Models-3 Users’ Models-3 Users’ WorkshopWorkshopRaleigh, North CarolinaRaleigh, North CarolinaOctober 27-23, 2003October 27-23, 2003

New Developments and New Developments and Applications of Models-3Applications of Models-3

in Canadain Canada

J. Wayne Boulton*, Mike Lepage, Xin Qiu, and Martin GauthierRWDI West Inc., Guelph, Ontario, Canada

Colin di CenzoEnvironment Canada P&YR, Vancouver, British Columbia, Canada

New DevelopmentsNew DevelopmentsMC2 to MM5 converterMC2 to MM5 converter

Convert MC2 output for input to SMOKE & CMAQ Re-project and interpolate horizontal grids Convert vertical grid to MM5 sigma levels Map different parameters between MC2 and MM5

Parallel processor CMAQ runs on LINUXParallel processor CMAQ runs on LINUX Compile CMAQ (source + libraries) to run in parallel

distributed LINUX environment (with UC Riverside)

Real-time AQ forecasting (MC2, SMOKE, CMAQ)Real-time AQ forecasting (MC2, SMOKE, CMAQ) Assist University of B.C. staff to setup of real-time

12-km & 4-km regional AQ forecasts for the PNW

PNW Model DomainsPNW Model Domains

MC2 to MM5MC2 to MM5Interpolation between projections and grid resolutions

4-km MM5

3.3-km MC2

MC2 to MC2 to MM5MM5

Time Invariant MCI P

Parameter MM5

Parameter MM5 Units Dimensions MC2 MC2 Units Dimension Notes

ALBEDO ALBD % (land,2) AL fraction (land,2)

EMISS SFEM fraction (land,2) Manually input (land,2)

LANDUSE LAND USE category (I ,J ) Manually input Cross

LATCRS LATITCRS decimal degrees

(I ,J ) LA decimal degrees

(I ,J )

LONCRS LONGICRS decimal degrees

(I ,J ) LO decimal degrees

(I ,J )

MAPCRS MAPFACCR [1] (I ,J ) Manually input – MCIP map projection Cross-point

MAPDOT MAPFACDT [1] (I ,J ) Manually input – MCIP map projection Dot-point

MAVAIL SLMO fraction (land,2) HS fraction (land,2)

SIGMAH SIGMAH half-sigma (K) SL half-sigma (K)

TERRAIN TERRAIN M (I ,J ) ME M (I ,J ) Cross-point

ZNT SFZ0 Cm (land,2) ZP LOG (m) (land,2)

Time Variant MCI P

Parameter MM5

Parameter MM5 Units Dimensions MC2 MC2 Units Dimension Notes

GLW LWDOWN W/m2 (I ,J ) SI W/m2 (I ,J ) = -LWDOWN

GROUNDT GROUND T K (I ,J ) TS K (I ,J ,3) Take the first one from MC2

HFX SHFLUX W/m2 (I ,J ) Manually converted - sensible heat flux

PP PP Pa (I ,J ,K) QX Pa (I ,J ,K) Interpolate to Sigma pre-MM5

PSA PSTARCRS Pa (I ,J ) 2P – PZ Pa (I ,J )

QCA CLW kg/kg (I ,J ,K) QD kg/kg (I ,J ,K) Interpolate to Sigma pre-MM5

QFX LHFLUX W/m2 (I ,J ) Manually converted - latent heat flux

QIA ICE kg/kg (I ,J ,K) QE kg/kg (I ,J ,K) Interpolate to Sigma pre-MM5

QRA RNW kg/kg (I ,J ,K) RQ kg/kg (I ,J ,K) Interpolate to Sigma pre-MM5

QSA SNOW kg/kg (I ,J ,K) = 0 kg/kg (I ,J ,K) Interpolate to Sigma pre-MM5

QVA Q kg/kg (I ,J ,K) HU kg/kg (I ,J ,K) Interpolate to Sigma pre-MM5

RAINCON RAIN CON Cm (I ,J ) PC M (I ,J )

