1. Unique Quality Control Issues Derek S. Arndt Oklahoma Climatological Survey June 25, 2002

2. OK Mesonet Automated QA Range Test Does the datum lie within a prescribed min/max? Step Test Does the datum exhibit a seemingly unrealistic jump in a time step? Persistence Test Does a time series exhibit unrealistically small variability?

3. OK Mesonet Automated QA Spatial Test Does an observation conform within a maximum tolerable deviation to that of neighboring stations? Like-instrument Test Does an observation significantly deviate from a similar instrument elsewhere on the tower?

4. Automated QA Flags Decision-making algorithm compiles results of automated QA tests Final flag (“good”, “suspect”, “warning” or “bad”) determined by logic The final flag is never final! The Qualparm table represents the QA Meteorologist’s latest and best assessment of observational quality for the given time Automated report arrives in the QA Meteorologist’s inbox each morning.

5. Capturing Real Events With Automated QA Automated QA software is invaluable as a front-line detector However, nothing beats the trained eye and brain of a QA meteorologist Sometimes, unique meteorological events fail QA tests and good data is suggested to be “bad”

6. Goodparms Goodparms reflect observations that are known to be good, but have failed an automated QA test In the next generation of OK Mesonet QA structure, Goodparm entries will override automated QA tests that have been “fooled” by Mother Nature

7. Noteworthy Goodparm Events The following examples of real meteorological phenomena that were initially “flagged” by automated QA processes Thanks to automated reporting, and a vigilant QA Meteorologist, these “bad” events turned out to be some of the network’s gems!

8. Inversion Poking Extreme spatial anomalies occur Air temperature Dew point temperature / relative humidity Wind direction Wind speed Associated with shallow inversion and stable surface layer

9. Inversion Poking Norman, OK Sounding 25 Oct 2001 1200 UTC

10. Inversion Poking OK Mesonet Sfc Plot 25 Oct 2001 1330 UTC

11. Inversion Poking North Central Oklahoma Elevation (meters)

12. Inversion Poking 26 Oct 1999 0600 UTC

13. Cold Air Pooling Extreme temperature anomalies develop due to radiational cooling Events tend to occur within a few hours of sunset, suggesting in situ cooling (versus cold air drainage) Radiationally cooled air is prevented from mixing

14. Cold Air Pooling 26 Oct 1999 0600 UTC

15. Cold Air Pooling East Central Oklahoma Elevation (meters)

16. Cold Air Pooling 4 Nov 1999 1215 UTC

17. Mesohighs Typically occur concurrently with convective line or cluster May trigger spatially-oriented QA tests May be a traveling QA phenomenon

18. Mesohighs 1 Jun 1999 0235 UTC

19. Mesohighs 1 Jun 1999 0235 UTC

20. Mesohighs 1 Jun 1999 0236 UTC

21. Mesolows Typically located off the trailing edge of convective precipitation May be a traveling QA phenomenon

22. Mesolows 25 May 2000 0700 UTC

23. Mesolows 25 May 2000 0730 UTC

24. Mesolows 25 May 2000 0800 UTC

25. Mesolows 25 May 2000 0659 UTC

26. Mesolows 25 May 2000 0730 UTC

27. Mesolows 25 May 2000 0801 UTC

28. Heatbursts Originate from air that has subsided dry-adiabatically from mid-levels of outwardly-innocuous thunderstorms Downdrafts sometimes cause damaging winds and substantial temperature rises Heatbursts occur much more often in Oklahoma than previously thought

29. Heatbursts 20 Sep 1998 1049 UTC

30. Heatbursts 20 Sep 1998 1148 UTC

31. Heatbursts 20 Sep 1998 1248 UTC

32. Heatbursts Air Temperature 20 Sep 1998 1145 UTC

33. Heatbursts Wind Gusts 20 Sep 1998 1145 UTC

34. Heatbursts Mesonet Meteogram 20-21 Sep 1998

35. Winter Precipitation Automated QA is quite good at detecting wind sensors that accumulate ice and barometers that become sealed from the atmosphere (P ~ T at constant V!) Unique conditions that accompany snowfall can sometimes lead to erroneous flags

36. Winter Precipitation Visible Imagery 7 Dec 1999

37. Winter Precipitation Air Temperature 7 Dec 1999 1000 UTC

38. Land-Atmosphere-Vegetation Soil temperatures are sensitive to the characteristics of the soil and vegetation above them Vegetation of surrounding land areas can impact temperature and dew point observations at a site

39. Land-Atmosphere-Vegetation

41. Sloshing and Wave Events

42. That’s It! Here’s to “bad” data! Best of luck to you and your network! Derek Arndt Oklahoma Climatological Survey darndt@ou.edu