Transcript Document

Nowcasting of thunderstorms from
GOES Infrared and Visible Imagery
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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
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Linear extrapolation involves:
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Estimating movement
Extrapolating based on movement
Techniques:
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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
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Some object-based methods
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Storm cell identification and tracking (SCIT)
 Developed at NSSL, now operational on NEXRAD
 Allows trends of thunderstorm properties
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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
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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
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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
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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
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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
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Not usable at small scales or for hurricanes
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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
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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
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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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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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Technique: Stages
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Clustering, tracking, interpolation in space (Barnes) and time (Kalman)
Courtesy: Yang et. al (2006)
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Technique: Details
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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)
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Why it works
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Hierarchical clustering
sidesteps problems inherent
in object-identification and
optical-flow based methods
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Advantages of technique
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Identify storms at multiple scales
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No storm-cell association errors
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Use optical flow to estimate motion
Increased accuracy
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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
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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
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K-Means Clustering
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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
Eastward
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Scale=1
s.ward
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Satellite Data
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Technique developed for radar modified for
satellite
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Data from Oct. 12, 2001 over Texas
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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
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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
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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
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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
Motion estimate
applied to
IR and Visible
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Forecast Method
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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
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Advection forecast beats persistence when storms are organized
 Does poorly when storms are evolving
IR forecasts are skilful
 Visible channel forecasts are not
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