Valliappa.Lakshmanan@noaa.gov 1

Valliappa.Lakshmanan@noaa.gov

Bob.Rabin@noaa.govNational Severe Storms Laboratory & University of Oklahomahttp://cimms.ou.edu/~lakshman/

Nowcasting of thunderstorms from GOES Infrared and Visible Imagery

Valliappa.Lakshmanan@noaa.gov 2

Nowcasting Thunderstorms From Infrared and Visible Imagery

KMeans Technique

Detection Technique

Results

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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

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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

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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?

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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)

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Why it works

Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods

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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

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Technique: Stages

Clustering, tracking, interpolation in space (Barnes) and time (Kalman)

Courtesy: Yang et. al (2006)

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Example: hurricane (Sep. 18, 2003)

Image Scale=1

Eastward s.ward

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Typhoon Nari (Taiwan, Sep. 16, 2001)

Composite reflectivity and CSI for forecasts > 20 dBZ Large-scale (temporally and spatially)

Courtesy: Yang et. al (2006)

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Nowcasting Thunderstorms From Infrared and Visible Imagery

KMeans Technique

Detection Technique

Results

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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

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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!

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Processing

IR to CloudCover

Clustering, Motion

estimation

Motion estimateapplied to

overshooting tops

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Nowcasting Thunderstorms From Infrared and Visible Imagery

KMeans Technique

Detection Technique

Results

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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!

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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

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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