noaa-mdl seminar 7 may 2008 bob rabin noaa/national severe storms lab norman. ok cimss university of...
TRANSCRIPT
NOAA-MDL Seminar7 May 2008
Bob RabinNOAA/National Severe Storms Lab
Norman. OK
CIMSS University of Wisconsin-Madison
Challenges in Remote Sensing to Improve Severe Weather Forecasting
Current Applications
A Web-based tool for monitoring MCSStorm Analysis Using Multiple Data Sets.
Robert Rabin, Tom Whittaker 2004
Advances in Visual Computing,
G. Bebis, R. Boyle, D. Koracin, B. Parvin, Ed(s)., Springer, 571-578.
Identify and track MCS
- Cold cloud tops
- Radar reflectivity
- Adjustable thresholdsTime trends of MCS characteristics
- Size
- Cloud top temperature stats
- Radar reflectivity stats
- Lightning
- Storm environment from RUC,...Real-time and archived data on-lineData access from NOMADS/THREDDS catalog
Data Flow
GOES
Radar
RUC model analysis
THREDDS
LightningTrackingAlgorithm Web Server
Example session:
Mesoscale Convective Complex: Mature Stage
Time Series: Mature Stage
Mesoscale Convective Complex: Decaying Stage
Time Series: Decaying Stage
Tornadic Storm Track
Time Series: Tornadic Storm Track
Real-time and archive data:
http://tracker.nssl.noaa.gov
Motion Estimation
Uses K-Means clustering and Kalman filters
Forecast dBZActual dBZ
30 min30 min
Need for new approach
Traditional centroid tracking Accurate at small scales, but not at large scales Inaccurate when storms merge or split Possible to extract trends from the information
Flow-based tracking Cross-correlation, Lagrangian methods, etc. Are accurate at large scales, but not at small scales Not useful in decision support because trends of storm
properties can not be extracted
K-Means clustering
K-Means clustering is a hybrid approach Cluster the input data to find clusters
Like centroid-based tracking methodsBut at different scales.
Track the clusters using flow-based methods (minimization of cost-functions)Like flow-based methodsDoes not involve cluster matching (e.g: Titan)
Example clusters
Two different scales shown
Both scales are
tracked
Extrapolation
Smooth the motion estimates spatially using OBAN
techniques (Gaussian kernel)
temporally using a Kalman filter (assuming constant velocity)
Repeat at different scales and choose scale appropriate to extrapolation time period.
Nowcasting Infrared Temperature
How good is the advection
technique
What is the quality of cloud cover nowcasts?
Effectively the quality of forecasting IR temperature < 233K
Blocks represent how well
persistence would do
The lines indicate how well the motion estimation technique does
1,2,3-hr nowcasts shown
Real-time loops (WSR-88D and GOES):
http://www.nssl.noaa.gov/~rabin/tracks
Detecting Overshooting Tops
Looked for high textural variability in visible
images
These are the thunderstorms to be identified and forecast
Shown outlined in red
Detection algorithm now running in real-time
at NSSL
http://www.nssl.noaa.gov/users/rabin/public_html/vis_1km/
Couplets
Another technique to identify thunderstorms developed by John Moses of NASA Looks for couplets of high
and low temperatures Data from 2200 UTC from the same
Oct. 12 case The pink tails indicate the
past position of these detections
As with our overshooting tops technique, persistence of detection is a problem
No. 17 jumps all over the place
No. 36’s direction is wrong
No. 39, 40, 41 have no real history
No. 37 is being tracked well
Real-time visible loops (comparison with
radar, upper-level divergence):
http://www.nssl.noaa.gov/~rabin/vis_1km
Mesoscale Wind Analysis from Water Vapor Imagery
Detecting Winds Aloft from Water Vapour Satellite Imagery in the Vicinity of Storms
Rabin, R.M., Corfidi, S.F., Brunner, J.C., Hane, C.E. Weather, 59, 251-257
GOES-8 Water Vapour Imagerydivergence (yellow, 10-5s-1), and absolute vorticity (red, 10-5s-1)
2300 UTC 19 July 1995
0445 UTC 20 July 1995
Upper air winds at 0000 UTC on 03 June 2003
300 mb rawinsonde analysis
Upper air winds at 0000 UTC on 03 June 2003 from satellite
black: 100-250, cyan: 251-350, yellow: 351-500 hPa
Surface weather map at 2300 UTC, 11 June 2003
Severe weather reports
wind damage (blue)
large hail (green)
tornadoes (red)
GOES-12 divergence at 300 hPa
11 June 2003 at 1945 UTC
12 June 2003 at 0045 UTC
First Guess (NOGAPS) divergence at 300 hPa
11 June 2003 at 1945 UTC
12 June 2003 at 0045 UTC
Surface weather map at 2300 UTC, 12 June 2003.
GOES-12 divergence at 300 hPa on 12 June 20032145 UTC
derived from satellite-winds
first guess model (NOGAPS)
VIS0115 UTC
WV0215 UTC
Greensburg, KSStorm
05 May 2007
Real-time and archive:
http://www.nssl.noaa.gov/~rabin/winds
http://cimss.ssec.wisc.edu/mesoscale_winds