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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 1 Nowcasting Thunderstorms From Infrared and Visible Imagery Tracking Storms: Existing Techniques Overview of Method Identifying Storms at Multiple Scales Motion Estimation and Forecast Valliappa.Lakshmanan@noaa.gov 2 Methods for estimating movement Linear extrapolation involves: Estimating movement Extrapolating based on movement Techniques: 1. 2. 3. 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 3 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 WSR88D 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 4 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 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 Tuttle, J., and R. Gall, 1999: A single-radar technique for estimating the winds in tropical cyclones. Bull. Amer. Meteor. Soc., 80, 653-668 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 5 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 6 Nowcasting Thunderstorms From Infrared and Visible Imagery Tracking Storms: Existing Techniques Overview of Method Identifying Storms at Multiple Scales Motion Estimation and Forecast Valliappa.Lakshmanan@noaa.gov 7 Technique: Stages Clustering, tracking, interpolation in space (Barnes) and time (Kalman) Courtesy: Yang et. al (2006) Valliappa.Lakshmanan@noaa.gov 8 Technique: Details 1. 2. 3. 4. 5. 6. 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 9 Why it works Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods Valliappa.Lakshmanan@noaa.gov 10 Advantages of technique Identify storms at multiple scales No storm-cell association errors Use optical flow to estimate motion Increased accuracy Hierarchical texture segmentation using K-Means clustering Yields nested partitions (storm cells inside squall lines) 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 11 Nowcasting Thunderstorms From Infrared and Visible Imagery Tracking Storms: Existing Techniques Overview of Method Identifying Storms at Multiple Scales Motion Estimation and Forecast Valliappa.Lakshmanan@noaa.gov 12 K-Means Clustering Contiguity-enhanced K-Means clustering Takes pixel value, texture and spatial proximity into account A vector segmentation problem Hierarchical segmentation Relax intercluster distances Prune regions based on size Valliappa.Lakshmanan@noaa.gov 13 Example: hurricane on radar (Sep. 18, 2003) Image Eastward Valliappa.Lakshmanan@noaa.gov Scale=1 s.ward 14 Satellite Data Technique developed for radar modified for satellite Data from Oct. 12, 2001 over Texas Funding from NASA and GOES-R programs Visible IR Band 2 Because technique expects higher values to be more significant, the IR temperatures were transformed as: C = 273 - IRTemperature 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 Valliappa.Lakshmanan@noaa.gov 15 Segmentation of infrared imagery Not just a simple thresholding scheme Coarsest scale was used because 1-3 hr forecasts desired. Valliappa.Lakshmanan@noaa.gov 16 Nowcasting Thunderstorms From Infrared and Visible Imagery Tracking Storms: Existing Techniques Overview of Method Identifying Storms at Multiple Scales Motion Estimation and Forecast Valliappa.Lakshmanan@noaa.gov 17 Motion Estimation Use identified storms in current image as template Move template around earlier image and find best match Match is where the absolute error of difference is minimized Not root mean square error: MAE is more noise-tolerant Minimize field by weighting pixel on difference from absolute minimum Find centroid of this minimum “region” Interpolate motion vectors between storms Valliappa.Lakshmanan@noaa.gov 18 Processing IR to CloudCover Motion estimate applied to IR and Visible Valliappa.Lakshmanan@noaa.gov 19 Forecast Method The forecast is done in three steps: Forward: project data forward in time to a spatial location given by the motion estimate at their current location and the elapsed time. Define a background (global) motion estimate given by the mean storm motion. Reverse: obtain data at a spatial point in the future based on the current wind direction at that spot and current spatial distribution of data. Valliappa.Lakshmanan@noaa.gov 20 Forecast Example (IR, +1hr, +2hr, +3hr) Valliappa.Lakshmanan@noaa.gov 21 Forecast Example (Visible, +1hr, +2hr, +3hr) Varying intensity levels are a problem Valliappa.Lakshmanan@noaa.gov 22 Skill compared to persistence Valliappa.Lakshmanan@noaa.gov 23 Conclusions Advection forecast beats persistence when storms are organized Does poorly when storms are evolving IR forecasts are skilful Visible channel forecasts are not Valliappa.Lakshmanan@noaa.gov 24