Transcript Document

Nowcasting of thunderstorms
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National Severe Storms Laboratory &
University of Oklahoma
Seminar at City University of New York CREST
program
http://cimms.ou.edu/~lakshman/
Oct. 23, 2006
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What is nowcasting?
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Skilled short-term estimates and predictions
 Typically 0-60 minutes
 For emergency managers, transportation, etc.
 Made by meteorologists
 With guidance from automated algorithms
Guidance to forecasters involves supplying estimates & predictions for:
 Spatial location of thunderstorms
 Where is the storm now? What is the path the storm has traveled?
 Where will the storm be in 30 minutes?
 Intensity of thunderstorms
 Weakening? Strengthening?
 Potential hazards
 Hail? Lightning? Tornadoes? Flooding?
Oct. 23, 2006
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Hazard prediction
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This talk will focus on estimating and predicting:
 Spatial location of thunderstorms
 Intensity characteristics of thunderstorms
Hazard prediction is carried out by tailored algorithms
 Hail Detection Algorithm
 Looks for high radar reflectivity aloft
 Cores may descend to cause hail
 Flash flood prediction algorithm
 Estimate rainfall amount based on radar reflectivity
 Accumulate rainfall in delineated basins
 Couple with flow model (soil moisture, etc.)
 Etc.
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How to do nowcasting
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Numerical models
 Can not be done in real-time
 Skill of numerical models an area of much research
 May be the future
Rule-based prediction of growth and decay
 Identify boundaries from multiple sensors or human input
 Extrapolate echoes likely to persist or form
 Approach of “Auto Nowcaster” from NCAR
 Qualitatively: works
 Quantitatively: similar issues as numerical models
Linear extrapolation of radar echoes
 Highly skilled in the short term (under 60 minutes)
 Can be done in real-time
 Assumption is of steady-state (no growth/decay)
Oct. 23, 2006
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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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Object-based methods, pros & cons
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How object-based methods work:
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Pros:
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Identify high-intensity clump of pixels as
“cells”
Associate cells between time frames
 Closest distance/values/overlap, etc.
Small-scale prediction
Can find out history of a thunderstorm
(“trends”)
Cons:
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Splits and merges hard to keep track of
Hard to avoid association errors
Most storm cells last only about 20
minutes
Large-scale predictions are difficult to
build up
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Optical flow methods
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How optical flow methods work
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Do not identify and associate cells
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Pro: Removes cell identification and
association errors
Con: No trends possible
Not affected by splits/merges
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Take rectangular region around each
pixel of current image
Move rectangular window around
previous image
Choose movement that minimizes error
between images
Need to ensure that successive pixels
do not have very different movements
Pro: More accurate motion estimates
Con: Small-scale tracking not possible
Poor motion estimates where no storms
available in current/previous image
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Often have to use global movement
Or interpolate between storms
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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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Oct. 23, 2006
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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Technique
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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
Oct. 23, 2006
Courtesy: Yang et. al (2006)
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Why it works
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Oct. 23, 2006
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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1. Identifying storms: K-Means clustering
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Obtain a vector of measurements at each pixel
 Statistics in neighborhood of each pixel (called “texture”)
 Can also use multiple sensors or channels
Divide up vector space into K “bands”
 The bands can be equally spaced by equal-probability
 Center the clustering algorithm at each of these bands
 Assign each pixel to the band that it lies in
Perform region growing
 Pixels in same band adjacent to each other are part of region
 Compute region properties
Move pixel from one region to another if cost function lowered
 Cost function lower if pixel moves to region whose mean texture it is closer to
 Cost function lower if pixel moves to region that it is closer (spatially) to
Iterate until stable
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The cost function
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The cost function takes into account
 Textural similarity between pixel at x,y and the mean texture of kth cluster
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Spatial contiguity of pixel to cluster
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Weighted appropriately (lambda=0.2 seems to work well)
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Clustering: example
Radar reflectivity
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K=4 clustering
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2. Hierarchical clustering
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At the end of iteration, all pixels have
been assigned to their best clusters
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Combine clusters to form larger
regions
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Most detailed scale of
segmentation
Scale=0
Clusters are typically very small
Find mean inter-cluster distance
Combine regions which are
spatially adjacent whose textural
means are close to each other
Reflectivity
Scale=0
Repeat to get largest regions
Scale=1
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Scale=2
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3. Compute motion estimates
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Starting with scale=2, project the current cluster backward
 Move the cluster around within the previous image
 Choose the movement that minimizes mean absolute error
 Minimization based on kernel estimate, to reduce outlier errors
A motion estimate obtained for each cluster
 Less noisy than pixel-based estimates
 Automatic smoothing over region of cluster
 Scale=0 is the noisiest (fewer pixels)
What about newly developing cells?
