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[email protected] Severe Storms Laboratory & University of Oklahomahttp://cimms.ou.edu/~lakshman/
Nowcasting of thunderstorms from GOES Infrared and Visible Imagery
Nowcasting Thunderstorms From Infrared and Visible Imagery
KMeans Technique
Detection Technique
Results
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
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
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
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?
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)
Why it works
Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods
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
Technique: Stages
Clustering, tracking, interpolation in space (Barnes) and time (Kalman)
Courtesy: Yang et. al (2006)
Typhoon Nari (Taiwan, Sep. 16, 2001)
Composite reflectivity and CSI for forecasts > 20 dBZ Large-scale (temporally and spatially)
Courtesy: Yang et. al (2006)
Nowcasting Thunderstorms From Infrared and Visible Imagery
KMeans Technique
Detection Technique
Results
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
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!
Processing
IR to CloudCover
Clustering, Motion
estimation
Motion estimateapplied to
overshooting tops
Nowcasting Thunderstorms From Infrared and Visible Imagery
KMeans Technique
Detection Technique
Results
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
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!
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
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
