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?
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
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
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)
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Methods for estimating movement
Linear extrapolation involves:
Estimating movement
Extrapolating based on movement
Techniques:
1.
2.
3.
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
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 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
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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Object-based methods, pros & cons
How object-based methods work:
Pros:
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:
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
How optical flow methods work
Do not identify and associate cells
Pro: Removes cell identification and
association errors
Con: No trends possible
Not affected by splits/merges
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
Often have to use global movement
Or interpolate between storms
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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
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
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
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.
2.
3.
4.
5.
6.
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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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
No storm-cell association errors
Use optical flow to estimate motion
Increased accuracy
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
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
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
The cost function takes into account
Textural similarity between pixel at x,y and the mean texture of kth cluster
Spatial contiguity of pixel to cluster
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
At the end of iteration, all pixels have
been assigned to their best clusters
Combine clusters to form larger
regions
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
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
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
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
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
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)
Composite reflectivity and CSI for forecasts > 20 dBZ
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)
1.
2.
3.
4.
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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)
1.
2.
3.
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
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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