Nowcasting of thunderstorms from GOES Infrared and Visible Imagery

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

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

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

4. 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 Valliappa.Lakshmanan@noaa.gov

5. 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 Valliappa.Lakshmanan@noaa.gov

6. 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? Valliappa.Lakshmanan@noaa.gov

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

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

9. 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 Valliappa.Lakshmanan@noaa.gov

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

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

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

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

14. 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 Valliappa.Lakshmanan@noaa.gov

15. 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! Valliappa.Lakshmanan@noaa.gov

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

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

18. 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 Valliappa.Lakshmanan@noaa.gov

19. 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! Valliappa.Lakshmanan@noaa.gov

20. 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 Valliappa.Lakshmanan@noaa.gov

21. 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 Valliappa.Lakshmanan@noaa.gov