A knowledge-based system to generate internet weather forecasts.

Dr Harvey Stern,Bureau of Meteorology, Australia

… a work in progress ...

IntroductionIntroduction

• A “pilot” knowledge-based system for the generation of internet forecasts is described.

• The system has been developed for Victoria.

• Forecasts generated include those for public, aviation, marine and media interests.

Description of the System.Description of the System.

• At the core of the system is an algorithm, written in HTML (and incorporating some JavaScript).

• The algorithm combines statistical interpretation of NWP output with other knowledge.

• The statistical interpretation component includes identification of the synoptic type.

Synoptic TypingSynoptic Typing

• The basis for the system is identification of the expected synoptic type.

• The direction, strength and curvature of the isobaric (surface) flow determine the type.

• These characteristics are determined from a grid of forecast pressure values.

Generating the OutputGenerating the Output

• Data associated with the identified type are statistically analysed.

• In a Perfect Prog mode, statistical relationships so derived are used to generate forecasts.

• HTML Code (incorporating JavaScript) is generated.

• The Code is uploaded to a Web Site.

Opening View.Opening View.

Entering the DataEntering the Data

Generating the Code.Generating the Code.

The “Bank” of Experience• Ramage proposed an “iterative” approach to locking in

improvements in forecasting.

• This is, indeed, the approach adopted here.

• Thereby, the skill increases as new knowledge is incorporated.

• Hence, progress is made towards the realisation of Ramage’s dream.

Multi-lingual FeatureMulti-lingual Feature

• An increasing component of the WEB is in languages other than English.

• Chinese may become the common language of the WEB.

• The system has a component that generates a forecast summary in Chinese.

The Output GeneratedThe Output Generated

Segment of the OutputSegment of the Output

Sample of the Forecasts.Sample of the Forecasts.

The Aviation Forecasts.The Aviation Forecasts.

Verification of SystemVerification of System

• Five skill measures are used.

• These are MIN, MAX, QPF, Precip/No Precip (P/NP), & BRIER.

• Skill measures are positive for forecasts better than climatology.

A Preliminary TrialA Preliminary Trial

• Conducted (last April) on an earlier (and much abbreviated) version of the system.

• Evaluation limited to one month, one location (Melbourne), and to day one.

A Subsequent TrialA Subsequent Trial

• Conducted (last November).

• Evaluation extended to include days one to seven.

• Still only for one location.

Combined Skill of Subsequent TrialCombined Skill of Subsequent Trial

Skill of Subsequent Trial’sSkill of Subsequent Trial’sMin Temp ForecastsMin Temp Forecasts

Skill of Subsequent Trial’sSkill of Subsequent Trial’sMax Temp ForecastsMax Temp Forecasts

Skill of Subsequent Trial’sSkill of Subsequent Trial’sQuantitative Precipitation ForecastsQuantitative Precipitation Forecasts

System PerformanceSystem Performance

• Skill Measures all show system forecasts better than climatology.

• They also are better than persistence.

• They are (on most measures) inferior to official forecasts, especially for days 1 and 2;

- with the notable exception of minimum temperature forecasts for days 3 to 7, inclusive.

• System forecasts would be expected to improve as new knowledge is incorporated.

Future Plans

• Extensively verify the system, covering all current observing sites.

• Enhance the sophistication of the statistical analysis.

• Incorporate new forecaster knowledge.

• Extend the multi-lingual feature to Australian indigenous languages.