COLLABORATIVE OBSERVATIONS OF WEATHER

A Weather Information Sharers’ Community of Practice

Katarina Elevant

Department of Media Technology and Graphic Arts

Royal Institute of Technology KTH, Lindstedtsv. 3, Stockholm, Sweden

Keywords: Weather, Forecast, Co-creation, Observation, Climate change, Community, Collective intelligence.

Abstract: Beside occasional disastrous impacts of weather, weather also affects daily life. Societal and environmental

challenges of the future include both providing customized weather information in-time due to users’ needs,

and detecting climate change and its impacts on land and ecosystems. The accuracy of weather and climatic

information is, however, limited by spatial and temporal borders that need to be overriden. Also, weather

information services cannot be fully customized, a problem arising from the spatial inaccuracy of weather

forecasts and observations. Here, the role of social media, collective and civic intelligence and crowd

sourcing should be investigated. This paper envisions a community of weather-interested users that provide

usable observations of weather and environmental change, and presents a web-based interface for this

community as a new method to collect weather and climatic information. User-generated weather

observations can be processed based on principles of collective intelligence and co-creation, in order to

improve, customize and personolize weather information.

1 INTRODUCTION

In the 17

century, two centuries before the

invention of the telegraph, Robert Plot, Secretary to

the Royal Society in England, collected weather

observations and noted that if the same observations

were made “in many foreign and remote parts at the

same time” we would “probably in time thereby

learn to be forewarn certainly of diverse

emergencies (such as heats, colds, dearth’s, plagues,

and other epidemical distempers)” (Konvicka,

1999).

Imagine Plot’s expectations on the 21st century’s

social media. Weather can be observed by anybody,

representing visible and perceivable expressions of

complex processes in the atmosphere. Ancient

cultures learned to understand signs of incoming

weather and its impacts on the environment

(Theophrastus). Fishermen and farmers, that possess

experience of the law of physics as eye-witnessing

governed movements of the air, are able to make

good observations of weather (Ångström, 1926).

For centuries, the development of meteorology

relied on human observers, still contributing to the

international exchange of observations from

meteorological synoptic stations (from Greek

synoptikos “to see together”), organized by the

World Meteorological Organization (WMO). One

basic problem for weather and environmental

forecasting is, however, related to the limited spatial

resolution of weather observation networks.

Introducing social networks, this paper is based

on the assumption that, whether an individual or an

organization of individuals, everybody may perceive

weather. Everybody can see, or observe, the weather

in their closest environment. As a parallel to Jenkins

(2006) “No one knows everything, everybody knows

something”, it can be stated that: No one can observe

weather everywhere, but everybody can observe

somewhere, or some of the weather. Thereby, a large

number of users could see the weather together, and

the essence of synoptikos (“to see together”)

suddenly reaches new proportions, as tools for

collective intelligence of web 2.0 are accessible.

1.1 Reasons to Talk about Weather

The development of weather services through

history has been connected to: (1) inventions of new

communication technologies, and (2) incitements to

save lives and property. Some early attempts to

organize weather observation networks were

392

Elevant K.

COLLABORATIVE OBSERVATIONS OF WEATHER - A Weather Information Sharersâ

Z Community of Practice.

DOI: 10.5220/0002810203920399

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

initiated after documented losses caused by severe

weather (Burton and Fitzroy, 1986; Craft, 1998;

Craft, 1999; Davis, 1984; Moran and Hopkins,

2002). The usability of weather observations was,

however dependent on long-distance communication

overriding spatial distances larger than the size of

weather systems ranging over 100 km, as the

character of atmospheric motions is highly

dependent on horizontal movements (Holton, 1992).

