1. New Applications for Forecasters EGOWS 2007 Heleen ter Pelkwijk

2. The items of this presentation Rich Intranet Application Dashboard Product Generation Application for the use of Conditional Climatology

3. Rich Intranet Application Purpose: Create an easily accessible operational intranet environment (weather items) for the forecasters.

4. Rich Intranet Application Reason: -More than half of the forecasters make frequent use of internet/intranet -Researchers want to have an easily accessible outlet to show the forecasters their experimental products.

5. Rich Intranet Application Method: - Creation of two intranet domains: Proeftuin.knmi.nl (the playground for scientists) Oper.knmi.nl (the operational website) - Creation of a combined menu (portal)

6. Rich Intranet Application Method: Definition of operational intranet system: Data streams, data processing and the server are monitored and when needed repaired 24 X 7. The experimental site has an operational server but no operational data!

7. Rich Intranet Application Result: -The menu is created from a database -All information has a tag “oper” or “exper” -All information has its own unique product number

8. Rich Intranet Application Result:

9. Rich Intranet Application Result:

10. Rich Intranet Application Result:

11. Rich Intranet Application Result:

12. Rich Intranet Application Result:

13. Rich Intranet Application Result:

14. Rich Intranet Application Result:

15. Rich Intranet Application Conclusion: Do we still need a dedicated meteorological workstation?

16. The items of this presentation Rich Intranet Application Dashboard Product Generation Application for the use of Conditional Climatology

17. Dashboard Purpose: - Dashboard = the interface of a monitoring tool - Dashboard gives a signal (trigger) in case a severe event occurs.

18. Dashboard Reason: - The forecaster has to handle more and more data - The forecaster has to make the right decision as soon as possible Desired results: -M-Makes the forecaster aware of important phenomenon as soon as possible -Q-Quality and efficiency -O-Optimal man-machine mix

19. Method: Possible monitoring types - observations – products (standard/incidental) - observations – model - model – model - model – product (difference can be a choice) - observation – observation - product – product (consistency) - Product – time (monitoring)

20. RADAR trigger module trigger dashboard detailed info (intranet) product editor Dashboard: the concept ground obs. model

21. Dashboard Realised at this moment 1.Observations – product - accumulation of precipitation from the radar / limit for a weather alarm - thunderstorm data from “FLITS” / limit for a weather alarm - wind observations / limit for a weather alarm - snow and blowing snow / limit for a weather alarm 2. Model – product - EPS / limit for a weather alarm - EPS / warnings for customers

22. Dashboard Result: What does it look like?

23. Dashboard Result: What does it look like?

24. Dashboard Problems: - Not too many triggers! - How to keep it conveniently arranged

25. Dashboard Solutions: - The use of smart mechanisms to reduce the amount of triggers - Filter and sort possibilities - Link triggers to the content of the forecasts

26. The items of this presentation Rich Intranet Application Dashboard Product Generation Application for the use of Conditional Climatology

27. Product Generation Purpose: Reducing the “routine jobs” by automate part of the standard production.

28. Product Generation Reason: -To make more efficient use of the time of the forecaster. -To make more time for the important things during the shift like advising customers and severe weather monitoring. -To make it possible to work with weather dependent shifts…..more time for applied research.

29. Product Generation Method:

30. data customers Forecaster: 1. Monitoring 2. Monitoring and adapt automatic product generation 3. Not automatic production 4. Not automatic monitoring objective post processing automatic Product generation automatic monitoring The red data streams are not monitored by the forecasters!

31. Basis data (Observations, models, statistic values) Generation of: Texts (retrieval / text generation) pictures triggers auto + mets Product parts (Text, pictures) Products (product layout) dashboard Weather dependent loop

32. Crossing of fixed threshold value(s)? Weather dependent control by the forecaster no yes Automatic distribution Trigger to the forecaster; A check and possible adjustments. Then distribution. Extra attention when the product becomes more important

33. Product Generation Goal:

34. Precipitation forecast Zoommeer Average of 3 stations: Vlissingen, Brasschaat (of Woensdrecht) en Gilze Rijen Modified long term forecast EPS output

35. Available alternative – Amount of observed precipitation

36. Available alternative – Amount of observed precipitation in detail

37. Available alternative – precipitation forecast

38. Glider forecast

39. ..already available within KNMI…

40. ..New ideas…

41. ..more ideas…

42. Product Generation At this moment: Pilot with the use of e-forms

43. The items of this presentation Rich Intranet Application Dashboard Product Generation Application for the use of Conditional Climatology

44. Purpose: An application which makes it possible to give an quick overview of the change in, for instance the visibility, during the next 12 hours, based on climatology. This systems works for fog/low visibility/low clouds

45. Application for the use of Conditional Climatology Reason: Give the forecaster an easy access to recent climatological reference material (1977-2006) to help him/her to make the forecast.

46. Application for the use of Conditional Climatology Methode: -conditionally -Use of classes (colours => autotaf) Fog (in m)Cloud height (in ft)0-200200-500 500-800500-1000 800-15001000-1500 1500-3000

47. Application for the use of Conditional Climatology Used: -Hourly synops over the past 30 years -20 stations

48. Application for the use of Conditional Climatology