these two factors relate to one another. There are a number of ambiguous

colour codes at the bottom of the diagram which, with no key, are useless. The reader

responsible for gritting is not given any supporting evidence to make their decision, no

comparative historical information, and no probability indication; they must trust the data

used within the visualisation is accurate.

As the raw data moves into the reach of the general public, there is a shift in how probabilities

are illustrated, that is if they are mentioned at all. Graphs are less likely to be seen, they are

replaced by tables as shown in Figure 7 taken from the UK Weather Channel website (11);

here a percentage chance of precipitation is given in text format within a cell.

Tufte mentions that Japanese graphical distinction is consistent with its heavy use of statistical

techniques in the workplace and extensive training (3); however, meteorological forecasts

found on Japanese websites found a similar, tabular expression of probability as can be seen in

Figure 8 from the Japan Meteorological Society website (12). This diagram is an improvement

on the Weather Channel table as it provides the reader with a scale of probability alongside a

level of precipitation (units are however missing but this may be due to the English translation

of the site).

Returning to the presentation by the Met Office on UK summer forecast 2009 (5), it is possible

to understand why the public may have misinterpreted the information by looking at the slide

shown in Figure 9. The reader is drawn to the large orange column stating 50% above

average, and may not read the accompanying text or even the details of the temperature on the

x-axis. It would be possible to read this graph and presume the summer temperature could be

50% above the average where, in fact, the graph states there is a 50% chance of above average

temperatures. The diagram does not show this above average temperature could only be anincrease of 0.1°C.


Weather on the Web



As seen in Figure 1, symbols have been used in weather graphics since the late 1880s. They

follow a standard design format that enables the reader to understand graphics produced in

different locations, by different people, and make comparisons. Due to the varied nature of

weather conditions, there are large numbers of pictograms to represent them; this section

observes some of the variety found on the internet. But first, it is worth noting a few points

on information memory and icon types.

Where a large amount of information is given to the reader to remember, it is useful to apply

the theory of Miller's Magic Number. Miller's research found the human brain can remember

seven 'chunks' of information, plus or minus two, within the short term memory (13). By

chunking information together it is possible to remember larger amounts of content. In the

example below Mnemonic devices are used to remember the colours of the visible light

spectrum. Letters representing a colour are chunked in order to represent a name to aid

memory (14).

Yvonne Rogers classifies icons by arranging them in four groups (15):

Figure 10 is a key found on the Met Office website (16); it shows a vast number of pictograms

for a large number of weather conditions and data sets. Memory of the weather condition

symbols is aided by designing them in a 'resemblance' format, i.e. each one (excluding fog and

haze) represents the weather it portrays, with ambiguous elements such as dust and mist

accompanied with descriptive type. An attempt to chunk the symbols has been made, but

lack of spacing does not help the reader to separate similar icons. Wind, temperature, solar

UV, and pressure chart symbols are mostly 'arbitrary' design, requiring the reader to learn

their meaning. Warm and cold fronts do give visual cues by colour and symbolism of suns

and icicles.

Weather Icons and Pictograms

Icon Memory

RO Y G BI V

Icon Classification

Resemblance: Direct portrayal of an object;

Exemplar: An example of the classification of objects they refer to, e.g. knife and fork toportray a restaurant;

Symbolic: A high level of abstraction than the actual object depicted, e.g. broken glass to

symbolise a fragile package;

Arbitrary: No relation to an object or concept (these icons must be learned), e.g. no

entry symbol.


Weather on the Web



Figure 11 is a collection of weather icons, found online, from different sources. All weather

condition icons are of the resemblance type (excluding wind which often appears as a

simplified version of the standard meteorological symbol seen in raw data) and are easily

comparable. Differences occur in the placement and scale, especially of the Japanese forecasts.

Forecasts of large areas often place icons on a geographical map, whereas more specific city

forecasts will place the icon within a table accompanied with more detailed text elements. The

Japanese graphics are the only ones within the sample to scale down the icons, and to use

colour coded arbitrary circles to represent rain. This style is verging on a graphical texture

effect using the data locations; it adds a 'smoothness' to the visual allowing the observer to

'see' weather patterns. Patterns and textures are discussed in the next section.

With advances in computer rendering, icons on the internet are becoming increasingly

animated, allowing almost real-time changes on screen. Figure 12 is a screenshot taken of the

author's 'Avatar' in Secondlife. The image is an observation of the National Oceanic and

Atmospheric Administration's live national weather centre; weather is updated in 8 minute

intervals and can be observed from any angle, allowing complete interaction by the observer's

character.

Meteorological information is often related to geographic locations, and this is the reason that

so much of the data is placed on maps. Geller describes four maps used in weather

presentation (2):

Tufte describes these graphics as 'data maps' – applying data to a geographical map (10). The

difficulty when creating meteorological data maps is that multivariate data does not just sit on

a level, two-dimensional plain; instead, the data may represent three-dimensions or more if

time factor is included.

Figure 13 is a chart designed by Edmond Halley in 1686 (18). This early example of trade

winds and monsoons illustrates how a pattern (or texture) of pen strokes can give direction byalignment, and density by closeness of strokes. Recent research into human perception by

Ware and Knight (1995) has shown three fundamental visual dimensions of texture:

Orientation, Size and Contrast (19).

Maps, Patterns and Textures

Contour maps: Show fronts of equal pressure, temperature, precipitation or moisture,

wind, or other factors. They are similar to topographic maps in their use of lines to

portray information;

Point-centered maps: Present data in a graphical format without showing

relationships, e.g. television forecast maps;

Photographic maps: Taken from satellites or from the ground, these typically showeither visible or infrared spectrums;

Radar maps: Often low resolution, these illustrate measurements taken over a large

area from a central point. They have similarities with Photographic (based on reflected

waves), Point-centred (taken from a central instrument), and Contoured (resulting

images show weather fronts), as shown in Figure 3.4.