Climate change as well as the fact that societal

changes and urbanisation increases the vulnerability

arising from weather (Changnon et al., 2000), put

weather information on the agenda. In 2005, the

hurricane Katrina, the most expensive disaster in

U.S. history, stated an example of the disastrous

effects of weather with $130 billion damage/costs

(NOAA, 2009). Through human history, climatic

disasters have affected communities and populations

around the world, such as the mysterious demise of

Viking settlements in Greenland in the 14th and

15th, believed to have occurred due to a temperature

decrease (Konvicka, 1999). The future society will

also have to tackle the increased frequency of severe

weather events (Parry, 2007).

New media technologies of the 21

century offer

possibilities to override spatial distances between

two people anywhere in the world. New ways of

communicating thus open windows toward both

distributing, and collecting, new weather

information. The objective of this paper is to look

into the opportunities offered by the “invention” of

web 2.0.

1.2 Motivation to Purchase Weather

Information

A fisherman’s motivation to observe weather is due

to the impact of weather on most “events” in the

fisherman’s daily life (Ångström, 1926) and comes

with concern about own property and life. Individual

motivation must thus be searched for within the

personal life, such as economic incitements,

improved quality of life, individual freedom,

planning and mobility. Studying customization of

weather services, Elevant (2009) concluded that

interest in weather had four different origins: leisure

time activities (such as outdoor hobbies), travel to

work, interest in technology, and genuine interest in

weather. Easy access to weather information through

different traditional and linear media channels

mostly offer passive consumption of weather

information. The information is thereafter filtered

due to personal relevance (Schneider and Laurion,

1993). Thus a challenge arises for not only private

enterprises aiming at creating attractive weather

services, but decision-makers that want to inform the

broad public about coming weather events.

1.3 Limitations in Current Services

Compared to traditional linear media weather

services, created to suit the majority of a target

group, web 1.0 do offer some level of customization

(e.g. city, region, hobby). Tools like GPS and digital

maps can zoom applications down to geographical

distances of meters. The service content is, however,

based on weather observations and forecasts

operating on spatial scales of kilometers (WMO,

2006).

Climatic information is based on an even coarser

spatial resolution of hundreds of kilometers.

Detection, as well as understanding of complex

processes related to climate change, point at the need

to increase both data volumes and quality.

Incomplete data sets restrict understanding of

changes in extremes and attribution of changes to

causes (Solomon et al., 2007). Most fingerprint work

has focused on global-scale changes in “primary”

climate variables, which underlines the importance

of developing methods to detect the effects of

greenhouse gases on climate and the environment.

Similar relations exist between the resolution of the

weather forecast and variables describing

consequences of weather such as: road conditions,

power plant efficiency, soil moist, crop growth.

This gap between the spatial scales requested by

meteorological applications, and the spatial

resolution of available weather information upon

which we base the content of weather information

services, illustrates the problem of customizing the

content of a weather service to a particular user’s

geographical position, and activity. As a result,

consumers acquire weather forecasts not entirely

relevant in regard to their needs.

Studying customized traffic weather alerts,

Elevant (2009) suggested that personalized weather

information can be based on user profiles and

information on perception, position, habits and

recent weather experiences, indirectly or directly

provided by the user while observing weather (e.g.

by ranking received weather forecasts). The level of

personalization will depend on the amount of

information provided by the user.

COLLABORATIVE OBSERVATIONS OF WEATHER - A Weather Information Sharers' Community of Practice

393

1.4 Objective: The “Share Weather”

Community

We can conclude that current weather services

struggle with problems regarding spatial inaccuracy.

Secondly, users should be more actively engaged, if

we ought to increase active acquisition of weather

information, which may be of particular interest

before severe weather events. The objective of the

paper is to demonstrate a web service based on co-

creation, discuss motivations to use the service as

well as some wider implications.

Web 2.0 has not only opened windows toward

customization of weather services, but offers the

opportunity to co-create weather information.

Almost everybody owns a cellular phone. Sensor

networks, such as road observation networks

measuring road conditions, are on progress and they

are used to improve the quality of local weather

information. However, the possibilities to collect

weather information from a large number of

individuals, now offered by web 2.0, are still

unexplored.