RAINNON RAIN NON Cm (I ,J ) PR - PC M (I ,J )

REGIME REGIME [1] (I ,J ) Manually converted - stability classes

RGRND SWDOWN W/m2 (I ,J ) FU W/m2 (I ,J )

TA T K (I ,J ,K) TT Cº (I ,J ,K) Interpolate to Sigma pre-MM5

UA U m/s (I ,J ,K) UU Knots (I ,J ,K) Interpolate to Sigma pre-MM5

UST UST m/s (I ,J ) UE m/s (I ,J )

VA V m/s (I ,J ,K) VV Knots (I ,J ,K) Interpolate to Sigma pre-MM5

WA W m/s (I ,J ,K+1) WZ m/s (I ,J ,K) K+1 layer = K layer (Interpolate to Sigma)

ZPBL PBL HGT M (I ,J ) H M (I ,J )

Mapping Time Invariant & Time Variant param’s from MC2 to MM5

MC2 to MM5MC2 to MM5Conversion performance & evaluation

MC2 to MM5 Data Conversion Wind Speed Profiles at 1300Z, Aug 9, 2001

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Wind Speed (m/s)

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Lev

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MM5 (Converted from MC2) MC2

MC2 to MM5 Data ConversionTemperature Profiles at 1300Z, Aug 9, 2001

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210 230 250 270 290 310

Tepmperature (K)

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Parallel CMAQ PerformanceParallel CMAQ PerformanceCPU time elapsed (seconds), and

Percent (%) time relative to a single-CPU run.

CMAQ Parallel Run ComparisonOn RAM Linux Cluster

15662

8985

5743

4648

100.0% 57.4% 36.7% 29.7%0

2000

4000

6000

8000

10000

12000

14000

16000

1 2 4 6

Num of CPUs

Ru

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ime

(sec

)

Ozone (ppbV)

PM2.5 (µg/m3)

Vancouver

ModelledMeasured

Legend

ModelledMeasured

Legend

CMAQ ModelCMAQ ModelPerformancePerformance

Ozone (ppbV)

PM2.5 (µg/m3)

ModelledMeasured

Legend

ModelledMeasured

Legend

CMAQ ModelCMAQ ModelPerformancePerformance

Chilliwack

New ApplicationsNew Applications

CMAQ runs in support of Health Canada risk assessmentsCMAQ runs in support of Health Canada risk assessments 75% electric vehicle fleet penetration with four

electricity production scenarios GIS-based post-processing routines developed Ethanol blend fuel (E10) scenarios (ongoing)

CMAQ runs in support of emission source contribution CMAQ runs in support of emission source contribution assessments from power plantsassessments from power plants Various emission / source contribution scenarios Hourly power plant emissions modelled in SMOKE

Development of improved spatial surrogates and temporal Development of improved spatial surrogates and temporal profiles for SMOKEprofiles for SMOKE Based on traffic demand model (EMME/2) outputs

Old Surrogates0.000063 - 0.001087

0.001088 - 0.002112

0.002113 - 0.003137

0.003138 - 0.004162

0.004163 - 0.005186

0.005187 - 0.006211

0.006212 - 0.007236

0.007237 - 0.008261

0.008262 - 0.009285

0.009286 - 0.010310

On-Road Vehicle On-Road Vehicle Surrogates & Temporal ProfilesSurrogates & Temporal Profiles

Spatial Surrogate Techniquesa) Road lengthb) VMT (traffic demand model)

All Roads - Cars and Trucks0.000000

0.000001 - 0.002000

0.002001 - 0.004000

0.004001 - 0.006000

0.006001 - 0.008000

0.008001 - 0.000000

0.000001 - 0.020000

0.020001 - 0.040000

0.040001 - 0.060000

0.060001 - 0.080000