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

Tracking Storms: Existing Techniques

Overview of Method

Identifying Storms at Multiple Scales

Motion Estimation and Forecast

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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

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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

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 8

Technique: Stages

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

Courtesy: Yang et. al (2006)

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Technique: Details

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

Valliappa.Lakshmanan@noaa.gov 12

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 13

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

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Example: hurricane on radar (Sep. 18, 2003)

Image Scale=1

Eastward s.ward

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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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Segmentation of infrared imagery

Coarsest scale was used because 1-3 hr forecasts desired.

Not just a simple thresholding scheme

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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 18

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

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Processing

IR to CloudCover

Clustering, Motion

estimation

Motion estimateapplied to

IR and Visible

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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.

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Forecast Example (IR, +1hr, +2hr, +3hr)

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Forecast Example (Visible, +1hr, +2hr, +3hr)

Varying intensity levels are a problem

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Skill compared to persistence

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Conclusions

Advection forecast beats persistence when storms are organized Does poorly when storms are evolving

IR forecasts are skilful Visible channel forecasts are not