The paper suggests a community of interest,

which offers important practice as creating

information valuable not only to the individual and

the community of interest, but to the whole society.

2 SHARED WEATHER DATA

Organized observation networks provided the first

systematic records by 1860. Climatologists

additionally use proxy palaeoclimatological sources

of information, derived from tree rings, ice cores,

coral growth, or features like ship logbook data.

Table 1 illustrates the development of weather

information networks, from reports provided

through the first telegraphic networks in the 19

century, to currently 10

observations: 15 satellites,

700 buoys, 3 000 aircraft, 7 300 ships and some 10

000 land-based stations (WMO, 2006).

Table 1: Weather information paradigms.

TECHNOLOGY OBSERVATION

POINTS

Human speech

1850 Telegraph

- 10

1940 Aviation

1950 Computer

- 10

1970 Satellites

- 10

1990 Web 1.0

- 10

2010 Web 2.0

– 10

Connecting hundred millions of people in

different places through web 2.0, offers a potential

solution for synoptic meteorology and the idea of

synoptikos “to see together”, as a large number of

citizens may share their weather observations with

each other, and see together. Hereby, we introduce

the idea of a web weather 2.0 paradigm.

2.1 Predictions and Observations

Weather forecasting is an initial-state problem

represented by a set of non-linear differential

equations (Holton, 2992). The initial state is

achieved from synoptic stations, which are boundary

condition input to numerical weather forecast

models (NWP). Due to complexity of the system,

simplifications are necessary. Here arise the two

major problems within weather forecasting. The first

is the simplification of the basic equations used to

calculate future states of the atmosphere. The second

is due to lack of observations of the current state.

Climate models, used to simulate global

environmental processes and trends meet even

greater challenges, while aiming at modeling three

different sets of processes: radiative, dynamic and

surface process (Peng et al., 2002; Oliver, 2005),

and are assembled by coupling general circulation

models of the atmosphere and oceans to land surface

and cryospheric models. Climate models use

parameterizations derived from large-scale

observations or extensive field investigations.

However, there are continuing problems with

sustaining adequate spatial sampling of climate

conditions (Martinson et al., 1998).

Summarizing, the number of observations from

around the world is inadequate to achieve the high-

resolution local information in order to provide

customized and personalized weather information,

as well as reliable detection of climate change and

climate projections. The models use a smaller grid

size than what is available with observations,

requiring interpolations due to missing data points.

2.2 Observation Biases and Limitations

Current weather observations are exposed to biases

due to: (1) human perception (e.g. Kent and Berry,

2004), (2) methods for measuring based on

“surrogate” variables (e.g., spectral radiance, radar

reflectivity, turbulence used to measure cloudiness,

precipitation, wind profiles and visibility, as

described by Park and Xu (1999), and the problem

with rain gauges studied by Robinson et al. (2004)),

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(3) physical environmental preconditions

characterizing the spot (e.g. topography, vegetation).

In meteorological applications additional biases

are created while: (4) performing necessary spatial

extrapolations non-representative of extreme values

and meso-scale phenomena (such as thunderstorms

or road surface microclimate conditions (Wallman el

al., 2005)), and (5) introducing different standards

(e.g. measuring wind speed at different heights).

For some applications, technological progress

like the introduction of satellites significantly

improved the data, such as increased data volumes

for monitoring of aridity and environmental change

(e.g. Svoboda et el., 2002), addressing issue (4). In

order to supply different applications for industry

and consumers with adequate input, additional

weather observations are operated by companies and

organizations conducting weather-sensitive

operations (e.g. wind power enterprises, road

administrators, sports), however creating

observation sets of different standards (issue (5)).

