[e-mail]

1
Nowcasting of thunderstorms from GOES Infrared
and Visible Imagery

• Valliappa.Lakshmanan_at_noaa.gov
• Bob.Rabin_at_noaa.gov
• National Severe Storms Laboratory University of
  Oklahoma
• http//cimms.ou.edu/lakshman/

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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

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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

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, 263276.
• 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 NowcastingA radar-based methodology. J.
  Atmos. Oceanic Technol., 10, 785797
• 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-identificat
  ion 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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Nowcasting Thunderstorms From Infrared and
Visible Imagery

• Tracking Storms Existing Techniques
• Overview of Method
• Identifying Storms at Multiple Scales
• Motion Estimation and Forecast

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 images
   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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Nowcasting Thunderstorms From Infrared and
Visible Imagery

• Tracking Storms Existing Techniques
• Overview of Method
• Identifying Storms at Multiple Scales
• Motion Estimation and Forecast

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
Scale1
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
Not just a simple thresholding scheme
Coarsest scale was used because 1-3 hr forecasts
desired.
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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

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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
Clustering, Motion estimation
IR to CloudCover
Motion estimate applied 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