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Nowcasting of thunderstorms. [email protected] National Severe Storms Laboratory & University of Oklahoma Seminar at City University of New York CREST program http://cimms.ou.edu/~lakshman/. What is nowcasting?. Skilled short-term estimates and predictions Typically 0-60 minutes - PowerPoint PPT Presentation
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Oct. 23, 2006 [email protected] 1
[email protected] Severe Storms Laboratory & University of OklahomaSeminar at City University of New York CREST programhttp://cimms.ou.edu/~lakshman/
Nowcasting of thunderstorms
Oct. 23, 2006 [email protected] 2
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 [email protected] 3
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.
Oct. 23, 2006 [email protected] 4
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)
Oct. 23, 2006 [email protected] 5
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
Oct. 23, 2006 [email protected] 6
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
Oct. 23, 2006 [email protected] 7
Object-based methods, pros & cons
How object-based methods work: Identify high-intensity clump of pixels as
“cells” Associate cells between time frames
Closest distance/values/overlap, etc. Pros:
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
Oct. 23, 2006 [email protected] 8
Optical flow methods
How optical flow methods work 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 Do not identify and associate cells
Pro: Removes cell identification and association errors
Con: No trends possible Not affected by splits/merges
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
Oct. 23, 2006 [email protected] 9
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
Oct. 23, 2006 [email protected] 10
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?
Oct. 23, 2006 [email protected] 11
Technique
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)
Oct. 23, 2006 [email protected] 12
Why it works
Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods
Oct. 23, 2006 [email protected] 13
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
Oct. 23, 2006 [email protected] 14
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
Oct. 23, 2006 [email protected] 15
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)
Oct. 23, 2006 [email protected] 17
2. Hierarchical clustering
At the end of iteration, all pixels have been assigned to their best clusters
Most detailed scale of segmentation
Scale=0 Clusters are typically very small
Combine clusters to form larger regions
Find mean inter-cluster distance Combine regions which are
spatially adjacent whose textural means are close to each other
Repeat to get largest regions
Reflectivity Scale=0
Scale=1 Scale=2
Oct. 23, 2006 [email protected] 18
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
Oct. 23, 2006 [email protected] 19
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
Oct. 23, 2006 [email protected] 20
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)
Oct. 23, 2006 [email protected] 21
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
Oct. 23, 2006 [email protected] 22
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
Oct. 23, 2006 [email protected] 24
Satellite water vapor (Feb. 28, 2003)
Image 30-min forecast
60-min forecast
Oct. 23, 2006 [email protected] 25
Typhoon Nari (Taiwan, Sep. 16, 2001)
Composite reflectivity and CSI for forecasts > 20 dBZ Large-scale (temporally and spatially)
Courtesy: Yang et. al (2006)
Oct. 23, 2006 [email protected] 26
Tornado case (Apr. 20, 1995)
Complete life-cycle of a storm: CSI at different scales and time periods
Oct. 23, 2006 [email protected] 28
Comparison with other techniques (dBZ)KTLX, May 3 1999
Bias CSI
MAEForecasting reflectivity through
different techniques (30min)
1. Persistence2. TREC (xcorr)3. Same wind-field for all storms4. Hierarchical K-Means + Kalman
Oct. 23, 2006 [email protected] 29
Comparison with other techniques (VIL) KTLX, May 3 1999
Bias CSI
MAEForecasting VIL through different
techniques (30 min)
1. Persistence2. TREC (xcorr)3. Same wind-field for all storms4. Hierarchical K-Means + Kalman
Oct. 23, 2006 [email protected] 30
Forecast loop of VIL (May 3, 1999)
Oct. 23, 2006 [email protected] 31
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/