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A knowledge-based system to generate internet weather forecasts.

A knowledge-based system to generate internet weather forecasts. Dr Harvey Stern, Bureau of Meteorology, Australia. … a work in progress . Introduction. A “pilot” knowledge-based system for the generation of internet forecasts is described. The system has been developed for Victoria.

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A knowledge-based system to generate internet weather forecasts.

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  1. A knowledge-based system to generate internet weather forecasts. Dr Harvey Stern, Bureau of Meteorology, Australia

  2. … a work in progress ...

  3. Introduction • 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.

  4. 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.

  5. Synoptic 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.

  6. Generating 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.

  7. Opening View.

  8. Entering the Data

  9. Generating the Code.

  10. 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.

  11. Multi-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.

  12. The Output Generated

  13. Segment of the Output

  14. Sample of the Forecasts.

  15. The Aviation Forecasts.

  16. Verification 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.

  17. A 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.

  18. A Subsequent Trial • Conducted (last November). • Evaluation extended to include days one to seven. • Still only for one location.

  19. Combined Skill of Subsequent Trial

  20. Skill of Subsequent Trial’sMin Temp Forecasts

  21. Skill of Subsequent Trial’sMax Temp Forecasts

  22. Skill of Subsequent Trial’sQuantitative Precipitation Forecasts

  23. System 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.

  24. 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.

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