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

Youhua Tang 1,2 , Jeffery T. McQueen 2 , Jianping Huang 1,2 , Marina Tsidulko 1,2 , Sarah Lu 1,2 , Ho-Chun Huang 1,2 , Stuart A. McKeen 3 , Daewon Byun 4 , Pius Lee 4 , R. Bradley Pierce 5 , Ivanka Stajner 6 , Thomas B. Ryerson 3 , Rebecca Washenfelder 3 , Jeff Peischl 3 , John S. Holloway 3 , David D. Parrish 3 , James M. Roberts 3 , Joost de Gouw 3 , and Carsten Warneke 3 1. IMSG, Camp Springs, MD 20746, USA 2. Environmental Modeling Center, NOAA National Centers for Environmental Prediction, 5200 Auth Road, Camp Springs, MD 20746, USA 3. NOAA Earth System Research Laboratory, Boulder, CO 80305, USA 4. NOAA Air Resource Laboratory, Silver Spring, MD 5. NOAA NESDIS/ORA, Madison, WI 6. Office of Science and Technology, NOAA National Weather Service, Silver Spring, MD

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 aerosol Output available on National Digital Guidance Database 48 hour forecasts from 06/12 UTC cycles CONUS “5x” Domain Eastern US “3x” Domain FY 06-07 268 grid cells 442 grid cells 2

NAM (WRF-NMM) run NAM POST to EGRD3D/BGRD3D Current Operational AQ processes PRDGEN Horizontally Interpolate to CONUS LCC A-grid Horizontally Interpolate to Hawaii LCC A-grid PreMAQ Adding point/area /mobile/ Biogenic emissions CONUS CB04 LCC C-grid CONUS CB05 LCC C-grid CMAQ CONUS CB04 CONUS CB05 Hawaii CB05 LCC C-grid Hawaii CB05 Horizontally Interpolate to Alaska LCC A-grid Alaska CB05 LCC C-grid Alaska CB05 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).

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CalNEX Field Campaign http://www.esrl.noaa.gov/csd/calnex/ Photographed by H. Stark NOAA WP-3D flights April-June 2010 Flight Altitude (m) 4

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

NO 2 CalNEX WP-3 Flight on 5/18/2010 SO 2

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The CB04 and CB05 predicted similar O 3 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 NO y , SO 2 as well as VOCs.

In CB05 NO y =NO+NO 2 +HNO 3 +PAN+PANX+HONO+PNA+NO 3 +NTR+N 2 O 5 *2 In CB04 NO y =NO+NO 2 +HNO 3 +PAN+HONO+PNA+NO 3 +NTR+N 2 O 5 *2

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O 3 is highly correlated to NO 2 /NO x ratio when CO or VOC is high. This relationship is correctly presented by all the models 10

NO z (NO y -NO x ) versus O 3 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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NOx/CO<0.004: Y=21.85+24.32*X R=0.496

0.004

: Y=20.15+12.99*X R=0.472

NOx/CO>0.04

: Y=-40.42+14.71*X R=0.284

The NO z versus O 3 relationship under certain NO x /CO ranges: < 0.004, 0.004-0.04 and > 0.04

When NO x is relatively high, titration could become more important, and O 3 production become less efficient.

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

0.004

: 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

: 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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R=0.954

R=0.939

R=0.884

In term of CB04 model versus CB05 model comparison, we have their correlation coefficient rankings: O 3 > total NOz >PAN

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PAN ratio in total NOz correlated to ambient VOC concentration (using Toluene as a representative) R=0.88

The observation shows that PAN/NOz ratio is nearly proportional to the VOC concentration when high O 3 available.

O 3 + hv  O 1 D + O 2 O 1 D + H 2 O  2OH 15 is

Summary

• Over Los Angeles basin or Southern California, the models systematically overpredicted most primarily emitted species, except CO, methanol and NH 3 .

• 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 O 3 predictions in the upper air, but it could also exaggerate the existing O 3 high bias in the lower altitudes.

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Summary (Continued)

• CB04 and CB05 mechanisms show different chemical behavior in predicting the O 3 /NO z relationship. This difference is not caused by their treatments of ozone photochemical formation, but their predictions for speciated NO z (such as PAN). • The flight measurements show that hydrocarbon depended NO z (like PAN) versus total NO highly correlated to ambient hydrocarbon z ratio is concentrations when O 3 (as OH precursor) concentration is relatively high (>75ppbv). However, none of the models is able to capture this feature. 17