nowcasting of thunderstorms from goes infrared and visible imagery
DESCRIPTION
Nowcasting of thunderstorms from GOES Infrared and Visible Imagery. [email protected] [email protected] National Severe Storms Laboratory & University of Oklahoma http://cimms.ou.edu/~lakshman/. Nowcasting Thunderstorms From Infrared and Visible Imagery. - PowerPoint PPT PresentationTRANSCRIPT
[email protected]@noaa.govNational Severe Storms Laboratory & University of Oklahomahttp://cimms.ou.edu/~lakshman/
Nowcasting of thunderstorms from GOES Infrared and Visible Imagery
Nowcasting Thunderstorms From Infrared and Visible Imagery
Tracking Storms: Existing Techniques
Overview of Method
Identifying Storms at Multiple Scales
Motion Estimation and Forecast
Methods for estimating movement
Linear extrapolation involves: Estimating movement Extrapolating based on movement
Techniques:1. Object identification and tracking
Find cells and track them2. 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
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
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 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?
Nowcasting Thunderstorms From Infrared and Visible Imagery
Tracking Storms: Existing Techniques
Overview of Method
Identifying Storms at Multiple Scales
Motion Estimation and Forecast
Technique: Stages
Clustering, tracking, interpolation in space (Barnes) and time (Kalman)
Courtesy: Yang et. al (2006)
Technique: Details
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)
Why it works
Hierarchical clustering sidesteps problems inherent in object-identification and optical-flow based methods
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
Nowcasting Thunderstorms From Infrared and Visible Imagery
Tracking Storms: Existing Techniques
Overview of Method
Identifying Storms at Multiple Scales
Motion Estimation and Forecast
K-Means Clustering
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
Satellite Data
Technique developed for radar modified for satellite
Funding from NASA and GOES-R programs Data from Oct. 12, 2001 over Texas
Visible IR Band 2
Because technique expects higher values to be more significant, the IR temperatures were transformed as:
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
C = 273 - IRTemperature
Segmentation of infrared imagery
Coarsest scale was used because 1-3 hr forecasts desired.
Not just a simple thresholding scheme
Nowcasting Thunderstorms From Infrared and Visible Imagery
Tracking Storms: Existing Techniques
Overview of Method
Identifying Storms at Multiple Scales
Motion Estimation and Forecast
Motion Estimation
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
Processing
IR to CloudCover
Clustering, Motion
estimation
Motion estimateapplied to
IR and Visible
Forecast Method
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.
Forecast Example (Visible, +1hr, +2hr, +3hr)
Varying intensity levels are a problem
Conclusions
Advection forecast beats persistence when storms are organized Does poorly when storms are evolving
IR forecasts are skilful Visible channel forecasts are not