We report on some initial work in which we designed interactive graphics to help climate scientists identify and extract good examples of simulated storm-tracks from a large dataset to help disseminate information to various audiences. A side-effect of this work was that the exploratory potential offered by the interactive graphics helped our climate scientist coauthors validate and interpret their data in a way that was not previously possible for them. We are extending this work to provide support for a wider range of validation and interpretative tasks, with a focus on answering questions of relevance to the insurance industry. We describe our collaborative approach, that draws on ideas from ’patchwork prototyping’ [2, 5] in which a rapid iterative process of design, implementation and testing, is used to help provide the functionality to support a set of ‘user stories’

Dykes, J.

Slingsby, A.

Strachan, J.

Vidale, P.

Wood, J.

English

City Research Online

Slingsby, A., Strachan, J., Dykes, J., Wood, J. & Vidale, P. (2011). Designing Interactive Graphics for Validating and Interpreting Storm Track Model Outputs. Paper presented at the GeoViz workshop, Mar 2011, Hamburg, Germany.City Research OnlineOriginal citation: Slingsby, A., Strachan, J., Dykes, J., Wood, J. & Vidale, P. (2011). Designing Interactive Graphics for Validating and Interpreting Storm Track Model Outputs. Paper presented at the GeoViz workshop, Mar 2011, Hamburg, Germany.Permanent City Research Online URL: http://openaccess.city.ac.uk/561/ Copyright & reuseCity  University  London has developed City  Research Online  so that  its  users  may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders. Users may download and/ or print one  copy  of  any  article(s)  in  City  Research  Online  to  facilitate  their  private  study  or  for  non-commercial research. Users may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain. All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact  with the author(s) of this paper, please email the team at publications@city.ac.uk.Designing Interactive Graphics for Validating and Interpreting StormTrack Model OutputsAidan Slingsby, Jane Strachan, Jason Dykes, Jo Wood and Pier-Luigi VidaleAbstract— We report on some initial work in which we designed interactive graphics to help climate scientists identify and extractgood examples of simulated storm-tracks from a large dataset to help disseminate information to various audiences. A side-effectof this work was that the exploratory potential offered by the interactive graphics helped our climate scientist coauthors validate andinterpret their data in a way that was not previously possible for them.We are extending this work to provide support for a wider range of validation and interpretative tasks, with a focus on answeringquestions of relevance to the insurance industry. We describe our collaborative approach, that draws on ideas from ’patchworkprototyping’ [2, 5] in which a rapid iterative process of design, implementation and testing, is used to help provide the functionality tosupport a set of ‘user stories’.Index Terms—Visualization, hurricane, wind storm, insurance, collaborative.1 INTRODUCTIONAdvances in supercomputer technology are enabling climate scientiststo run increasingly large and detailed climate simulation models [3].These help gain a better understanding of the dynamics and naturalvariability of the global climate system, but create large amounts ofdata which need to be processed and validated prior being analysed.The NCAS (National Centre for Atmospheric Science) is using highresolution GCMs (General Circulation Models) that allow individualstorms to be resolved. A feature tracking algorithm [4] developed atthe University of Reading, is used to extract storm tracks from thesimulations. This process produces datasets containing thousands ofsimulated storm tracks at a temporal resolution of six-hours that spanmany decades. These have the potential to provide a more realisticreflection of natural variability in the climate system than from histor-ical records alone, and are being used to advise the insurance industryon its exposure to atmospheric risk. This is made possible throughour membership of the Willis Research Network, the world’s largestcollaboration between academia and the insurance industry.In recent collaborative work [6], we designed and implementeda tool that enabled climate scientists to visually identify storm-trackconfigurations of interest and export these as video clips. These videoclips contained tangible examples of storm-track genesis and develop-ment and they were used to illustrate examples about atmospheric riskto the insurance industry.2 THE TOOLThe tool was designed to be a simple means of identifying examplesto illustrate presentations about atmospheric risk and then exportingthese as video clips.It animates storm-track genesis and development through time, ona zoomable world map and within a user-definable temporal window.• Aidan Slingsby is with the giCentre, City University London, E-mail:a.slingsby@soi.city.ac.uk.• Jane Strachan is with NCAS, University of Reading, E-mail:j.strachan@reading.ac.uk.• Jason Dykes is with the giCentre, City University London, E-mail:jad7@soi.city.ac.uk.• Jo Wood is with the giCentre, City University London, E-mail:jwo@soi.city.ac.uk.• Pier-Luigi Vidale is with NCAS, University of Reading, E-mail:p.l.vidale@reading.ac.uk.Manuscript received 31 March 2010; accepted 1 August 2010; posted online24 October 2010; mailed on 16 October 2010.For information on obtaining reprints of this article, please sendemail to: tvcg@computer.org.Transparency along the track indicates age within the chosen temporalwindow and its thickness indicates vorticity. Users can jump to anyposition along the timeline with a simple mouse movement and candisplay the tracks at different pressure levels. Track configurationsthat are visually interesting can be identified and exported to videoclips. A video that demonstrates this is available [6].3 EXPLORATORY VISUAL ANALYSIS FOR CLIMATE SCIENCEAlthough the tool was not initially intended for exploratory visualanalysis, our climate science coauthors quickly recognised its ex-ploratory potential as it provided a level of interaction with their datanot previously experienced. In particular, they appreciated being ableto explore the dataset without a priori assumptions. This was in con-trast with the existing tools available in climate science, where a pri-ori assumptions are required when preparing and processing the data.They found that visually exploring the dataset interactively, helpedthem identify and develop hypotheses to test, making their existinganalysis tools more useful.Fig. 1. Co-genesis of tracks in Central America and West Africa.Fig. 2. Storm-tracks that initially form together, but subsequently di-verge. Similar to those during last year’s Atlantic season (2010).Fig. 3. Storm-tracks that go through an ‘extra-tropical’ transition.In addition to the original purpose of the tool, the tool assistedmodel validation and helped identify the effects of climate conditionson the characteristics and configuration of storm-tracks. Some usefulobservations included:• The spatial and temporal clustering of track genesis in Fig. 1 in-dicates time periods where conditions appear to be favourable fortropical cyclone development. Sometimes, these tracks developalong similar paths. In other cases, such as in Fig. 2, they di-verge, following different paths. These differences are likely dueto the relative effects of local and large-scale conditions. Estab-lishing these steering conditions is important to understandingstorm behaviour.