1. System for Storm Analysis Using Multiple Data Sets FY 2007 Proposal to the NOAA HPCC Program Principal Investigator:Robert M. Rabin Collaborators:  Valliappa Lakshmanan (CIMSS, University of Oklahoma)‏  Jaime Daniels (NOAA/NESDIS)‏  Arnie Gruber (NOAA/CREST, City College of New York)‏  Steven Weiss(NOAA/SPC)‏  Tom Whittaker (CIMSS, University of Wisconsin-Madison)‏ Proposal Theme:Technologies for Modeling, Analysis, or Visualization Funding Summary:FY 2007 $ 38,000 (in-kind matching: $15,000)

2. OBJECTIVES Extend the web-based "Tool for storm analysis using multiple data sets" to include additional observational data and to provide short-term forecasts of storm location and relevant atmospheric conditions. Additional data will include derived products from the national network of WSR-88D Doppler radars not routinely available elsewhere: storm relative inflow and low-level air convergence hail diagnosis and rotation tracks Tracking of satellite, radar reflectivity fields and estimated precipitation will be linked together utilizing an existing approach which accounts for: movement of multiple scales movement of cloud and radar reflectivity and 'forecast' positions 1-2 hours into the future (Lakshamanan,2003)‏ Provide output from this tool to operational units in NOAA. This includes testing at the Storm Prediction Center (SPC), transfer of imagery to AWIPS and N-AWIPS, and input to a nowcasting system for greater New York City (in collaboration with NOAA cooperative institute CREST).

3. LEVERAGE A cost effective means to further develop tools for research and operations which evaluate and display time trends of a diverse set of variables in the reference frame of propagating storms. Our proposed HPCC efforts are of interest to these programs and we will benefit from working together on the import of data into AWIPS and implementation and testing of the tracking tools: Collaboration between NESDIS (Jaime Daniels) and MDL to import various GOES satellite products into AWIPS (in parallel to SCAN which uses radar data). Collaborative effort between NSSL and MDL which has successfully pushed LMA and radar analysis products to AWIPS workstations at four different WFO's in 2005. Collaborative effort with NESDIS and CREST (NOAA Cooperative Institute) to develop a nowcasting system for the greater New York City region.

4. DELIVERABLES Working system providing the tracking of satellite, radar reflectivity, estimated precipitation, 'forecast' positions 1-2 hours, and time trends in environmental variables. Viewable outputs in AWIPS and N-AWIPS. _______________________________________________________________________________________ PERFORMANCE MEASURES Enhancement of the K-means algorithm to track radar and satellite simultaneously. Tracking of satellite, radar reflectivity fields and estimated precipitation will be linked together by enhancing an existing approach which accounts for: movement of multiple scales movement of cloud and radar reflectivity and 'forecast' positions 1-2 hours into the future (Lakshamanan, 2003). Implementation of additional observational data useful in providing short-term forecasts of storm location and relevant atmospheric conditions. Additional data will include derived products from the national network of WSR-88D Doppler radars, such as estimates of storm relative inflow and air convergence not routinely available elsewhere. Transfer of output from this tool to operational units in NOAA. Testing at the Storm Prediction Center (SPC), transfer of imagery to AWIPS and N-AWIPS Input to a nowcasting system for greater New York City (in collaboration with NOAA cooperative institute CREST). _______________________________________________________________________________________ _______________________________________________________ MILESTONES Month 01 - enhancements of K-means algorithm Month 03 - enhancement of web tool to ingest radar grids Month 06 - successful demonstration of radar-satellite tracking in web-based display Month 09 - successful demonstration of products in AWIPS and N-AWIPS Month 12 - implementation and testing of precipitation nowcasting in NYC metro area in collaboration CREST project

