1. Prediction Thrust Activities in CASA Briefing for WNI Dr. Ming Xue Director of CAPS Center for Analysis and Prediction of Storms August 2, 2006

2. High-resolution simulations and tornadoes, tornadic thunderstorms and downbursts

3. 25-m tornado simulation using 2048 1-GHz alpha processors. 30-min simulation took 18 hours

4. Movie of Cloud Water Field 25 m, 7.5x7.5km domain, 30 minutes 25 m resolution simulation of a tornado within a supercell thunderstorm Numerical Simulation by Ming Xue School of Meteorology University of Oklahoma Movie by Greg Foss Pittsburgh Supercomputing Center

5. See 2 movies

6. Variational Analysis of Over-sampled Dual-Doppler Radial Velocity Data and Application to the Analysis of Tornado Circulations Ming Xue 1, Shun Liu 1 and Tian-you Yu 2 1 Center for Analysis of Prediction of Storms University of Oklahoma Based on Xue, Liu and Yu (JTech 2006, Accepted)

7. Motivation CASA radars will have 100 m radial resolution but 2 degree beam width therefore coarse azimuthal resolutions The azimuthal resolution is most important for capturing wind shear associated with tornado circulations Emulator-based studies have shown that the broad beams of CASA radars present the largest challenge with tornado detection/identification More detailed flow structure can be recovered from over-sampled dual-Doppler wind measurements Variational method combined with a realistic observation operator/radar emulator can retrieve sub-beam structures smoothed out by the broad beam-weighting function, when over- lapping samples are available

8. Illustration of the simulation of radial velocity data from a gridded wind field. Similar to that of Wood and Brown (1997), except for over-sampling. Sample Volume

9. In the following 1-D illustration, when the data samples are the same in number as the number of grid points, the grid point values can be determined exactly. Equation is A x = y, where y is observation vector, x is the state vector on the grid, and A contains the coefficients of discrete weighting function. We seek x from y. Our problem is more difficult because u and v are not directly observed (V r is), and observations may be insufficient to determine u and v. Variational method is most suitable for such a problem – under- determinedness can be avoided by including a background and/or spatial smoothing – realized via recursive filter Dual-Doppler wind observations are assumed and an observational operator that simulates the radar volume sampling is used. Principle

10. Radial Velocity Data Emulation Wood and Brown (1997) Vortex is assumed 2D

11. Cost function of variational analysis B u and B v are modeled using recursive filter. Spatial de-correlation scale was 5 grid intervals for idealized case and 10 grid interval for real case Background wind is assumed zero.

12. Tests using Simulated Data Simulated radar observations for the 25 m tornado simulation of Xue Analysis domain is 9 x 9 km. Grid size is 361 x 361 with 25 m grid spacing. Range resolution is 100m. Experiments examine the impact of the following on analysis: –azimuthal increment of over-sampling, –distance of radars from tornado, –effective beamwidth

13. Simulated radial velocity fields Radar is located at (0, 0) km, or 15 km south of the 9 km × 9 km analysis domain center with azimuthal increments of (a) 2 o, (b) 1 o and (c) 0.125 o (c) 0.125 o 1o1o 2o2o Over-sampling alone, without wind retrieval, is not much of a help beyond a factor of two over-sampling

14. Impact of azimuthal increments of sampling “truth” & analyses from radial velocity data sampled at azimuthal increments of 0.125 o and 2 o Beamwidth = 2 o Distance of radars = 15 km truth O/S 0.125 o N/O 2 o u v wind

15. Correlation coefficients (CC) for different azimuthal increments when the radars are located 15 km from the center of the analysis domain. Azimuthal increment 0.1250.51.01.52.0 CC0.900.820.780.720.68

16. Impacts of radar distance from the tornado Correlation coefficients of the analyses when the radars are located at different distances from the center of analysis domain, for azimuthal increments of 0.125º and 2º. Distance12.015.018.021.024.0 Azimuthal increment 0.125º 0.920.900.830.820.76 Azimuthal increment 2º 0.710.690.660.650.56

17. Analyzed winds from V r data sampled at azimuthal increments of 0.125 o (upper panel) and 2.0 o (lower panel). Beamwidth = 2 o, distance = 24 km. Impacts of radar distance from the tornado O/S 0.125 o N/O 2 o

18. Impacts of radar distance from the tornado Analyzed winds from V r data sampled at azimuthal increments of 0.125 o (upper panel) and 2.0 o (lower panel). Beamwidth = 2 o, distance = 15 km. O/S 0.125 o N/O 2 o

19. Impacts of effective beamwidth Increment = 0.125 o, beamwidth = 1 o or 2 o, distance=15km CC = 0.95 for 1 o, 5% > CC of 2 o beamwidth case 1o1o 2o2o

20. Tests using over-sampled KOUN data and regular KTLX data Level-I data for May 8, 2003 OKC tornado from KOUN reprocessed to over-sample at 1/8°increments. 64 pulses are used in each sample. Data from KTLX at regular 1°sample intervals are used together in the variational dual-Doppler analysis

21. Radar radial velocity observations from KTLX and KOUN for May 8 th, 2003 OKC tornado case KTLX KOUN 0.125 o azimuthal increment KOUN 1.0 o azimuthal increment

22. Analyzed wind fields with 8 times oversampling and without oversampling for KOUN 8 times oversampling with KOUN no oversampling u v V 60 35

23. Summary Significantly more details of the flow can be recovered through variational analysis from over-sampled dual-Doppler winds, which is important for characterizing tornado circulation and tornado detection For simulated data, when the azimuthal increments (~0.1 degree @ 15 km range & 1 o effective beamwidth) are comparable to the ‘truth’ resolution (25m), the tornado circulation can be analyzed rather accurately (CC=0.95). The analysis is still smoother than the ‘truth’, due to the need for the help of background error (spatial) covariances (because of the under-determinedness issue at least at the far ranges), realized through spatial smoothing Tests with a real tornado case indicates that reliable over-sampled data can be obtained through re-processing Level-I data, for the WSR-88D radars, and the wind analysis using ‘ over-sampled ’ data from one radar alone is shown to improve dual-Doppler wind analysis for tornado. The over-sampled data are averaged over the sample number of pulses as non-over-sampled data – kind of running averages.