Despite advanced space technology, applications

like modeling impacts of climate change on

ecosystems and land provides uncertainties due to

extrapolations and parameterizations (issue (4)), and

methods (issues (2) and (3)). For example, when

assessing land degradation, experts tend to

underestimate “the abilities of local farmers, many

of whom have been able to modify their land

management.” (Stroosnijder, 2007). Again, the need

to study local extremes in order to improve NWP’s,

as well as document serious effects of climate

change - the urgent need to detect how the weather

is changing on long-term - is clearly expressed by

issues (2), (3) and (4).

2.3 Creating Additional Weather Data

Statistical approaches have to be introduced if

looking beyond the limitations set up by the

complexity of the reality. Statistically “correcting”

outputs of NWP’s, so called “nowcasing”, is a part

of daily operations in many weather service centers,

providers and businesses (e.g. road transportation,

wind power). The weather forecasting of the future

may attribute a large number of data, if we can find

ways to motivate sharing, and establish methods of

processing, and standards. The objective is to focus

on user-generated “shared weather” information, and

motivation of citizens to contribute with local

information forming human observation networks in

cyberspace.

2.3.1 Co-creation of Weather Information

We are aiming at answering the question whether

the weather-men may be replaced by the weather

community. Collective intelligence (Jenkins, 2006;

Levy, 1997), is redefining our traditional

assumptions about expertise, encouraging changes in

the knowledge hegemony of a number of fields

(Walsh, 1999). A delicate example from geosciences

was illustrated by a story of a gold-mining firm that

shared knowledge on geological information with

the world (Tapscott and Williams, 2006),

demonstrating how useful information about the

environment can be achieved from a variety of

information sources, even within an area

traditionally held by specialists and experts. In the

early beginning of weather forecasting, all

observations were collected from individuals that

served as experts in their role as weather observers

(Table 1). The “share weather” system presented

here will collect information from non-experts, at

low cost, with the purpose of “nowcasting” the web

service the users can directly benefit from.

2.3.2 Co-creation of Climatic Information

Based on a denser observation network on regional

level, world-wide, weather information sharers can

perform adequate spatial sampling of climate

conditions, flora and fauna. Such voluntary

observations may serve as “field investigations”,

extending the empirical data set necessary to create

environmental model parameterizations. The shared

weather data may also be processed together with

other climatic data, in order to be used as boundary

conditions to environmental models. Shifts in storm

tracks and intensification in the evaporation and

precipitation cycles due to climate change would

alter the frequency and intensity of floods and

droughts (Milly et al., 2002), which can be recorded

by human weather observers in cyberspace

providing more frequent local observations of wet

(or dry) soil and flooding (or droughts). These high-

resolution records of the environment can be

collected by “weather information sharers”.

Observations made by individuals can be useful,

even necessary, in order to address climate change

issues.

2.4 The “Shared Weather” Bias

Because different people perceive weather

differently, each user will provide an observation

error, a combination of randomness and a systematic

COLLABORATIVE OBSERVATIONS OF WEATHER - A Weather Information Sharers' Community of Practice

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error. From the example of Wikipedia (e.g. Jenkins),

it is, however, evident that documentation on

objective information can be created from a large

number of individual contributions by the process of

peer-viewing. Additionally, we can learn, even

quantify, human biases by keeping records of users’

own observations and habits (Elevant, 2009).

Furthermore, individual biases may be measured by

comparing human observations to the closest source

of more reliable data (e.g. WMO). Mobile weather

spotters guarantee some observation overlapping,

enabling comparison between different observations.

The key argument is though, that within the “shared

weather community”, enough data quantities can be

collected to erase individual biases, and quantify

user biases in order to make systematic corrections

of incoming observations. Additionally, human

senses and simple instruments can be combined,

using low-cost instruments.

