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Nowcasting of thunderstorms. National Severe Storms Laboratory & University of Oklahoma Seminar at City University of New York CREST program What is nowcasting?. Skilled short-term estimates and predictions Typically 0-60 minutes

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nowcasting of thunderstorms

Nowcasting of thunderstorms

National Severe Storms Laboratory & University of Oklahoma

Seminar at City University of New York CREST program

what is nowcasting
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?

hazard prediction
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.

how to do nowcasting
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)

methods for estimating movement
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

some object based methods
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
    • MCSs are large, so overlap-based methods work well

object based methods pros cons
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

optical flow methods
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

some optical flow methods
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

need for hybrid technique
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?

  • 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)

why it works
Why it works
  • Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods

advantages of technique
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

1 identifying storms k means clustering
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

the cost function
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)

clustering example
Radar reflectivity K=4 clusteringClustering: example

2 hierarchical clustering
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





3 compute motion estimates
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

4 spatially interpolate motion vectors
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

5 kalman filter
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)

6 use motion estimate to do forecast
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

7 trends
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

example hurricane sep 18 2003
Example: hurricane (Sep. 18, 2003)





satellite water vapor feb 28 2003
Satellite water vapor (Feb. 28, 2003)


30-min forecast

60-min forecast

typhoon nari taiwan sep 16 2001
Typhoon Nari (Taiwan, Sep. 16, 2001)
  • Composite reflectivity and CSI for forecasts > 20 dBZ
    • Large-scale (temporally and spatially)

Courtesy: Yang et. al (2006)

tornado case may 8 2003
Tornado case (May 8, 2003)

Courtesy: Yang et. al (2006)

comparison with other techniques dbz ktlx may 3 1999
Comparison with other techniques (dBZ)KTLX, May 3 1999



  • Forecasting reflectivity through different techniques (30min)
  • Persistence
  • TREC (xcorr)
  • Same wind-field for all storms
  • Hierarchical K-Means + Kalman


comparison with other techniques vil ktlx may 3 1999
Comparison with other techniques (VIL) KTLX, May 3 1999



  • Forecasting VIL through different techniques (30 min)
  • Persistence
  • TREC (xcorr)
  • Same wind-field for all storms
  • Hierarchical K-Means + Kalman


forecast loop of vil may 3 1999
Forecast loop of VIL (May 3, 1999)

  • 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
  • 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
  • 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.