 Limit the search space to maximum expected storm movement
 If mean absolute error is too large, assume that cell is new
 Will take movement based on neighboring cells
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4. Spatially interpolate motion vectors
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Need motion estimate between regions
 Spatially interpolate between regions
 Weighted by distance from region (Cressman weights)
 Weighted by size of region
 Fill out spatial grid
Can use background wind field to fill out domain
 Constant weight for background wind field (from model)
 Use scale=2 motion estimate as background field for scale=1
Repeat process to get motion vector for scale=2
 Use scale=1 motion estimate as background field for scale=0
 Repeat process to get motion vector for scale=1
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5. Kalman filter
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Motion estimates are smoothed in time
 Each pixel runs a Kalman filter (constant acceleration model)
 Smoothes the motion estimates
Courtesy: Yang et. al (2006)
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6. Use motion estimate to do forecast
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Forward
 Using motion estimate at a pixel, project the point to where it should be
 Create a spatial Gaussian distribution of the point’s value at that location
Interpolation
 For fast moving storms, it is possible that there will be gaps in the output field
 Interpolate between projected points
Use different scales for different time periods, for example:
 Use scale=0 for forecasting less than 15 minutes
 Use scale=1 for forecasting 15-45 minutes
 Use scale=2 for forecasting longer than 45 minutes
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7. Trends
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What about trends?
 Compute properties of current cluster
 Min, max, mean, count, histogram, etc.
 Project cluster backwards onto previous sets of images
 Can use fields other than the field being tracked
 Compute properties of projected cluster
 Use to diagnose trends
Not used operationally yet
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Example: hurricane (Sep. 18, 2003)
Image
Eastward
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Scale=1
s.ward
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Satellite water vapor (Feb. 28, 2003)
Image
30-min forecast
60-min forecast
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Typhoon Nari (Taiwan, Sep. 16, 2001)
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Composite reflectivity and CSI for forecasts > 20 dBZ
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Large-scale (temporally and spatially)
Courtesy: Yang et. al (2006)
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Tornado case (Apr. 20, 1995)
Complete life-cycle of a storm: CSI at different scales and time periods
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Tornado case (May 8, 2003)
Courtesy: Yang et. al (2006)
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Comparison with other techniques (dBZ)
KTLX, May 3 1999
Bias
MAE
CSI
Forecasting reflectivity through
different techniques (30min)
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Persistence
TREC (xcorr)
Same wind-field for all storms
Hierarchical K-Means + Kalman
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Comparison with other techniques (VIL)
KTLX, May 3 1999
Bias
MAE
CSI
Forecasting VIL through different
techniques (30 min)
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4.
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Persistence
TREC (xcorr)
Same wind-field for all storms
Hierarchical K-Means + Kalman
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Forecast loop of VIL (May 3, 1999)
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References
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Technique described in this paper:
 Lakshmanan, V., R. Rabin, and V. DeBrunner, 2003: Multiscale storm
identification and forecast. J. Atm. Res., 67-68, 367-380
 http://cimms.ou.edu/~lakshman/Papers/kmeans_motion.pdf
Some of the results shown here are from:
 Yang, H., J. Zhang, C. Langston, S. Wang (2006): Synchronization of Multiple
Radar Observations in 3-D Radar Mosaic, 12th Conf. on Aviation, Range and
Aerospace Meteo. Atlanta, GA, P1.10
 http://ams.confex.com/ams/pdfpapers/104386.pdf
Software implementation
 w2segmotion is one of the algorithms that is part of WDSS-II
 Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2006 (In Press): The
warning decision support system - integrated information (WDSS-II). Weather
and Forecasting.
 http://www.wdssii.org/
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