Comparison of NOAA/NCEP 12km CMAQ Forecasts with CalNEX WP-3 Measurements

Comparison of NOAA/NCEP 12km CMAQ Forecasts with CalNEX

WP-3 Measurements

Youhua Tang1,2, Jeffery T. McQueen2, Jianping Huang1,2, Marina Tsidulko1,2, Sarah Lu1,2, Ho-Chun Huang1,2, Stuart A. McKeen3, Daewon Byun4, Pius Lee4, R. Bradley Pierce5, Ivanka Stajner6,

Thomas B. Ryerson3, Rebecca Washenfelder3, Jeff Peischl3, John S. Holloway3, David D. Parrish3, James M. Roberts3, Joost

de Gouw3, and Carsten Warneke3

1. IMSG, Camp Springs, MD 20746, USA2. Environmental Modeling Center, NOAA National Centers for Environmental Prediction, 5200

Auth Road, Camp Springs, MD 20746, USA3. NOAA Earth System Research Laboratory, Boulder, CO 80305, USA4. NOAA Air Resource Laboratory, Silver Spring, MD5. NOAA NESDIS/ORA, Madison, WI6. Office of Science and Technology, NOAA National Weather Service, Silver Spring, MD

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142

442 grid cells268 grid cells

Eastern US “1x”

Domain FY 04-05

CONUS “5x” Domain

Eastern US “3x” DomainFY 06-07

NAQFC Configuration

Emissions• EPA CEM anthropogenic inventories• 2005 base year projects to the current year w/ EGU

point sources• BEIS V3 biogenic emissions

Met Model• North American Mesoscale Forecast System (NAM,

WRF-NMM)•12km 60 vertical levels

AQ Model:• EPA Community Multi-scale Air Quality Model • CMAQ V4.6: 12km/L22 CONUS domain • Operational: CB04 gas-phase • Experimental: CB05/AERO4 aerosolOutput available on National Digital Guidance Database 48 hour forecasts from 06/12 UTC cycles

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Horizontally Interpolate to CONUS LCC A-grid

Horizontally Interpolate to Hawaii LCC A-grid

Horizontally Interpolate to Alaska LCC A-grid

NAM (WRF-NMM) run

NAM POST to EGRD3D/BGRD3D

PRDGEN

CONUS CB04LCC C-grid

PreMAQAdding point/area/mobile/Biogenicemissions

CONUS CB05LCC C-grid

Hawaii CB05LCC C-grid

Alaska CB05LCC C-grid

CONUS CB04 CONUS CB05 Hawaii CB05 Alaska CB05CMAQ

Current Operational AQ processes

Static profile lateral boundary condition (LBC) is applied to these AQ runs. One experimental CB05 run (available after May 15 2010) used the LBC from the RAQMS global model (RLBC).

4Flight Altitude (m)

CalNEX Field Campaign

http://www.esrl.noaa.gov/csd/calnex/

Photographed by H. Stark

NOAA WP-3D flightsApril-June 2010

WP-3 flight on 05/18 was mainly over Southern California (Los Angeles area)

Ozone

CO

CalNEX WP-3 Flight on 5/18/2010

SO2NO2

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The CB04 and CB05 predicted similar O3 and CO concentrations in the flights mainly over California. The lateral boundary conditions have stronger impact on upper air concentrations.

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Over Southern California, the models significantly overepredicted NOy, SO2 as well as VOCs.

In CB05 NOy=NO+NO2+HNO3+PAN+PANX+HONO+PNA+NO3+NTR+N2O5*2

In CB04 NOy=NO+NO2+HNO3+PAN+HONO+PNA+NO3+NTR+N2O5*2

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O3 is highly correlated to NO2/NOx ratio when CO or VOC is high. This relationship is correctly presented by all the models

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NOz (NOy-NOx) versus O3 as the indicator of ozone production efficiency

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Same plot but for Southern California (South of 36°N, west of –116°W)

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The NOz versus O3 relationship under certain NOx/CO ranges: < 0.004, 0.004-0.04 and > 0.04

When NOx is relatively high, titration could become more important, and O3 production become less efficient.

NOx/CO<0.004: Y=21.85+24.32*X R=0.496

0.004<NOx/CO<0.04: Y=20.15+12.99*X R=0.472

NOx/CO>0.04: Y=-40.42+14.71*X R=0.284

NOx/CO<0.004: Y=39.73+5.79*X R=0.783

0.004<NOx/CO<0.04: Y=32.54+6.95*X R=0.519

NOx/CO>0.04: Y=-79.82+16.65*X R=0.190

NOx/CO<0.004: Y=40.49+6.54*X R=0.841

0.004<NOx/CO<0.04: Y=38.39+6.47*X R=0.820

NOx/CO>0.04: Y=-20.38+9.39*X R=0.385

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In term of CB04 model versus CB05 model comparison, we have their correlation coefficient rankings:O3 > total NOz >PAN

R=0.954

R=0.884

R=0.939

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PAN ratio in total NOz correlated to ambient VOC concentration (using Toluene as a representative)

The observation shows that PAN/NOz ratio is nearly proportional to the VOC concentration when high O3 is available.O3+ hv O1D + O2

O1D + H2O 2OH

R=0.88

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Summary• Over Los Angeles basin or Southern California,

the models systematically overpredicted most primarily emitted species, except CO, methanol and NH3.

• The models tend to underestimate background CO by 20-50 ppbv. Using alternative lateral boundary conditions, such as RAQMS, could help improve the CO and O3 predictions in the upper air, but it could also exaggerate the existing O3 high bias in the lower altitudes.

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Summary (Continued)

• CB04 and CB05 mechanisms show different chemical behavior in predicting the O3/NOz relationship. This difference is not caused by their treatments of ozone photochemical formation, but their predictions for speciated NOz (such as PAN).

• The flight measurements show that hydrocarbon depended NOz (like PAN) versus total NOz ratio is highly correlated to ambient hydrocarbon concentrations when O3 (as OH precursor) concentration is relatively high (>75ppbv). However, none of the models is able to capture this feature.