1. Valliappa.Lakshmanan@noaa.gov 1 Bob.Rabin@noaa.gov National Severe Storms Laboratory & University of Oklahoma http://cimms.ou.edu/~lakshman/ Nowcasting of thunderstorms from GOES Infrared and Visible Imagery

2. Valliappa.Lakshmanan@noaa.gov2 Nowcasting Thunderstorms From Infrared and Visible Imagery KMeans Technique Detection Technique Results

3. Valliappa.Lakshmanan@noaa.gov3 Methods for estimating movement Linear extrapolation involves: Estimating movement Extrapolating based on movement Techniques: 1. Object identification and tracking Find cells and track them 2. Optical flow techniques Find optimal motion between rectangular subgrids at different times 3. Hybrid technique Find cells and find optimal motion between cell and previous image

4. Valliappa.Lakshmanan@noaa.gov4 Some object-based methods Storm cell identification and tracking (SCIT) Developed at NSSL, now operational on NEXRAD Allows trends of thunderstorm properties Johnson J. T., P. L. MacKeen, A. Witt, E. D. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The Storm Cell Identification and Tracking Algorithm: An enhanced WSR- 88D algorithm. Weather & Forecasting, 13, 263–276. Multi-radar version part of WDSS-II Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) Developed at NCAR, part of Autonowcaster Dixon M. J., and G. Weiner, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797 Optimization procedure to associate cells from successive time periods Satellite-based MCS-tracking methods Association is based on overlap between MCS at different times Morel C. and S. Senesi, 2002: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Q. J. Royal Meteo. Soc., 128, 1953-1971 http://www.ssec.wisc.edu/~rabin/hpcc/storm_tracker.html MCSs are large, so overlap-based methods work well

5. Valliappa.Lakshmanan@noaa.gov5 Some optical flow methods TREC Minimize mean square error within subgrids between images No global motion vector, so can be used in hurricane tracking Results in a very chaotic wind field in other situations Tuttle, J., and R. Gall, 1999: A single-radar technique for estimating the winds in tropical cyclones. Bull. Amer. Meteor. Soc., 80, 653-668 Large-scale “growth and decay” tracker MIT/Lincoln Lab, used in airport weather tracking Smooth the images with large elliptical filter, limit deviation from global vector Not usable at small scales or for hurricanes Wolfson, M. M., Forman, B. E., Hallowell, R. G., and M. P. Moore (1999): The Growth and Decay Storm Tracker, 8th Conference on Aviation, Range, and Aerospace Meteorology, Dallas, TX, p58-62 McGill Algorithm of Precipitation by Lagrangian Extrapolation (MAPLE) Variational optimization instead of a global motion vector Tracking for large scales only, but permits hurricanes and smooth fields Germann, U. and I. Zawadski, 2002: Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of methodology. Mon. Wea. Rev., 130, 2859-2873

6. Valliappa.Lakshmanan@noaa.gov6 Need for hybrid technique Need an algorithm that is capable of Tracking multiple scales: from storm cells to squall lines Storm cells possible with SCIT (object-identification method) Squall lines possible with LL tracker (elliptical filters + optical flow) Providing trend information Surveys indicate: most useful guidance information provided by SCIT Estimating movement accurately Like MAPLE How?

7. Valliappa.Lakshmanan@noaa.gov7 Technique 1. Identify storm cells based on reflectivity and its “texture” 2. Merge storm cells into larger scale entities 3. Estimate storm motion for each entity by comparing the entity with the previous image’s pixels 4. Interpolate spatially between the entities 5. Smooth motion estimates in time 6. Use motion vectors to make forecasts Courtesy: Yang et. al (2006)

8. Valliappa.Lakshmanan@noaa.gov8 Why it works Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods

9. Valliappa.Lakshmanan@noaa.gov9 Advantages of technique Identify storms at multiple scales Hierarchical texture segmentation using K-Means clustering Yields nested partitions (storm cells inside squall lines) No storm-cell association errors Use optical flow to estimate motion Increased accuracy Instead of rectangular sub-grids, minimize error within storm cell Single movement for each cell Chaotic windfields avoided No global vector Cressman interpolation between cells to fill out areas spatially Kalman filter at each pixel to smooth out estimates temporally

10. Valliappa.Lakshmanan@noaa.gov10 Technique: Stages Clustering, tracking, interpolation in space (Barnes) and time (Kalman) Courtesy: Yang et. al (2006)

11. Valliappa.Lakshmanan@noaa.gov11 Example: hurricane (Sep. 18, 2003) Image Scale=1 Eastwards.ward

12. Valliappa.Lakshmanan@noaa.gov12 Typhoon Nari (Taiwan, Sep. 16, 2001) Composite reflectivity and CSI for forecasts > 20 dBZ Large-scale (temporally and spatially) Courtesy: Yang et. al (2006)

13. Valliappa.Lakshmanan@noaa.gov13 Nowcasting Thunderstorms From Infrared and Visible Imagery KMeans Technique Detection Technique Results

14. Valliappa.Lakshmanan@noaa.gov14 Satellite Data Technique developed for radar modified for satellite Funding from NASA and GOES-R programs Data from Oct. 12, 2001 over Texas Visible IR Band 2 Because technique expects higher values to be more significant, the IR temperatures were transformed as: Termed “CloudCover” Would have been better to use ground temperature instead of 273K Values above 40 were assumed to be convective complexes worth tracking Effectively cloud top temperatures below 233K C = 273 - IRTemperature

15. Valliappa.Lakshmanan@noaa.gov15 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 Bob, provide website URL here!

16. Valliappa.Lakshmanan@noaa.gov16 Processing IR to CloudCover Clustering, Motion estimation Motion estimate applied to overshooting tops

17. Valliappa.Lakshmanan@noaa.gov17 Nowcasting Thunderstorms From Infrared and Visible Imagery KMeans Technique Detection Technique Results

18. Valliappa.Lakshmanan@noaa.gov18 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

19. Valliappa.Lakshmanan@noaa.gov19 Nowcasting Overshooting Tops The detected overshooting tops are not persistent Need to examine whether it’s because the tops do move around a lot Or whether the detection technique is not robust with respect to position For example, the IR temperature nowcast towards end of sequence was CSI=0.6 But overshooting tops nowcast has CSI around 0.05!

20. Valliappa.Lakshmanan@noaa.gov20 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

21. Valliappa.Lakshmanan@noaa.gov21 Couplets vs. Overshooting Tops Fewer detections with the overshooting tops technique than with the couplets one Perhaps the overshooting tops technique’s thresholds are too stringent Both techniques need to be improved Identification mechanism not robust across time 7 couplets 1 overshooting top