1. Combining mesoscale, nowcast, and CFD model output in near real-time for protecting urban areas and buildings from releases of hazardous airborne materials S. Swerdlin, T. Warner, J. Copeland, D. Hahn, J. Sun, R. Sharman, Y. Liu, J. Knievel, A. Crook, M. Raines National Center for Atmospheric Research swerdlin@ucar.edu J. Weil University of Colorado, Boulder

2. Concept Combine models at various scales to provide detailed urban wind field awareness Combine models at various scales to provide detailed urban wind field awareness Develop hazardous material sensor network and algorithms to detect and track airborne releases Develop hazardous material sensor network and algorithms to detect and track airborne releases Detect a release, characterize source, and use transport and dispersion model with time-varying 3-D urban wind field to predict path and concentration of material Detect a release, characterize source, and use transport and dispersion model with time-varying 3-D urban wind field to predict path and concentration of material

3. Goals Provide early detection and warning of hazardous airborne releases Provide early detection and warning of hazardous airborne releases Aid evacuation and recovery operations by proving better information to decision makers Aid evacuation and recovery operations by proving better information to decision makers Dual use: Monitor and reverse-locate sources of industrial pollution; support fire fighting and flood management Dual use: Monitor and reverse-locate sources of industrial pollution; support fire fighting and flood management

4. Computing urban wind fields NCAR developing operational system in Washington, DC: three models used to compute “rooftop” fields NCAR developing operational system in Washington, DC: three models used to compute “rooftop” fields RTFDDA: MM5-based Real-Time Four- Dimensional Data AssimilationRTFDDA: MM5-based Real-Time Four- Dimensional Data Assimilation VDRAS: Variational Doppler RADAR Assimilation SystemVDRAS: Variational Doppler RADAR Assimilation System VLAS: Variational LIDAR Assimilation SystemVLAS: Variational LIDAR Assimilation System Blend these onto a common grid Blend these onto a common grid Use blend to provide initial and lateral boundary conditions for city- and building- aware models Use blend to provide initial and lateral boundary conditions for city- and building- aware models

5. “Rooftop” model 1: MM5-based RT-FDDA time Forecast RT- FDDA e.g., new 6 h forecast every 30 min at 500 m res, using real-time obs TAMDAR LIDAR RADAR SATELLITE SURFACE OBS QuickSCAT scatterometer UPPER AIR

6. Rooftop models 2&3: VDRAS and VLAS RADAR Radial winds Desired 3-D winds LIDAR RADAR/LIDAR assimilation system. Uses 4 Dimensional Variational Assimilation (4DVAR) to retrieve 3-D winds from Doppler radar/lidar

7. Example: VDRAS coupled to plume model VDRAS wind vectors show convergence line below formation of thunderstorm cells

8. Example 1: VDRAS coupled to plume model (cont) Wash. D.C. 1629 LT release 1557 LT release Release height – 10 m 1 kg inert, nonbuoyant gas 15 June 1998 Emergency response application. Two simulated releases 30 minutes apart: plume model coupled to VDRAS winds

9. L = 10-100 km L = 1-10 km L = 10-1000 km skimming flow models Spatial-temporal blending scheme skimming flow master grid (updated every 5 mins) RT-FDDA VDRAS VDRAS VLAS VLAS

10. skimming flow master grid (covers large urban area) CFDRC’s CFD-Urban, updated every 10 mins L = 2-5 m Urban canopy flow: 10 x 10 km tiles L = 10-20 m LANL’s QUIC-Urb, updated every 5 mins Provides 3-D, time-varying, initial, and lateral boundary conditions to building- aware models, every 5 minutes Building flow: 1.5 x 1.5 km tiles

11. Example 2: VLAS applied at the neighborhood scale CTI Doppler lidar Notional plume

12. VLAS wind vectors in Washington, D.C., 7 May 2004, day time Storm to the NE

13. Closely separated simulated releases have distinct patterns

14. Simulated releases from same location, at 5-min intervals

15. Sensitivity of simulated plume prediction to atmospheric stability conditions Neutral/Convective atmosphere Stable atmosphere

16. Using high-resolution winds to compute “threat zone” in near real-time X e.g., agent released at X would require 1 min to impact the target 1 km x t – 1 m X t – 3 m

17. Concept: continuously map and detect plumes with rooftop LIDAR network Continuously collected LIDAR output, and examine for presence of unusual plumes

18. Simulation of rooftop lidar’s view of point-source hazardous agent release in mild haze T+0s T+40s T+60s T+80s T+20s T+100s T+120s T+140s T+160s T+180s T+200s T+220s T+240s

19. Conclusion Scheme of creating multi-resolution “rooftop” blend, and using this to provide background and forcing conditions for building-aware models seems to be effective Scheme of creating multi-resolution “rooftop” blend, and using this to provide background and forcing conditions for building-aware models seems to be effective More verification is needed to determine skill of urban coupled NWP plume-model system More verification is needed to determine skill of urban coupled NWP plume-model system