Uncertainty Estimation and Visualization of Wind in Weather Forecasts

ard Fjukstad

1,2

, John Markus Bjørndalen

and Otto Anshus

Department of computer science, University of Tromsø, Tromsø, Norway

Norwegian Meteorological Institute, Tromsø, Tromsø, Norway

Keywords:

Numerical Models, Weather Forecasting, Distributed Computation, Scientiﬁc Visualization.

Abstract:

The Collaborative Symbiotic Weather Forecasting system, CSWF, let individual users do on-demand small-

region, short-term, and very high-resolution forecasts. When the regions have some overlap, a symbiotic

forecast can be produced based on the individual forecasts from each region. Small differences in where the

center of the region is located when there is complex terrain in the region, leads to signiﬁcant differences in

the forecasted values of wind speed and direction. These differences reﬂect the uncertainty of the numerical

model. This paper describes two different ways of presenting these differences using a traditional map based

approach on a laptop and a display wall, and an augmented reality approach on a tablet. The approaches have

their distinct advantages and disadvantages depending on the actual use and requirements of the user.

1 INTRODUCTION

Visualization of uncertainty in weather forecasts are

often done using a colored range around a mean value

on a curve, or a mean value with some number repre-

senting the expected variation around this value. This

works well for single value parameters, like temper-

ature or snow depth. For parameters represented by

two values like wind where both speed and wind di-

rection has uncertainties, avoiding clutter in the visu-

alization may limit the methods used.

This paper studies using two different ways of

visualizing uncertainty of wind forecasts computed

by the Collaborative Symbiotic Weather Forecasts

(CSWF) system on a 2D map. The CSWF system use

a professional fully featured and widely used numer-

ical atmospheric model, the WRF (Michalakes et al.,

2002), set up to produce a forecast for a small area,

e.g., a few kilometers across, and for a short time pe-

riod, e.g., 6 hours. Previous work has documented

that when using background meteorological data from

national weather services, a single CSWF forecast can

be computed on-demand in a few minutes on a typi-

cal multi-core 2012 model year PC (Fjukstad et al.,

2013). Compared to pre-computed forecasts from

weather services, the CSWF system gives a user ac-

cess to all result parameters of a forecast. Conse-

quently, a user can do highly customized visualiza-

tions.

While a user always can produce a forecast lo-

cally, the CSWF system can also collect forecasts

from other CSWF systems. The forecasts are amalga-

mated into a symbiotic forecast with the uncertainty

estimates. The CSWF system amalgamates differ-

ent users’ forecasts when they are for areas with only

slightly different geographical centers. The small dif-

ference in center locations can result in signiﬁcant dif-

ferences in the model representation of the topogra-

phy. This can result in signiﬁcant differences in the

forecasts. The differences can be seen as representing

the uncertainty of a single forecast. This uncertainty

is most typically largest in areas with complex terrain,

like the area around the city of Tromsø, Norway, with

fjords and steep mountains.

Uncertainty in meteorological data has been ex-

plored in many ways. The ensemble prediction sys-

tem, EPS (Molteni et al., 1996), and probability fore-

casting have made several types of visualization part

of the standard toolkit for weather services. One ex-

ample is the spaghetti diagrams

where one isoline in

a contoured map is shown for several EPS forecasts at

the same time.

This paper reports on a few forecast visualization

approaches for wind forecasts and their suitability on

computers ranging from a tablet, a PC, to a large high-

resolution tiled display wall. The wind forecasts are

of small areas, for a short period of time, and where

the user is situated within the forecasted area.

http://tinyurl.com/pj6owmx

321

Fjukstad B., Bjørndalen J. and Anshus O..

Uncertainty Estimation and Visualization of Wind in Weather Forecasts.

DOI: 10.5220/0004660603210328

In Proceedings of the 5th International Conference on Information Visualization Theory and Applications (IVAPP-2014), pages 321-328

ISBN: 978-989-758-005-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

The ﬁrst approach for visualizing uncertainty is

similar to a wind rose, alternatively using individual

glyphs for what can be labeled as wind chaos. Wind

is described by at least two dimensions, direction and

speed, and both are be visualized at the same time on a

map. The visualization is complicated because the in-

dividual forecasts done by the CSWF systems usually

have slightly different grid placements, and therefore

each wind forecast have slightly different locations.

The second option is labeled wind immersion.

