Intelligent Agents in the Australian Bureau of Meteorology

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Intelligent Agents in the Australian Bureau of
Meteorology

• Sandy Dance and Mal Gorman

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

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Forecast DB stage 1
radar
satellite
products
AWS
model
interfaces
db1
db2
db3
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Intelligent Alerts Goals

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

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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

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Consistency Alerts

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

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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!

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Forecast DB - with agents
Cold front
radar
Microburst detector
forecast
satellite
?? alert
front detector
warning
AWS
?? detector
Storm track
special
model
?? detector
???
db1
db2
db3
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and again in more detail.
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An example of an agent based detector
microburst detection
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Reflectivity output showing detected microbursts
(see www.bom.gov.au/weather/radar/ for more radar)
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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.

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

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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!

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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 DAMLOIL, or
  extensions or alternatives to these. Again an
  open research question.
• DARPA agent markup language, ontology inference
  language

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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

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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.