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Combining mesoscale, nowcast, and CFD Combining mesoscale, nowcast, and CFD model output in near real-time for model output in near real-time for
protecting urban areas and buildings from protecting urban areas and buildings from releases of hazardous airborne materialsreleases of hazardous airborne materials
S. Swerdlin, T. Warner, J. Copeland, D. Hahn, S. Swerdlin, T. Warner, J. Copeland, D. Hahn, J. Sun, R. Sharman, Y. Liu, J. Knievel, A. J. Sun, R. Sharman, Y. Liu, J. Knievel, A.
Crook, M. RainesCrook, M. Raines
National Center for Atmospheric Research National Center for Atmospheric Research [email protected]@ucar.edu
J. WeilJ. Weil
University of Colorado, BoulderUniversity of Colorado, Boulder
ConceptConcept
Combine models at various scales to Combine models at various scales to provide detailed urban wind field provide detailed urban wind field awarenessawareness
Develop hazardous material sensor Develop hazardous material sensor network and algorithms to detect and network and algorithms to detect and track airborne releasestrack airborne releases
Detect a release, characterize source, and Detect a release, characterize source, and use transport and dispersion model with use transport and dispersion model with time-varying 3-D urban wind field to time-varying 3-D urban wind field to predict path and concentration of materialpredict path and concentration of material
GoalsGoals
Provide early detection and warning Provide early detection and warning of hazardous airborne releasesof hazardous airborne releases
Aid evacuation and recovery Aid evacuation and recovery operations by proving better operations by proving better information to decision makersinformation to decision makers
Dual use: Monitor and reverse-locate Dual use: Monitor and reverse-locate sources of industrial pollution; sources of industrial pollution; support fire fighting and flood support fire fighting and flood managementmanagement
Computing urban wind fieldsComputing urban wind fields NCAR developing operational system in NCAR developing operational system in
Washington, DC: three models used to Washington, DC: three models used to compute “rooftop” fieldscompute “rooftop” fields• RTFDDARTFDDA: MM5-based Real-Time Four-: MM5-based Real-Time Four-
Dimensional Data AssimilationDimensional Data Assimilation• VDRASVDRAS: Variational Doppler RADAR : Variational Doppler RADAR
Assimilation SystemAssimilation System• VLASVLAS: Variational : Variational LIDARLIDAR Assimilation System Assimilation System
Blend these onto a common gridBlend these onto a common grid Use blend to provide initial and lateral Use blend to provide initial and lateral
boundary conditions for city- and building-boundary conditions for city- and building-aware modelsaware models
““Rooftop” model 1: MM5-based Rooftop” model 1: MM5-based RT-FDDART-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
Rooftop models 2&3: VDRAS and Rooftop models 2&3: VDRAS and VLASVLAS
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
Example: VDRAS coupled to plume Example: VDRAS coupled to plume modelmodel
VDRAS wind vectors show convergence line below formation of thunderstorm cells
Example 1: VDRAS coupled to Example 1: VDRAS coupled to plume model (cont)plume model (cont)
Wash.D.C.
1629 LTrelease1557 LT
release
Release height – 10 m1 kg inert, nonbuoyant gas15 June 1998
Emergency response application. Two simulated releases 30 minutes apart: plume model coupled to VDRAS winds
L = 10-100 kmL = 10-100 km L = 1-10 kmL = 1-10 kmL = 10-1000 kmL = 10-1000 km
skimming flow models
Spatial-temporal blending scheme
skimming flow master
grid (updated every 5 mins)
RT-FDDART-FDDA VDRASVDRAS VLASVLAS
skimming flow master grid
(covers large urban area)
CFDRC’s CFDRC’s CFD-Urban, CFD-Urban,
updated updated every 10 every 10
minsmins
L = 2-5 mL = 2-5 m
Urban canopy flow: 10 x 10 km tiles
L = 10-20 mL = 10-20 m
LANL’s LANL’s QUIC-Urb, QUIC-Urb,
updated updated every 5 minsevery 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
Example 2: VLAS applied at the Example 2: VLAS applied at the neighborhood scaleneighborhood scale
CTI Doppler lidar
Notional plume
VLAS wind vectors in Washington, VLAS wind vectors in Washington, D.C., 7 May 2004, day timeD.C., 7 May 2004, day time
Storm to the NE
Closely separated simulated Closely separated simulated releases have distinct patternsreleases have distinct patterns
Simulated releases from same Simulated releases from same location, at 5-min intervalslocation, at 5-min intervals
Sensitivity of simulated plume Sensitivity of simulated plume prediction to atmospheric stability prediction to atmospheric stability
conditionsconditionsNeutral/Convective
atmosphere Stable atmosphere
Using high-resolution winds to compute Using high-resolution winds to compute “threat zone” in near real-time“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
Concept: continuously map and detect Concept: continuously map and detect plumes with rooftop LIDAR networkplumes with rooftop LIDAR network
Continuously collected LIDAR output, and examine for presence of unusual plumes
Simulation of rooftop lidar’s view of Simulation of rooftop lidar’s view of point-source hazardous agent point-source hazardous agent
release in mild hazerelease in mild hazeT+0s T+40s T+60s T+80sT+20s
T+100s T+120s T+140s T+160s T+180s
T+200s T+220s T+240s
ConclusionConclusion
Scheme of creating multi-resolution Scheme of creating multi-resolution “rooftop” blend, and using this to “rooftop” blend, and using this to provide background and forcing provide background and forcing conditions for building-aware models conditions for building-aware models seems to be effectiveseems to be effective
More verification is needed to More verification is needed to determine skill of urban coupled NWP determine skill of urban coupled NWP plume-model systemplume-model system