NOAA-MDL Seminar 7 May 2008 Bob Rabin NOAA/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 MCS Storm 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,

Identify and track MCS - Cold cloud tops - Radar reflectivity - Adjustable thresholds Time trends of MCS characteristics - Size - Cloud top temperature stats - Radar reflectivity stats - Lightning - Storm environment from RUC,... Real-time and archived data on-line Data access from NOMADS/THREDDS catalog

Data Flow GOES Radar RUC model analysis THREDDS Lightning Tracking Algorithm 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:

Motion Estimation Uses K-Means clustering and Kalman filters Forecast dBZActual dBZ 30 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 methods But at different scales.  Track the clusters using flow-based methods (minimization of cost-functions)‏ Like flow-based methods Does 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):

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 public_html/vis_1km/ 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):

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,

GOES-8 Water Vapour Imagery divergence (yellow, s - 1 ), and absolute vorticity (red, s - 1 )‏ 2300 UTC 19 July UTC 20 July 1995

Upper air winds at 0000 UTC on 03 June mb rawinsonde analysis

Upper air winds at 0000 UTC on 03 June 2003 from satellite black: , cyan: , yellow: 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 UTC derived from satellite-winds first guess model (NOGAPS)‏

VIS 0115 UTC WV 0215 UTC Greensburg, KS Storm 05 May 2007

Real-time and archive: