Intelligent Agents in the Australian Bureau of

MeteorologySandy 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

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

Forecast “Database”

• Machine-readable forecasts in database• Forecaster “personal digital assistant” (PDA)• Automatic alerting• Multi “view” product generation• Integration of existing systems

A project to enhance the forecasting process,

involving:

radar

satellite

model

AWS

db1 db2 db3

products

Forecast DB – stage 1

interfaces

Intelligent Alerts Goals

• Forecaster PDA

• Alerts from inconsistency between Forecast / Guidance / Observations

• Weather element alerts, eg temp

• Severe weather event alerts, eg hail

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

Consistency Alerts

Inter-comparison between:

• Forecasts and observations (verification),

• Observations and guidance,

• Guidance and forecasts.

(guidance = numerical atmospheric model)

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!

Forecast DB - with agents

radar

satellite

model

AWS

db1 db2 db3

Microburstdetector

frontdetector

??detector

??detector ???

Storm track

?? alert

Cold front

forecast

special

warning

…and again in more detail.

An example of an agent –based detector: microburst detection

Reflectivity output showing detected microbursts

(see www.bom.gov.au/weather/radar/ for more radar)

Exploratory pilot projectTo 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.

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 TAF

A typical AWS

Alerting agent pilot

Data flow view of pilot agent network.

Research issues raisedThe 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 managementThese are research issues that will be dealt with in a possible

ARC Linkage grant in association with RMIT and AOS.

Self-describing data

• 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!

We require a data representation that:

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

Service lookup

• 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

Agents will need to seek data sources upon startup, as well as continuously during operation.

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.