1. NOAA-MDL Seminar 7 May 2008 Bob Rabin NOAA/National Severe Storms Lab Norman. OK CIMSS University of Wisconsin-Madison Challenges in Remote Sensing to Improve Severe Weather Forecasting Current Applications

2. A Web-based tool for monitoring MCS Storm Analysis Using Multiple Data Sets. Robert Rabin, Tom Whittaker 2004 Advances in Visual Computing, G. Bebis, R. Boyle, D. Koracin, B. Parvin, Ed(s)., Springer, 571-578.

3. Identify and track MCS - Cold cloud tops - Radar reflectivity - Adjustable thresholds Time trends of MCS characteristics - Size - Cloud top temperature stats - Radar reflectivity stats - Lightning - Storm environment from RUC,... Real-time and archived data on-line Data access from NOMADS/THREDDS catalog

4. Data Flow GOES Radar RUC model analysis THREDDS Lightning Tracking Algorithm Web Server

5. Example session:

13. Mesoscale Convective Complex: Mature Stage

14. Time Series: Mature Stage

15. Mesoscale Convective Complex: Decaying Stage

16. Time Series: Decaying Stage

17. Tornadic Storm Track

18. Time Series: Tornadic Storm Track

19. Real-time and archive data: http://tracker.nssl.noaa.gov

20. Motion Estimation Uses K-Means clustering and Kalman filters Forecast dBZActual dBZ 30 min

21. Need for new approach Traditional centroid tracking  Accurate at small scales, but not at large scales  Inaccurate when storms merge or split  Possible to extract trends from the information Flow-based tracking  Cross-correlation, Lagrangian methods, etc.  Are accurate at large scales, but not at small scales  Not useful in decision support because trends of storm properties can not be extracted

22. K-Means clustering K-Means clustering is a hybrid approach  Cluster the input data to find clusters Like centroid-based tracking methods But at different scales.  Track the clusters using flow-based methods (minimization of cost-functions)‏ Like flow-based methods Does not involve cluster matching (e.g: Titan)‏

23. Example clusters Two different scales shown Both scales are tracked

24. Extrapolation Smooth the motion estimates  spatially using OBAN techniques (Gaussian kernel)‏  temporally using a Kalman filter (assuming constant velocity)‏ Repeat at different scales and choose scale appropriate to extrapolation time period.

25. Nowcasting Infrared Temperature How good is the advection technique What is the quality of cloud cover nowcasts? Effectively the quality of forecasting IR temperature < 233K Blocks represent how well persistence would do The lines indicate how well the motion estimation technique does 1,2,3-hr nowcasts shown

26. Real-time loops (WSR-88D and GOES): http://www.nssl.noaa.gov/~rabin/tracks

27. Detecting Overshooting Tops Looked for high textural variability in visible images These are the thunderstorms to be identified and forecast Shown outlined in red Detection algorithm now running in real-time at NSSL http://www.nssl.noaa.gov/users/rabin/ public_html/vis_1km/ http://www.nssl.noaa.gov/users/rabin/ public_html/vis_1km/

28. Couplets Another technique to identify thunderstorms developed by John Moses of NASA Looks for couplets of high and low temperatures Data from 2200 UTC from the same Oct. 12 case The pink tails indicate the past position of these detections As with our overshooting tops technique, persistence of detection is a problem No. 17 jumps all over the place No. 36’s direction is wrong No. 39, 40, 41 have no real history No. 37 is being tracked well

29. Real-time visible loops (comparison with radar, upper-level divergence): http://www.nssl.noaa.gov/~rabin/vis_1km

30. Mesoscale Wind Analysis from Water Vapor Imagery Detecting Winds Aloft from Water Vapour Satellite Imagery in the Vicinity of Storms Rabin, R.M., Corfidi, S.F., Brunner, J.C., Hane, C.E. Weather, 59, 251-257

31. GOES-8 Water Vapour Imagery divergence (yellow, 10 - 5 s - 1 ), and absolute vorticity (red, 10 - 5 s - 1 )‏ 2300 UTC 19 July 1995 0445 UTC 20 July 1995

32. Upper air winds at 0000 UTC on 03 June 2003 300 mb rawinsonde analysis

33. Upper air winds at 0000 UTC on 03 June 2003 from satellite black: 100-250, cyan: 251-350, yellow: 351-500 hPa

34. Surface weather map at 2300 UTC, 11 June 2003 Severe weather reports wind damage (blue) large hail (green)‏ tornadoes (red)‏

35. GOES-12 divergence at 300 hPa 11 June 2003 at 1945 UTC 12 June 2003 at 0045 UTC

36. First Guess (NOGAPS) divergence at 300 hPa 11 June 2003 at 1945 UTC 12 June 2003 at 0045 UTC

37. Surface weather map at 2300 UTC, 12 June 2003.

38. GOES-12 divergence at 300 hPa on 12 June 2003 2145 UTC derived from satellite-winds first guess model (NOGAPS)‏

39. VIS 0115 UTC WV 0215 UTC Greensburg, KS Storm 05 May 2007

40. Real-time and archive: http://www.nssl.noaa.gov/~rabin/winds http://cimss.ssec.wisc.edu/mesoscale_winds