Weather on the Web



As meteorological information is passed from the scientists to the general public on the

internet, there seems to be a loss in data integrity, and especially the portrayal of probability.

This may be the result of 'dumbing down' information for the masses due to the presumption

of potential misinterpretation; or caused by graphic designers with little understanding of the

raw data, who may be only interested in stylistic elements. To improve this situation, research

would need to be performed to ascertain public understanding of meteorological data

visualisation; outcomes from this investigation would surely benefit other areas requiring

knowledge of complex data analysis such as the medical sector and climate change authorities.

An understanding of user requirements is needed before visualisations are designed. The

designer should identify if the user requires a broad outlook of weather conditions (e.g. by

country) and produce work with subtle colour changes and low levels of chartjunk. If the user

requires a more specific analysis of a weather element, designs should aid them in locating

points of interest (maybe increasing the difference threshold) and observing trends by making

comparisons using high levels of data-ink.

As advanced methods develop for collecting and analysing raw meteorological data,

visualisations for making this information clear to the public will also need to be enhanced;

giving the user a rich, real-time interactive experience, and a complete understanding of the

varied probabilities involved in predicting the weather. If achieved, public perception and

trust in weather forecasts will improve and faith will be restored from disastrous media

attention on meteorological misunderstandings.

Conclusions


Weather on the Web



References

1) Weather Forecasting. URL: http://en.wikipedia.org/wiki/Weather_forecasting [7th Dec, 2009. 15:48]

2) Geller, T. (2007). Envisioning the Wind: Meteorology Graphics at Weather Underground. Computer Graphics and Applications, IEEE. 27, Issue 5, 92-97

3) Tufte, E.R. (2007). The Visual Display of Quantitative Information. 2nd ed. Cheshire, Connecticut: Graphics Press

4) Discussions between the author and Ross Reynolds, Senior Teaching Fellow, MSc Programme Director & Admissions Tutor.School of Mathematics, Meteorology and Physics, Reading University. 1st Dec, 2009

5) Met Office Summer Forecast 2009. URL:http://www.metoffice.gov.uk/corporate/pressoffice/2009/pr20090430.html [3rd Dec, 2009]

6) Topping, A. (2009) Rain puts dampers on 'barbeque summer' . URL:http://www.guardian.co.uk/uk/2009/jul/29/summer-weather-forecast-rain-holiday [3rd Dec, 2009. 13:25]

7) Fentiman, P (2009) Why good weather is hard to predict . URL:http://www.independent.co.uk/environment/climate-change/why-good-weather-is-hard-to-predict

1764208.html [3rd Dec, 2009. 13:31]

8) ECMWF: Geographical coverage. URL:http://www.ecmwf.int/products/forecasts/d/charts/monitoring/coverage/dcover/ [3rd Dec, 2009. 10:34]

9) ECMWF: Seasonal Range Forecast . URL:

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/ [3rd Dec,2009. 10:35]

10) Tufte, E.R. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, Connecticut:Graphics Press

11) Monthly forecast for Reading, from the Weather Channel. URL: http://uk.weather.com/weather/monthlyUKXX0117?cm_ven=yahoo_uk&cm_cat=citypage&cm_ite=weather&cm_pla=monthly [6th Dec, 2009. 17:14]

12) Daily forecasts: Chiba. URL: http://www.jma.go.jp/en/yoho/318.html [5th Dec, 2009. 9:29]

13) Miller, G.A. (1956). The Magical Number Seven, plus or minus two: some limits in our capacity for processinginformation, The Psychological Review , Vol 63, 81-97 (cited in (Visocky O'Grady, J. and Visocky O'Grady, K.(2008). The Information Design Handbook. Switzerland: Rotovision)

14) Visocky O'Grady, J. and Visocky O'Grady, K. (2008). The Information Design Handbook. Switzerland: Rotovision

15) Rogers, Y. (1989). Icons at the interface: their usefulness, Interacting with Computers, 1 105-117 (cited in:Malamed, C. (2009). Visual Language for Designers. Massachusetts: Rockport

16) UK: Forecast Weather. URL: http://www.metoffice.gov.uk/weather/uk/uk_forecast_weather.html [3rd Dec, 2009.10:56]

17) Weber, A. (2006). 3D weather data visualization in Second Life. URL:http://www.secondlifeinsider.com/2006/10/28/3d-weather-data-visualization-in-second-life/ [3rd Dec, 2006.23:16]

18) Halley, E. (1686). An Historical Account of the Trade Winds, and Monsoons, Observable in the Seas Between andNear the Tropicks; With an Attempt to Assign the Physical Cause of Said Winds, Philosophical Transactions. 183,

153-168 (cited in Tufte, E.R. (2007))

19) Ware, C. and Knight, W. (1995). Using Visual Texture for Information Display, ACM Transactions on Graphics,Vol.14, No. 1, 3-20

20) Ying Tang; Huamin Qu; Yingcai Wu; Hong Zhou. (2006). Natural Textures for Weather Data Visualization. IV 2006,

Tenth International Conference on 5-7th July 2006, 741-750

21) Weather Underground. URL: http://www.wunderground.com/wundermap/?lat=34.01485825&lon=118.49143219&zoom=10&pin=Santa%20Monica,%20CA [7th Dec, 2009. 23:35]

22) BBC Weather. URL: http://news.bbc.co.uk/weather/ [1st Dec, 2009. 13:45]

23) The Weather Channel (USA). URL:http://www.weather.com/outlook/travel/businesstraveler/local/UKXX0117?from=enhsearch_drilldown [3rdDec, 2009. 13:43]


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