• Fig. 3 shows tracks that make an extra-tropical transition, i.e.change from tropical storms (warm core storms) to those that af-fect higher latitudes (cold core storms). Again, conditions underwhich this occurs needed to be established.• Fig. 2 was recognised as being similar to the Atlantic season lastyear (2010), prompting the question of whether the conditionswere also similar, an observation which merits further explo-ration of model and observational data.• Increasing the time window enables comparison of basins overseveral seasons. In some years, the season starts earlier than inothers and the precise reasons for this are not known.4 DESIGNING FURTHER FUNCTIONALITYWe are extending this initial work by adding functionality to supportother types of task.The process we are using to achieve this draws on some of theideas from ‘patchwork prototyping’ [2] where ideas are implementedquickly and there is a rapid turnaround between implementation andtesting and strong collaboration throughout. This approach work hasworked well for us in the past [5].To guide the process of designing new functionality, we use scenar-ios that describe what a user is trying to find out and why. Scenarioscan be thought of as stories [1] and term these ‘user stories’, whichare descriptions of focussed tasks, and why they are important. It isimportant that user stories are written in terms of the task that needs tobe fulfilled rather than in terms of functionality that might be required.Our designs are informed by the information visualisation literatureand our experience of designing visual analysis techniques.5 USER STORIESWe have identified user stories which describe tasks which the climatescientists would like to carry out using interactive visualisation.• What climatic conditions lead to the co-genesis of tracks, the co-development of tracks along the same path and the divergenceof paths? Spatial and temporal clustering is of particular impor-tance to the insurance industry because the impact of multipleevents at the same time and place is much greater than for justone event. The insurance industry traditionally considers eventsas independent, yet storms have been observed to cluster in timeand space.• How does the geographical location of tropical cyclones trackschange between El Nino, La Nina and neutral years? This hasimplications for risk management because the state of the ElNino Southern Oscillation (ENSO) can provide an early indica-tor to seasonal tropical cyclone activity. Improved understandingof the connection between ENSO and tropical cyclone activitycould improve our predictive ability.• When do storms make extratropical transition (change from trop-ical storms to storms affecting mid-latitudes)? What climaticconditions lead to this? It is important, because tropical cyclonesand mid-latitude storms are usually considered as independent ofeach other in the insurance industry, yet they are often just dif-ferent parts of the same storm track.• How many storms make landfall in both in the US (as tropicalstorms) and Europe (as windstorms)? Related to the above.• How does sea surface temperature affect the location and inten-sity of our simulated tropical storms in the different basins? Doesthis relationship agree with observational findings? This ques-tion is important for model validation and our understanding ofhow climate change may affect tropical cyclone activity.• How does sea-surface temperature change with the North At-lantic Oscillation index and how does this affect our simulatedtropical cyclone activity? This question also relates to modelvalidation, but is also important from natural climate variabilitypoint of view.• What conditions lead to storm seasons starting early or late?6 DESIGN CONSIDERATIONSClimatic conditions. Most of these user-stories involve establishingthe climatic conditions under which particular configurations of storm-tracks occur. Providing access to these is likely to be useful, but needto establish exactly which to provide.Comparison. Many of the user stories involve comparison. The toolallows spatial comparison (through the map) but only filtered througha temporal window. Temporal fading helps make temporal compar-isons, but only within particular temporal windows. Other temporalcomparisons are difficult, including that between different cyclic unitsof time. Non-spatial and temporal comparisons are difficult.Filter The tool currently temporally filters, but some user storieswould benefit from other types of temporal filtering (e.g. month), spa-tial filtering (e.g. East Coast of US) and filtering using other attributes.Highlight/symbolise. Highlighting tracks that meeting particularcriteria is likely to aid comparison.7 CONCLUSIONWe have demonstrated potential for exploratory visual analysis for val-idating and interpreting of storm-track data. We are designing func-tionality to support this through a collaborative and iterative process.ACKNOWLEDGMENTSThis work is supported by the Willis Research Network.REFERENCES[1] J. Carroll. Five reasons for scenario-based design. Interacting with Com-puters, 13(1):43–60, 2000.[2] I. R. Floyd, M. C. Jones, D. Rathi, and M. B. Twidale. Web mash-upsand patchwork prototyping:. In System Sciences, 2007. HICSS 2007. 40thAnnual Hawaii International Conference on, page 86, 2007.[3] A. Gilchrist. Numerical simulation of climate and climate change. Nature,276(5686):342–345, 1979.[4] K. I. Hodges. Feature tracking on a unit sphere. Monthly Weather Review,123:3458–3465, 1995.[5] A. Slingsby, R. Lowe, J. Dykes, D. Stephenson, J. Wood, and T. Jupp. Apilot study for the collaborative development of new ways of visualisingseasonal climate forecasts. Proceedings of GIS Research UK (GISRUK),pages 291–297, Durham, 2009.[6] A. Slingsby, J. Strachan, P.-L. Vidale, J. Dykes, and J. Wood. Making hur-ricane tracks accessible. http://www.discoveryexhibition.org/pmwiki.php/Entries/Slingsby2010, 2010.

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