Wind is visualized from the viewpoint of a user stand-

ing within the forecast area. Using a tablet with a for-

ward looking camera and sensors for geographical lo-

cation, tilt, pitch and direction, the location and view

window of the camera can be quite accurately deter-

mined. A user points the camera in any direction, and

can see the output from the camera on the tablet dis-

play with the corresponding wind forecast overlaid.

2 RELATED WORK

Uncertainly visualization of 2D vector ﬂow have been

subject to research by many groups. Isolines or iso-

surfaces may be extended for showing uncertainties

(Pothkow and Hege, 2011). In this paper two ap-

proaches are used. Either Tukey (Tukey, 1977) type

box plots for time series or glyph based (Wittenbrink

et al., 1996; Hlawatsch et al., 2011) for 2D maps.

Lodha et. al (Lodha et al., 1996) presents glyphs as

one of several ways of visualizing uncertainty. The

method used depends on the source of the uncertainty,

ie. observations with measurements errors, position-

ing errors or temporal uncertainties.

Wind roses

are often used for describing the vari-

ations of wind direction and speed at a location. The

wind direction are often limited to a small sett of sec-

tors, the number of occurrences in each sector are

used for the length of the plot and colors for indica-

tion frequencies of different wind speeds. Two differ-

ent datasets from the same location can also be com-

bined into one plot, as in (Carvalho et al., 2012). An

overview of some uncertainty visualization is found in

(Brodlie, 2008; Brodlie et al., 2012). In (MacEachren,

1992) several ways of visualizing bivariate data is ex-

plored, including the need to visualize both the origi-

nating data and the uncertainty at the same time. This

is useful when when visualizing uncertainty in wind

forecasts because both direction and wind speed have

their independent uncertainties that needs to be visu-

alized at the same time.

http://www.wcc.nrcs.usda.gov/climate/windrose.html

3 ARCHITECTURE

The CSWF systems is architected around two abstrac-

tions; The forecast abstraction and the forecast pre-

sentation abstraction. Due to the very different capa-

bilities of different devices it was found to be ben-

eﬁcial to separate the forecasts themselves from the

presentation. The architecture is illustrated in Figure

Forecast

Presentation

Forecasts

Local

Forecast

Collaborators

Forecasts

Amalgamated

Forecast

Figure 1: The CSWF system architecture.

The forecast abstraction deals with everything

about production, collaboration, processing and stor-

age of local forecasts, forecasts from collaborating

users and amalgamated forecasts. This abstraction en-

capsulates the most computing intensive parts of the

system and are meant to be located on one or more

stationary computers under the users control.

The forecast presentation abstraction provides the

user interface to the CSWF system. This part of the

system is highly dependent on the actual device used.

The prototype uses applications on many different de-

vices from mobile telephones to large Display walls.

4 DESIGN

The design of the CSWF system is based on a client-

server model. The server is assumed located on a

computer under the user’s full control and is assumed

to be accessible from all the users devices. The design

is illustrated in Figure 2.

The user’s device communicates with the fore-

cast abstraction frontend using HTTP and a REST-full

(Fielding, 2000) API. This simpliﬁes both the server

and the client, as no state is maintained when commu-

nicating between devices and the server.

The functionality of the home server is partitioned

into separate parts allowing for incremental develop-

ment and maintenance of the parts. Simple service

level agreements, SLA’s between the parts have been

written allowing for independent development. The

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Forecast Presentation Abstraction

Client

Application

Forecast Abstraction

Synchronous

request for

forecast

Reply with

URL

to forecast

Local

Forecast

Computation

Forecast

Amalgament

Forecast

Data

Collaboration

System

Frontend

Home server

User device

Figure 2: This CSWF system design.

design also allows for the various parts of the home

server to be executed on different computers. The ex-

ecution of the numerical atmospheric model is most

likely to be done on a dedicated stationary desktop

computer, preferably with many CPUs and/or many

cores. Part of the pre-processing and preparing for vi-

sualization also requires a standard desktop computer.

5 IMPLEMENTATION

The CSWF system is a set of multi-threaded pro-

cesses. Each sub-system comprises of one or several

processes. All server processes are compiled for run-

ning on either Linux or OS X. The 3.4 version of the

WRF atmospheric model is compiled for running on a

Linux computer. When executing the CSWF system

on an OS X computer, we either compute the WRF

model on the same machine using a virtualized Linux

environment or on a separate Linux computer.

Many of the services are so lightweight that they

can even be executed on a Raspberry Pi

computer.

This does not include the WRF model and some of

the pre-visualization production.

