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Intelligent Agents in the Australian Bureau of Meteorology

Intelligent Agents in the Australian Bureau of Meteorology. Sandy Dance and Mal Gorman. Introduction. About the Bureau of Meteorology Project to improve forecast process Alerts Agents in Bureau TAF alert pilot project Research issues The future.

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Intelligent Agents in the Australian Bureau of Meteorology

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  1. Intelligent Agents in the Australian Bureau of Meteorology Sandy Dance and Mal Gorman

  2. Introduction • About the Bureau of Meteorology • Project to improve forecast process • Alerts • Agents in Bureau • TAF alert pilot project • Research issues • The future

  3. New Bureau building in March 2004, 700 Collins St, Docklands.

  4. Forecast “Database” A project to enhance the forecasting process, involving: • Machine-readable forecasts in database • Forecaster “personal digital assistant” (PDA) • Automatic alerting • Multi “view” product generation • Integration of existing systems

  5. ForecastDB – stage 1 radar satellite products AWS model interfaces db1 db2 db3

  6. Intelligent Alerts Goals • Forecaster PDA • Alerts from inconsistency between Forecast / Guidance / Observations • Weather element alerts, eg temp • Severe weather event alerts, eg hail

  7. Forecaster PDA • Manage alerts • Sanity check for forecasts (“deviates from climate”) • Arrival alerts (ie, latest model, satellite images) • ‘elephant stamps’ for successful unusual forecasts • Automatic text generation for various forecast types • Graphical editing of numerical forecast • Control of alerting through media such as SMS, email, phone

  8. Consistency Alerts Inter-comparison between: • Forecasts and observations (verification), • Observations and guidance, • Guidance and forecasts. (guidance = numerical atmospheric model)

  9. Severe weather alerts • Storm alerts from radar • Microburst from radar • Tornado from radar • Hail from radar • Lightning from radar and GPATS • Fronts from satellite ….this is not exhaustive!

  10. Forecast DB - with agents Cold front radar Microburst detector forecast satellite ?? alert front detector warning AWS ?? detector Stormtrack special model ?? detector ??? db1 db2 db3

  11. …and again in more detail.

  12. An example of an agent –based detector: microburst detection

  13. Reflectivity output showing detected microbursts (see www.bom.gov.au/weather/radar/ for more radar)

  14. Exploratory pilot project To trial an end-to-end system employing Jack agents to alert on discrepancies between aviation forecasts and observations. • Inputs: TAF (forecast) and AWS (observation) data from decoders • Passed by TCP/IP and Jacob to Jack agent network • An agent handles subscription to data of interest by other agents • A monitoring agent issues alerts upon discrepancies between TAF and AWS data • GUI subscribes to alerts and displays them under control of forecaster. Conducted in collaboration with RMIT Agents Group and Agent Oriented Software Pty Ltd.

  15. A typical TAF TAF YMML 122218Z 0024 24006KT 9999 FEW025 BKN030 FM02 18015KT 9999 SCT040 FM17 25006KT 9999 BKN025 T 15 19 20 16 Q 1028 1026 1025 1026 A typical AWS

  16. Alerting agent pilot Data flow view of pilot agent network.

  17. Research issues raised The wish list from the Bureau, plus experience from the pilot project, highlight our requirements for a large scale Bureau agent network. These include: • Self-describing data • Service description • Service lookup • Failure handling • Dynamic quality-of-service management These are research issues that will be dealt with in a possible ARC Linkage grant in association with RMIT and AOS.

  18. Self-describing data We require a data representation that: • Allows agents to interpret data from elsewhere sensibly • Allows reasoning about data • Allows translation between related concepts. Could use our in-house metadata-rich tree-table-xml. Or more generally, an object model that can represent rich agent-oriented semantics and ontologies with data. A research question!

  19. Service Description • Services will need to be advertised and searched. • Must allow efficient reasoning about services, • Must express the data provided, the transformations made, and the quality of the data and service. Could use technologies like DAML+OIL*, or extensions or alternatives to these. Again an open research question. * DARPA agent markup language, ontology inference language

  20. Service lookup Agents will need to seek data sources upon startup, as well as continuously during operation. • Must allow new services to compete with old • Handle data source failure or removal by seeking alternatives • Handle vastly different temporal characteristics of data sources

  21. The future • Extend the pilot to more stations, datatypes, forecast types, alerting scenarios. • Merge with forecaster GUI under development • Incorporate severe weather detectors into the network. • Pursue research issues to give us agents that can find and talk to each other – possible ARC Linkage grant! • Gradually infiltrate agents throughout the Bureau.

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