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 swerdlin@ucar.eduswerdlin@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