The CSWF system prototype supports a limited

number of data types. The WRF model results are

stored as NetCDF ﬁles

. Some of the presentation ap-

http://www.raspberrypi.org/

http://www.unidata.ucar.edu/software/netcdf/

plications use KML ﬁles

. Other applications use im-

ages, HTML pages and simple text ﬁles. Most such

ﬁles are generated as part of the local forecast produc-

tion.

Applications for the forecast presentation abstrac-

tion have been implemented for a small range of plat-

forms and operating systems. This include applica-

tions for iPhones, iPads, mobile device web browsers,

web browsers on laptops or stationary devices and

also a simple data conversion programs to create data

usable in the standard visualizing desktop applica-

tion DIANA (Martinsen et al., 2005) developed by the

Norwegian Meteorological Institute.

The forecast presentation application is written in

Objective C for iPhones/iPads. Javascript is used to

visualize forecasts in a browser. For special devices

like a display wall, we used C++ and Python to visual-

ize the forecasts. C and Python was used for the other

processes and for controlling purposes. The WRF at-

mospheric model is mostly written in Fortran, using

MPI for internal communication.

5.1 Firewall and NAT

Access to a CSWF system at home from a roaming

mobile device is complicated because the home net-

works are often behind ﬁrewalls, and use network ad-

dress translation, NAT. The CSWF system can be ac-

cessed from devices on external networks using NAT

traversal techniques (M

uller et al., 2010). In the pro-

totype, no techniques for NAT traversal are used. The

CSWF system is expected to be accessible by cor-

rect setup of ﬁrewalls, possibly using Simple Network

Management Protocol (SNMP) and Universal Plug &

Play (UPnP).

5.2 Background Data

Forecast presentation applications can request fore-

casts for any small geographical area for a 6-hour pe-

riod. The prototype is limited to an area including

Scandinavia because of limitations in disk space for

the background topographical and other data. Global

coverage of these static background data sets is freely

available, and takes around 10 GB of storage at the

current spatial resolution. With this background data

the user will have the potential of creating forecast for

anywhere on the globe. Background meteorological

data is available from many sources. The prototype

uses the global dataset from NOAA’s NOMAD

ser-

vice.

https://developers.google.com/kml/documentation/

http://nomads.ncep.noaa.gov/

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6 USER APPLICATIONS

Several prototype forecast presentation applications

for Linux, OS X and iOS have been created. Some of

these are described brieﬂy in the following sections.

Each user will be able to use the applications on var-

ious platforms to specify and adjust the visualization

of the meteorological parameters for various speciﬁc

purposes.

The following sections presents some of the ap-

plications with focus on visualizing uncertainty in

weather forecasts from the CSWF system. All appli-

cations exist as prototypes and most are in daily use.

6.1 iPhone and iPad

To visualize forecasts in 2D and on typical mobile

platforms, a browser on the forecast presentation de-

vice is used. The browser runs a small Javascript vi-

sualization script that uses the Google Maps API. The

script pulls in image tiles from CSWF and renders

them on the presentation device. See Figure 3.

Figure 3: An example showing wind and temperature on a

smart phone.

An application for a tablet that shows the current

view of the back facing camera overlaid with meteo-

rological information have also been created. Using

data from the GPS, the compass and the accelerom-

eters on the device, we know where the device is lo-

cated, which way the camera is facing and the tilt of

the device.

The user can request a forecast centered on the

GPS location of a tablet, and then explore the weather

forecast by pointing the tablet’s camera into the sur-

rounding landscape to study on the tablets display the

weather forecast superimposed with the camera im-

age.

To view data from another location, the user must

physically move around. A screen shot of the applica-

tion with two different types of visualization is shown

in Figures 4 and 5. This application uses a device

with a screen size usable for detailed visualization.

The device has communication capabilities sufﬁcient

to receive the data in KML format, and has the pro-

cessing power to do the visualization on the device.

Figure 4: Screenshot of tablet with camera and an example

of overlaid meteorological information

6.2 Home Computer

Data from the CSWF system can be visualized in a

browser or in standard visualization applications, like

the DIANA system. Visualizing the uncertainty in the

forecasts from the CSWF system can be done using

two slightly different techniques. On is utilizing all

forecast for a speciﬁc time in a classical wind rose

where the length of each arrow represents the wind

speed, and the direction represent the wind direction.