Even with a small number of observers, peer-

viewing reduces the human bias, addressing issue

(1) in section 2.2. The web 2.0 solution is of

particular interest when regarding variables difficult

to measure by instruments (e.g. cloudiness). Asking

users to confirm or reject cloud pictures, peer-

viewing may address present limitations due to

measurement instruments (2). The mobility of

weather spotters addresses limitations caused by

physical environmental conditions for spatially fixed

synoptic stations (3). The information possess a

strong user perspective overriding the problem of

different standards (5), and while defined by

different individual’s position, activities and

perception, it is more easily customized to users with

similar profiles. Most importantly, meso-scale

phenomena (e.g. thunderstorms) (4), not easily

detected by WMO stations and predicted by NWP,

are detectable by human mobile observers.

3 THE “SHARE WEATHER”

COMMUNITY

Inspired by the example from the contest on

geological data (Tapscott and Williams, 2006), it can

be argued that the users of “share weather” should

be offered compensation for their efforts. Here

suggested is that, for every volunteer contribution, a

new weather forecast is generated. Earlier was

concluded that motivation is related to personal life

and interests. Thus it can be suggested that co-

creation of weather information can be performed

within a community of interest gathering people

with interest in weather. It is evident from section

1.2 that the best observers are those already

interested in weather, further supporting the

argument that a community of interest can be

established on these grounds.

3.1 The “Shareweather” Interface

In order to motivate participation, members of the

“share weather community” should be able to make

weather reports in different formats using different

devises, depending on present needs and abilities.

Weather reports can be created for chosen places

(e.g. chosen on a map or using positioning systems),

either instantly or several hours or days after

observing.

Weather variables can be measured either

subjectively, or using instruments, which, although

not objective in the sense that they are not

standardized, we call objective. As illustrated in

Picture 1, subjective measurements are for example

picking a suitable text from a drop-down menu,

describing the type of clouds, change of cloudiness

during the latest hours, the part of the sky to which

the clouds are concentrated, wind direction and

estimated speed, temperature change compared to

yesterday’s, visibility, precipitation, precipitation

intensity, slipperiness on road (ice, hoarfrost, black

ice). Cloud categories are chosen by clicking on a

suitable picture resembling the clouds observed.

Additional traffic-related subjectively measured

variables are for example traffic congestion and

traffic flow. Variables that can be measured

objectively are: wind, humidity, temperature,

precipitation amount, travel time, snow depth.

Observations of the environment are represented

by subjective reports on the status of the soil and

ground, water levels, rivers and run-off.

Additionally, observations of the environment such

as seasonal changes in the surrounding habitat and

nature are reported: spring blooming, peak blooming

ranges and calendars, amount of particular flowers

and other plants, as well as cultivated vegetables and

fruits in the garden. The “share weather” portal may

also receive reports on observed species such as

animals and insects.

One innovation integrated into the system, are

pictures taken with a cellular phone (objective

reports), representing an easy way of reporting and

probably added value in terms of entertainment. An

important rewarding mechanism is a local weather

forecast provided to the user whenever pushing the

“send report” button. The system is based on the

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Figure 1: The “share weather” interface.

principle that the more information the user will

send, the more – and more accurate – weather

information will the user receive. Other

functionalities motivating membership in the “share

weather” community are logbooks, calendars, and

personalized books, discussion forums and

possibility to share and see reports performed by

others, or applications created from those data.

3.2 Community of Interest

Earlier, illustrations of empirically accumulated

knowledge on weather and environmental processes

between the air, land and water, conserved by local

habitants that are personally affected by weather,

were provided (Stroosnijder, 2007 and Ångström,

1926). Other evidence support that the best weather

observers (among travellers) are those who need

weather information the most and that an initial need

for accurate weather forecasts in daily life also

encouraged sharing weather information (Elevant,

2009).

Fishermen, farmers and long-distance drivers –

whose life and property are exposed to nature and its

elements - are examples of motivated weather

observers that could join and benefit from the “share

weather” community of interest. As not only the

motivation, but also the ability, to observe weather is

due to training and awareness of weather, it can be

suggested that other individuals can be “trained” in

the same way as the farmer and the fisherman, if

their attention was directed toward weather

phenomena, possibly encouraged by participation in

the “share weather” community.