5. MILESTONES Month 01 - enhancements of K-means algorithm Month 03 - enhancement of web tool to ingest radar grids Month 06 - successful demonstration of radar-satellite tracking in web-based display Month 09 - successful demonstration of products in AWIPS and N-AWIPS Month 12 - implementation and testing of precipitation nowcasting in NYC metro area in collaboration CREST project LINKS Presentation: International Symposium on Visual Computing (2005) Presentation: International Symposium on Visual Computing (2005) Paper (in book): Rabin, R. M., T. Whittaker, 2006: Tool for Storm Analysis Using Multiple Data Sets. Paper (in book) Advances in Visual Computing, G. Bebis, R. Boyle, D. Koracin, B. Parvin, Ed(s)., Springer, 571-578. Nowcast ProjectNowcast Project: Example case study with NOAA CREST (City College of New York). On-line Tracker ToolOn-line Tracker Tool: Real-time and archive. Scale dependent motionScale dependent motion: Overview of K-means approach and 0-1 hr position forecasting (from Lakshmanan).

6. Version 3 of ThunderTracker Version 1 was a simple display with animations and overlays Version 2 was a full-blown prototype that added many tracking features with some graph plotting Version 3 moves beyond those with  A new, extensible database  More complex graphing and overlays on client side  Perhaps using Flash instead of Java on client side?

7. New Server-side Database MySQL based instead of previous “flat text file” Needed more flexibility to handle additional parameters and image types Tables fed from different sources but accessed through Java server-side technology

8. Database Structure Two tables:  Storm-connected data Each tracked storm has many items (columns)‏ Indexed by time; sorted on retrieval by storm-key number  Images Table stores references to the image files, not the images Types: Satellite (visible, IR), Radar, Lightning Indexed by time MySQL chosen  Easy, simple needs with no critical storage or retrieval considerations

9. ThunderTracker Database Table - Images Table Storm-Tracked Parameters * GOES – visible * GOES – IR * Radar – VIP levels * Lightning – counts * Date-time information * Storm ID, Location, Date-time * CAPE * Bunkers Storm motion * Observed Storm motion * Convective Inhibition * LCL Height * Shear * Helicity * Min IR temperature and couplets * Size & Orientation * Radar reflectivity and VIL * Rotation (Doppler azimuthal shear)‏ * Hail probability, expected size * Echo Top height * Upper-level divergence

10. Use Flash instead of Java on Client Problems with Java (client) plug-in:  Installation requires “admin privileges”  Plug-in memory limit is only 64MB by default Hard to change default – different on each platform  Incompatibilities with older Java versions If we move to Java 1.5 and Swing components, then old users of IE running Microsoft's Java will not work Flash advantages:  No memory limit, easy install, alread on more than 95% of computers world-wide, easy and clean updates, excellent image and graphing libraries  Use ActionScript for a “single-frame Flash movie” allows for animations, overlays, etc

11. 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, 571-578.

12. 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

13. Real-time and archive data: http://tracker.nssl.noaa.gov

14. Example session:

15. Mesoscale Convective Complex: Mature Stage

16. Time Series: Mature Stage

17. Mesoscale Convective Complex: Decaying Stage

18. Time Series: Decaying Stage

19. Tornadic Storm Track

20. Time Series: Tornadic Storm Track

21. "K-means" technique for tracking satellite cloud top and radar reflectivity features.