See Figure 6. Here all the forecasts are relocated to

the nearest grid point in the local forecast produced

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Figure 5: Screenshot of tablet with streamlines with colored

points that can be animated to illustrate wind speed.

bye the user. All 28 available forecasts are used in the

ﬁgure.

symb−a−00 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−02 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−04 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−06 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−08 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−10 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−12 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−14 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−16 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−18 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−a−20 WIND.SYMB (+6) 2013−02−11 18 UTC

Monday 2013−02−11 18 UTC

Figure 6: Many forecasts visualized as a classical wind rose

using individual glyphs for each forecasts originating in the

same location using the local forecast grid. A legend outside

the shown area on the ﬁgure shows the length-to-speed.

One other way of illustrating the uncertainty of the

small number of forecasts is illustrated in Figure 7.

Here a small number of forecast are visualized using

the original grid from each forecast. The length rep-

resents the wind speed and the direction the wind di-

rection. As can be seen this can very easily get very

crowded and only 10 different forecasts are used in

this ﬁgure.

6.3 Display Wall

The TromsøDisplay Wall (Anshus et al., 2013) con-

sists of 28 PCs driving 28 projectors for a total screen

symb−p−20 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−18 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−16 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−14 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−12 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−10 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−08 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−06 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−04 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−02 WIND.SYMB (+6) 2013−02−11 18 UTC

symb−p−00 WIND.SYMB (+6) 2013−02−11 18 UTC

Monday 2013−02−11 18 UTC

Figure 7: Many forecasts visualized on the same time. Indi-

vidual glyphs for each forecast originating in each forecasts

grid. A legend outside the shown area on the ﬁgure shows

the length-to-speed.

size of 22Mpixels. Applications can be executed on

a PC utilizing a very large virtual VNC frame buffer

(Stødle et al., 2007) used by the 28 viewers in the

driving PCs.

Using the DIANA system allows for utilizing the

whole display for visualizing all forecasts as illus-

trated in Figure 8. One example where the very large

pixel count is used for visualizing many individual

forecasts at the same time is illustrated in Figure 9.

Figure 8: The DIANA application used on the display wall

Figure 9: Example of many forecasts at the same time on a

large display wall.

UncertaintyEstimationandVisualizationofWindinWeatherForecasts

325

7 RESULTS

The prototype of the CSWF system demonstrates

that individually produced numerical weather fore-

casts can be combined for a better estimation of some

of the uncertainties in the forecasts. The combined

forecasts and the uncertainty estimation can be illus-

trated with Figure 10. The ﬁgure shows the observed

wind speeds at two measuring stations approximately

3 km from each other. The observed wind speed is

a solid curve. The ﬁgures also show the locally pro-

duced forecast with the dashed curve. The collabo-

rative exchanged forecasts with 27 other CSWF sys-

tems randomly spread around the location of the lo-

cal forecasts are collected, and the mean and stan-

dard deviation of the wind speed is calculated for

each point in the local forecast grid. This symbi-

otic forecast is illustrated in the Figure using a box

plot/candlestick form showing the box for ± one stan-

dard deviation, and the whiskers for ± two standard

deviations. The Figure contains 6 hour forecasts and

observations valid at 18 UTC each day.

The top part of Figure 10 shows the results from

station on top of the Island in an area with high trees.

The forecast model predicts often to strong wind.

This is the expected performance of the WRF model

in an area with increased ground-induced turbulence

from the trees.

The bottom part of Figure 10 shows the results

from the Tromsø airport. The wind gauge is situated

in an open ﬁeld by the runway. The forecast model

has better skill at this station. This is also the ex-

pected performance of the model. The observed wind

speed in the lower part of the ﬁgure is clearly outside

the range from the CSWF system in only three of 19

days, and the mean of the CSWF forecasts better than

the single local forecast on all but one, days.

8 DISCUSSION

We compared actual observed values for wind speed

and direction with a single as well as a symbiotic fore-

cast. A single forecast typically does not quite match

reality. However, it is relatively close. This is a val-

idation that it is meaningful to do even a single fore-

cast, and that the approach used by CSWF to do so

is sound meteorologically. The WRF model used by

CSWF have earlier also been independently validated

(Carvalho et al., 2012). A symbiotic forecast more

often matches the actual wind than a single forecast.

Only rarely is the actual observed wind signiﬁcantly

outside the uncertainty range of the symbiotic fore-

casts. A way of illustrating a single and a symbiotic

forecast against observed wind speed and directions,

is given in Figure 10. The ﬁgure visualizes the un-

certainty and distribution of the uncertainty using a

candlestick/box plot type visualization.

In Figure 10 a set of forecasts of wind speed, is

compared against the observed wind speed at a spe-

ciﬁc point in time, which is a very strict comparison.