However, beside the high-quality spatial

information and personalized services, the “share

weather” system may also provide motives such as

contributing to the environment.

3.3 Environmental Practice of the

“Shareweather” Community

Environmental politics and practice meet challenges

like conflicts between environmental interests and

interest of individuals, often regarded the roots of

unsustainable development (Connelly and Smith,

2003). Despite motivation to act on

climate change,

many consider that they do not have information on

what action they

can take to mitigate climate change

(e.g. Lowe et al., 2006).

Studies of so called “trust networks” (e.g.

Cheshire and Cook, 2004), show that the social

context and community responsibility norms can

play an important role in trust-building. Studying

what motivates wikipedians, (Nov, 2007) reached

similar conclusions on ideological incitements, and

not the least the importance of experience of fun.

Most important, ideological incitements and

willingness to participate for “the common good”

were discovered.

Thus, assuming that attractive interface and

functionality are present, and based upon the expert

paradigm and the fact that weather can be observed

by anybody anywhere, it is justifiable to assume that

the “share weather” community of co-creating

weather-enthusiasts, can grow and become a

community of practice collecting important

information on weather, environment and climate

change.

4 CONCLUSIONS

From small weather observation networks enabled

by the invention of the telegraph, we are now about

to face a 21

century web weather 2.0 paradigm. A

“share weather” system based on co-creation,

collective intelligence and peer-viewing of users’

own weather observations can be a community of

practice offering high-resolution short-term weather

COLLABORATIVE OBSERVATIONS OF WEATHER - A Weather Information Sharers' Community of Practice

397

forecasts, and contributing to detecting climate

change.

The 20

century development of meteorological

services led to sophisticated tools and methods for

more accurate weather predictions (e.g. NWP). By

contrast, the accuracy of measuring instruments has

not changed and the number of weather observation

stations is sparse compared to the resolution of

available models. Future NWP’s may integrate data

from a number of sources, including the “share

weather” system. Historical steps like introduction

of satellite data as input to NWP’s illustrate the

immense potential of sensor networks. This paper

suggests that meteorological data also may include

human networks in cyberspace based on social

media.

Local weather phenomena, in particular special

requirements by different applications and

customization and personalization of weather

services for media consumers, are beyond reach of

current weather observations and sensor networks.

The ability of individuals to observe, understand,

adapt to, even modify, their environment and

habitat, is an unexplored societal resource and

source of knowledge that can be shared. If

systematically collected, user-generated weather

information can be processed and integrated into a

share weather system as presented here, offering a

high-quality web service and attractive services to

the members of the “shareweather” community – a

community of interest in weather information. Most

important, the shared weather information can

contribute to significant progress within weather

“nowcasting” raising the quality of weather

information services and applications. Additionally,

the share weather web service would generate values

in its users’ daily life, and practices valuable to the

community as collecting information on climate and

environmental change.

Local observations of the “shareweather”

community can detect local phenomena and

extremes, addressing the current problems with lack

of spatial data coverage necessary to detect,

understand and model the effects of climate change.

Time-demanding processes of collecting and

verifying weather and climate data (e.g. IPCC

synthesis reports), may be shortened by using web

2.0 tools to collect a large number of local

observations, further analysed by experts. On the

other hand, early weather warning services may be

improved, as for such traditional public sector

services “information typically travels serially and

sequentially, from one processing unit to the next”

(Horan and Schooley, 2007), while social media

networks possess the flexibility to collect and

distribute information fast, and additionally are

trustworthy improving the odds that citizens will

adopt to severe weather.

The rise of social networks provides not only an

option for storage of expert opinions and distribution

of knowledge, but enables co-creation of weather

information by everybody, as: nobody can observe

everything, but everybody can observe some(thing)

of the weather.

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