22. Real-time loops (WSR-88D and GOES): http://www.nssl.noaa.gov/~rabin/tracks

23. Thunderstorm Tracks from GOES-11/12 and WSR-88D by Robert Rabin 1 Thunderstorms are identified by cold cloud tops from the GOES-11/12 IR window channel (11 micron) imagery or from radar echos. An algorithm originally developed by Brian Mapes (19??) is used to automatically detect cloud clusters with temperatures less than a cloud top temperature threshold. Here, the algorithm has been adapted to identify and track areas where the radar reflectivity exceeds a threshold. The areas or clusters can be followed in time by searching for partial overlap in their position between successive images. (This implies that for a given speed of movement and image frequency, that the clusters must exceed a certain size threshold to be sucessfully tracked. For example, the diameter must be greater than 15 km for a movement of up to 60 km/hr and 15 min image frequency). A time series of the centroid position, surface area, mean, maximum and mimimum reflectivity are evaluated for each cluster. When run on GOES data, the cloud tracks can be displayed for different cloud top temperature thresholds ranging from -43 to -73 deg. C. For WSR-88 data, the algorithm is run with different reflectivity thresholds (5 dbZ intervals from 30 to 60 dbZ). For each value, a unique set of precipitation clusters, tracks and reflectivity statistics are obtained. The user can decide which threshold to use in displaying the output. Real-time movies include data from the past 3 hours and and future projection of images from the K-means algorithm developed by V. Lakshmanan of NSSL and CIMMS University of Oklahoma.K-means algorithmV. Lakshmanan Fig. 1. GOES moviesFig. 2. WSR-88D movies Past 3 hours Including 60,120 minute nowcastIncluding 30, 60 minute nowcastIncluding 60,120 minute nowcastIncluding 30, 60 minute nowcast

24. Example of GOES IR Cloud tops, observed 1045 UTC

25. Example of GOES IR Cloud tops, observed 1132 UTC

26. Example of GOES IR Cloud tops, projected 1-hr, 1245 UTC

27. Example of GOES IR Cloud tops, projected 2-hr, 1345 UTC

28. Wind vectors and horizontal divergence at 300 mb (green contours) from GOES water vapor imagery.

29. Identifying and Tracking Severe Weather Precursor Signatures from High-resolution Satellite Data in Real-Time FY 2008 Proposal to the NOAA HPCC Program Principal Investigator:Robert Rabin Collaborators: Valliappa Lakshmanan (CIMMS, University of Oklahoma)‏ Jaime Daniels (NOAA/NESDIS)‏ Arnie Gruber (NOAA/CREST, City College of New York)‏ Kurt Hondl (NOAA/NSSL)‏ Steven Weiss (NOAA/SPC)‏ John Moses (NASA/Goddard, Geenbelt MD)‏ Wayne Feltz (CIMSS, University of Wisconsin-Madison)‏ Proposal Theme: Technologies for Modeling, Analysis or Visualization Funding Summary: FY 2008 $ 37,000 (In-Kind $10,000)‏

30. Benefits Enable timely use of GOES high resolution imagery data for algorithm development identification and tracking rapidly changing storm top features keys on overshooting tops (intense updrafts) from hi-res visible imagery incorporates info from GOES multi-spectral data (leverage from NASA) links satellite and radar products for thunderstorm tracking Multi-Line-Office, Multi-University Proposal, Leverages tasks from existing NOAA, NASA projects Builds on previous HPCC projects Makes use of high bandwidth data transfer Cost Effective means to further develop tools for research and operations Transfer output to operational NOAA units (AWIPS, N-AWIPS)‏

31. Methodology Receive, fuse and remap visible 1km satellite data from both GOES satellites over the CONUS. Detect overshooting tops from the visible images. Track overshooting tops over time and attach radar and satellite based parameters with these overshooting tops so that forecasters can study the time evolution. Display algorithm output (real-time) in Java applet.

32. Deliverables Overshooting cloud top algorithm output for combined use with radar data. Viewable in: 1) a Java applet on the web, 2) AWIPS, and 3) N-AWIPS. Milestones Receive award notification (tentative) – 15 March 2008 Order and receive hardware – 15 April 2008 Have overshooting tops algorithm running in real-time – 15 May 2008

33. Real-time loops (WSR-88D, GOES): http://www.nssl.noaa.gov/~rabin/vis_1km

34. GOES 1km visible probability of “overshooting tops”: 11 June 08 2332 UTC

35. GOES Cloud top couplets: 11 June 08 2332 UTC

36. WSR-88D Composite reflectivity: 11 June 08 2328 UTC

37. GOES Cloud top temperature: 11 June 08 2332 UTC