The single local forecasts have some skill in forecast-

ing the wind speed, and the combined forecasts from

the CSWF system have even better skill.

When validating high-resolution numerical mod-

els, it is a problem that the spatial resolution may be

better than the resolution of some of the meta-data as-

sociated with the observation. In our case, the actual

geographical location of the measuring point for wind

speed at the airport was not the location given by the

meta-data. For historical reasons the location of the

meteorological stations at airports have been listed as

the location of the barometer. However, in our case

this was approximate one kilometer from the location

of the wind sensor.

Visualizing a set of forecasts from the CSWF sys-

tem using the DIANA application on a desktop com-

puter and on the large display wall illustrates the ef-

fect of the display size on the usability of the visu-

alization. The wind rose-like approach illustrated in

Figure 6 was judged as usable on both devices. The

compact form easily visualizes the complete set of 28

forecasts and the effectively illustrates uncertainty in

both wind speed and direction. The main difference

between the desktop computer and the display wall is

that on the display wall a larger area can be viewed

at the same time. A more compact glyph integrating

both variation and spread could also have been used.

Since this approach will display all forecasts, outliers

that would be masked using aggregate glyphs, are eas-

ily recognized. This is particularly noticeable in loca-

tions with large variation in wind direction. Using in-

dividual glyphs for each forecasts also allows the user

to identify possible speciﬁc dangerous combinations

of wind speed and direction, that may be masked us-

ing an aggregated glyph. The wind rose method will

of cause increase the burden on the user to interpret

the plots correctly compared to aggregated glyphs.

However, using the wind chaos approach illus-

trated in Figure 7 is very different on the two de-

vices. On the desktop, only a few of the 28 avail-

able forecasts could be usefully visualized at the same

time. With 28 forecasts simultaneously visualized,

the desktop display simply ﬁlled up with arrows, and

no pattern could be discerned. The much larger num-

ber of pixels available on the display wall allows for

much more information and allowed for all 28 fore-

casts to be shown while it was still possible to iden-

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0 2 4 6 8 10 12 14 16 18 20

Windspeed m/s

Day, June 2013

90450 TROMSO

Symbiotic Forecasts

Local forecast

Observations

0 2 4 6 8 10 12 14 16 18 20

Windspeed m/s

Day, June 2013

90490 TROMSO - LANGNES

Symbiotic Forecasts

Local forecast

Observations

Figure 10: Local forecasts using dashed lines and stars, CSWF system forecasts using box plot with whiskers and observed

wind speeds using solid lines and ﬁlled rectangles. The two measuring stations are approximate 3 km apart, illustrating the

large spatial variations in model forecasts and especially in the observations.

tify areas with large variation and therefore large un-

certainty. This is also illustrated in Figure 9. This

also preserves the actual grid location of each fore-

cast, allowing for a better understanding of the spatial

distribution of the wind speed and direction.

The augmented reality see through effect using a

tablet as illustrated in Figures 4 and 5, is more re-

stricting in terms of how the uncertainty is visualized.

For each wind arrow shown in Figure 4 the Proba-

bility Density Function, PDF, of the uncertainty is

known and can be represented by, say, the standard

deviation in that point. This value can then be used

for coloring the wind arrow where the size and direc-

tion represents the values from the local forecasts. A

color gradient from green to red, representing small to

large uncertainties is therefore possible. In the same

way the streamlines illustrated in Figure 5 illustrate

the uncertainty at points in the grid by varying the

width. The direction and speed is illustrated by us-

ing colored sections that are animated moving along

the streamlines in a speed proportional to the wind

speed at that point. A possible extension would be

to implement graduated glyphs like the Noodles sys-

tem (Sanyal et al., 2010) along the streamlines. Using

individual isolines for a parameter, a contour boxplot

(Whitaker et al., 2013) could also be used for this type

of augmented reality visualization.

9 CONCLUSIONS

The two different approches for visualizing the un-

certainty of weather forecasts generated by the Col-

laborative Symbiotic Weather Forecast system clearly

have their uses in different settings and for different

purposes.

The augmented reality visualization demands that

the user is located within the forecasted area and have

good visual overview around.

The map-based visualization can be used regard-

less of actual location and is also suitable for use on

very large displays.

ACKNOWLEDGEMENTS

This work has been supported by the Norwegian Re-

search Council, projects No. 159936/V30, SHARE

- A Distributed Shared Virtual Desktop for Sim-

ple, Scalable and Robust Resource Sharing across

Computers, Storage and Display Devices, and No.

155550/420 - Display Wall with Compute Cluster.

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327

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