1. Evaluation of CASA (and other gap-filling) Radars Severe Weather Workshop for NWS Warning Decision Making - 11 July 2007 Kurt Hondl DOC/NOAA/OAR National Severe Storms Lab Kurt.Hondl@noaa.gov Kurt.Hondl@noaa.gov Brenda Philips CASA Eng. Research Center U Mass, Amherst bphilips@ecs.umass.edu bphilips@ecs.umass.edu

2. Gap-Filling Radars  Any radar that NOAA doesn’t control  CASA  FAA TDWR (and ARSR)  Environment Canada radars  TV station radars  Mobile radars (SMART-R)

3. Gap-Filling Radars  Considerations  Scan Strategy  Data Quality / Filtering  Data Resolution (time and space)  Moments (reflectivity only, dual pol)  Wavelength  PRF (dual PRF)  How do these considerations affect my decision making process?  How do gap-filling radars change my warning products?

4. What is CASA?  National Science Foundation Engineering Research Center  Academic, Government and Private Sector Partners  NOAA is a CASA partner  CASA’s Focus: New weather observation system paradigm based on low-power, low-cost networks of radars.  Faculty, students and industry/practitioners work in a multi-disciplinary environment on real-world technology.  Year 4 of a 10-year research project

5. Radar System Comparison CASAWSR-88D X-band (3cm), 25 kW, 1.8 deg beamwidth, Dual Pol S-band (10cm), 750 kW, 1 deg beamwidth, Dual Pol upgrade coming 40 km radar range, 50 m range gates230 km radar range, 250 m range gates 1 deg azimuthal spacing1 deg azimuthal spacing (upgrade to 0.5 deg spacing next year) Dual PRF(38 m/s Nyquist Velocity) 8.0 deg 11.0 deg 20060815-222919 20060815-222924 gap

6. End users: NWS, Emergency Response, Researchers 1. Radars Scan atmosphere and send data to repository (initially centralized, later distributed) 2. Weather Detection algorithms run on data 3. Detections and other data are “posted” in Feature Repository 4. Tasks are generated based on detections and User Rules 5. Optimal Radar Scans are configured to complete as many tasks as possible while maximizing data utility to users CASA System Architecture

7. Radar Scanning Strategy - DCAS Distributed Collaborative Adaptive Sensing: 1 minute update consisting of a 20 sec. surveillance scan and 40 sec. targeted sector scans based on multiple user needs, feature detections, radar capabilities. 360 degree surveillance scans at 2 degrees. Small squares are weather detections. Larger polygons are potential tasks that could be scanned. Sector scan. The number of lines indicate how many elevations are scanned

9. CASA Spring Experiment ‘07  9 April – 10 June  Three, 3-week, IOPs  Daily meeting to establish plan for day, evaluation of prior day’s results  24/7 operation during weather events  Kick-off meeting March 30, mid term evaluation, final evaluation  Evaluation in Hazardous Weather Testbed, pilot emergency mangers, CASA reseachers. Experiments will run in 2008 and 2009

10. Experimental Warning Project Tasks  Obtain NOAA input into the design of a new weather observation system  Forecasters evaluate real time CASA data in test and case studies (May 8, April 10), complete evaluation  What weather features do you see in CASA data that might help with severe weather warning decisions?  What are the strengths and weaknesses of CASA’s technical capabilities?

11. Feedback on Weather Features seen in CASA data  Mini Super Cells/Small Circulations  Detailed Super Cell storm structure  Hook Echo  V-Notch  Bow Echo  Convergence/Divergence  Rotation couplets: mesocyclonic, tornadic  Lacking: Low reflectivity boundaries KWLE at 1.0 deg elevation, 21km range (~200m AGL) CASA Single Radar Data

12. CASA’s Adaptive Sector scanning at multiple elevations from 1 to 14 degrees, 40 sec. sector scan NEXRAD Comparison CASA High Resolution Data Ground Truth Verification by Val Castor’s NEWS9 April 10, 2007 Elevated Super Cell

13. Minco Mini-Supercell Tornado May 8, ~11pm CDT 10:51 CDT © Patrick Marsh

14. Feedback on Technology  High resolution data and strong gradients provide more detailed view of weather features: v-notch, bow echoes, downdrafts, appendages  Lower troposphere data shows mini-super cells, fine scale rotations.  1 minute updates provide more rapid confirmation of feature evolution  2 degree, 360 degree scans important for situational awareness  Rapid sector scans at multiple elevations enables faster vertical analysis of storm structure, but…  Scans not always in the right location, narrow sectors an issue

15. Narrow Sectors Issues:  Task Definition  Trade Offs: sector width, update time, elevations

16. Feedback on Technology  Data quality reflects early stage of system  Minimum reflectivity threshold of 20 dBZ hinders seeing boundaries  Existing visualization tools need to be enhanced to accommodate DCAS scanning.  Merged data, navigating sector scans  Decouple system heartbeat from visualization Needed: New Conceptual Models for Severe Warning Process for CASA data Which fine scale rotations warrant a warning? What are the characteristics of super cell evolution in CASA data? Impact of 1 minute updates for resolving uncertainty

17. Next Steps  Analysis of storm features to begin to develop new conceptual models for warning: science/operations collaboration  Development of training for HWT2008  Modify systems for storm season 2008, 2009 based on feedback  Data quality issues  Modify adaptive scanning  New visualization approaches  Bring case studies to other forecast offices for feedback