24. 6/066/076/08 Closed-loop peak echo tracking 4 node mosaic’d data operational STSM nowcaster operational 2 km forecasts using ADAS 1 km forecasts using 3DVAR Project ends Implement differential phase attenuation: 7/1/06 Closed-loop peak echo tracking: 7/8/06 GMAP clutter removal operational: 8/15/06 Network-based attenuation R&D using IP1 data: 7/1/06 – 9/1/06 Implement & test network-based attenuation: 9/1/06 – 12/1/06 Network-based attenuation operational: 5/07 PTDM clutter R&D using IP1 data: 7/1/06 – 8/31/06 Implement PTDM in IP1: 9/1/06 – 12/1/06 Implement 4 node mosaic using REORDER 7/06 – 7/06 4 node mosaic’d data operational 9/06 Implement STSM nowcaster in IP1: 8/06 – 10/06 STSM nowcaster R&D using IP1: 11/06 – 5/07 STSM nowcaster operational: 5/07 2 km forecasts using IP1 and ADAS: 5/07 1 km forecasts using IP1 and 3DVAR: 5/08 IP1 Project R&D Schedule (version 1.0)

25. Nowcasting STSM nowcaster operational (2/2007) WDSS-II Storm Cell Identification and Tracking (SCIT) NWP model-based very short-range forecasting

26. Nowcasting v.s. NWP The quoted NWP models are not initialized with radar and/or other high-res data and/or their resolution is too low (Jim Wilson 2006, US-Korea Workshop) NWP should be here! Properly initialized NWP models should out-perform extrapolation/statistical nowcasting models from time zero Simple, fast nowcasting systems are useful for very short ranges because they are fast, not because they are intrinsically better

27. Tornado #1 2200 UTC 2204-2210 UTC OKC tornado 2210-2238 UTC 30 km long path F4 Real Forecast Examples: May 8 th, 2003 OKC tornado

28. ARPS 1-km-grid forecast using 3DVAR + Cloud Analysis cycles over 1 hour 30-min forecast 40-min forecast Reflectivity at 1.45º elevation

29. Observed v.s. Predicted Z and Vr at 1.45° Observation 1 km Forecast From 2140 to 2240 UTC every 5-min Reflectivity Radial velocity

30. Prediction using 100 m resolution grid (over 22 minutes) obs. tornado track pert. pressure sfc winds

31. NWP v.s. Non-dynamic Nowcasting State-of-the-art NWP (model + DA) systems are capable of predicting real tornadoes now! There is real hope for ‘warn on prediction’, even for tornadoes!

32. Time line of planned real time implementations of data assimilation (DA), adaptive observation (AO) and prediction systems for CASA DA and Prediction System Features and FunctionalitiesStart of realtime operation NowcastingStorm Tracking and Nowcasting System; 1 – 60 minute forecast, 1-5 minute forecasts used in MC&C for closed loop control Spring 2007 1 st generation DA & NWP ADAS/3DVAR and Cloud Analysis initialization of ARPS, 5-10 minute update cycles; 30 minute to 6 hour forecast Spring 2007, ADAS-based system, ~2 km resolution; Spring 2008, 3DVAR-based system, ~ 1 km resolution 2 st generation DA & NWP EnKF DA with improved NWP model; 30-second analysis cycles; 30 minute to 6 hour forecast Spring 2009 3 st generation DA, AO & NWP Ensemble-based DA, AO and prediction; fully functional ETKF-based adaptive sampling, EnKF data assimilation and ensemble prediction system; scanning strategy optimized for forecast over 30 minute to 6 hour is decided before the scanning time and integrated into the MC&C. Assimilating clear air data and polarimetric parameters. Spring 2010-2011, within IP5.

33. Planned domain for 2007 CAPS spring forecast project for LEAD and CASA Resolution 2 – 4 km Three 2-km forecasts, one 30 hours and two 9 hours long 8-member 4 km ensembles Assimilating WSR-88D and CASA radar data in the two 9-h 2-km forecasts Requires 8,400 CPU hours/day Using ARPS and WRF Will need about 2000 CPUs concurrently to do all of the above.

34. Main Areas Research of Analysis and Prediction Thrust (mostly at OU) Radar emulation Tornado detection and anticipation algorithms Real time analysis EnKF and 3DVAR data assimilation Storm-scale NWP Dual-pol data assimilation Adaptive observing system development Optimal scanning strategies for detection and NWP Tornado dynamics and phenomenological studies using CASA radar data

35. Hazardous Weather Detection Adaptation and tuning of WDSS-II WSR-88D-based severe weather detection algorithms to work with CASA Networked radar data (MDA/TDA, LLSD and wind analysis) Observing platform-independent detection algorithms based on high-resolution analysis/assimilation data sets, that make full use of all available data (Fritchie et al. 2005; Xue et al. 2006) Wavelet analysis-based tornado detection algorithm (TDA, Liu et al. 2006) TDA based on spectrum/time series data (Yu et al. 2003) Tornado characterization and detection via optimal fitting to data of low-order tornado vortex models (Potvin et al. 2005) Tornado anticipation algorithms via data mining/pattern recognition (Rosendahl, Droegemeier and McGovern) Hydrometeor classification, wind analysis and rain rate estimation (Brenda Dolan)