A Case Study Using the CMAQ Coupling with Global Dust Models

Youhua Tang, Pius Lee, Marina Tsidulko, Ho-Chun Huang, Sarah Lu, Dongchul KimScientific Applications International Corporation, Camp Springs, Maryland

Jeffery T. McQueen, Geoffrey J. DiMegoNOAA/NWS/National Centers for Environmental Prediction, Camp Springs, Maryland.

Robert B. PierceNOAA/NESDIS Advanced Satellite Products Branch, Madison, Wisconsin

Patricia K. Quinn, Timothy S. Bates NOAA Pacific Marine Environmental Laboratory, Seattle, WA

Hsin-Mu Lin, Daiwen Kang, Daniel Tong, Shao-cai YuScience and Technology Corporation, Hampton, VA.

Rohit Mathur, Jonathan E. Pleim, Tanya L. Otte, George Pouliot, Jeffrey O. Young, Kenneth L. Schere EPA National Exposure Research Laboratory, Research Triangle Park

Paula M. DavidsonOffice of Science and Technology, NOAA/National Weather Service, Silver Spring, MD

Ivanka StajnerNoblis Inc, Falls Church, VA

Objective

• Current operational WRF-NMM/CMAQ forecast still uses static profile lateral boundary condition (LBC). Our testing shows dynamic ozone LBCs from global models have significant impact on air quality prediction in upper and middle troposphere. What is the impact on particulate matter prediction?

• During Texas Air Quality Study 2006, the model inter-comparison team found all 7 regional air quality models missed some high-PM events that can not be reasonably interpreted with any local or regional factors. Here we revisit these events by coupling a regional model with global models.

WRF-NMM/CMAQ Model Configuration

• Driven by hourly meteorological forecasts from the operational North America Mesoscale (NAM) WRF-NMM prediction system.

• The operational CMAQ system covering Continental USA in 12km horizontal resolution

Carbon Bond Mechanism-4 (CBM4) with AERO3

22 vertical layers up to 100hPa.

vertical diffusivity and dry deposition based on Pleim and Xu (2001),

scale J-table for photolysis attenuation due to cloud

Asymmeric Convective Scheme (ACM) (Pleim and Chang, 1992).

RAQMS(Real-time Air Quality

Modeling System, Pierce et al, 2003)

GFS-GOCART (offline dust)

Horizontal Resolution

22 T126 (~1x1)

Meteorology GFS analysis GFS retrospective run

Anthropogenic emissions

GEIA/EDGAR with updated Asian emission

(Streets et al. 2003)Not active

Biomass burning emissions

ecosystem/severity based

Not active

3-D Var Data Assimilation

OMI/TES/MODIS assimilation

Not applicable

Input frequency to CMAQ

Every 6 hours Every 3 hours

Global Models as CMAQ LBC Providers

GOCART has 5 dust bins in diameter:

0.2-2 μm, 2-4 μm, 4-6 μm, 6-12 μm, 12-18 μm

Which are mapped into CMAQ with

PM2.5=bin1+0.4187*bin2

PM_Coarse=0.5813*bin2+bin3+0.7685*bin4

GFS-GOCART prediction for a dust intrusion event around Aug 2, 2006

Surface weather map on July 28, 2006

Dust Intrusion Path

GFS-GOCART and RAQMS exhibit differences in altitude and concentration of dust along the eastern lateral boundary of CMAQ that causes differences in PM prediction over Texas

GFS-GOCART LBC

RAQMS LBC

Dust Intrusion Path

CMAQ surface PM2.5 (μg/m3) Compared to AIRNOW at 18Z, 08/02/2006

GFS-GOCART LBC RAQMS LBC

Comparison for surface stations over Texas

Florida

Julian day 212 is July 31

The NOAA ship Ron Brown measurements also showed the dust signal in marine boundary layer.

07/27 07/29 07/31 08/02 08/04 08/06 08/08 08/10 08/12 08/14D a tes (U T C )

0

20

40

60

80

100

Pre

dic

ted

/Ob

serv

ed D

ust

PM

10 (g

/m3 )

CMAQ with GFS-GOCART LBCCMAQ with RAQMS LBCObserved Dust PM10 Mass

07/27 07/29 07/31 08/02 08/04 08/06 08/08 08/10 08/12 08/14D a tes (U T C )

0

4

8

12

16

Ob

serv

ed T

otal

Si,

Ca,

Fe

(g/

m3 )

Elemental SiElemental CaElemental Fe

Ron Brown dust mass is calculated as

[Dust] = 2.2[Al] + 2.5[Si] + 1.63[Ca] + 2.2[Fe] + 1.9[Ti]

This equation includes a 16% correction factor to account for the presence of oxides of other elements such as K, Na, Mn, Mg, and V. Also, the equation omits K from biomass burning by using Fe as a surrogate for soil K and an average K/Fe ratio of 0.6 in soil. (Malm et al., JGR, 99, 1347, 1994)

Another method to use GFS-GOCART output: CMAQ base + GOCART DUST PM2.5 (Bin1+0.4187Bin2)

Aug 17

Aug 18

Aug 19Aug 20

Aug 21Aug 22

Aug 23

Aug 25

Aug 285 km

3 km

CALIPSO images provided by Dave Winker

GFS-GOCART prediction for another dust intrusion event around Aug 28, 2006

Another intrusion event around Aug 28

CMAQ predictions compared to Ron Brown data

08/22 08/24 08/26 08/28 08/30 09/01 09/03 09/05 09/07D a tes (U T C )

0

2

4

6

8O

bse

rved

Tot

al S

i, C

a, F

e (

g/m

3 )

0

5

10

15

20

25

Pre

dic

ted

Du

st P

M10

(g

/m3 )

CMAQ with GFS-GOCART LBCCMAQ with RAQMS LBCElemental SiElemental CaElemental Fe

All Stations South of 38N, East of -105W

CMAQ baseS=0.418 R=0.462

MB= -4.65S=0.301 R=0.431

MB= -7.94

CMAQ with GFS-GOCART LBC

S=0.607 R=0.538MB= -2.98

S=0.709 R=0.542 MB= -4.11

CMAQ with RAQMS LBC

S=0.458 R=0.402 MB= -2.25

S=0.386 R=0.480 MB= -6.64

CMAQ +GOCARTS=1.092 R=0.492

MB= -0.783S=1.828 R=0.458

MB= 1.93

Model simulations compared to AIRNOW hourly PM2.5 data

All Stations South of 38N, East of -105W

CMAQ baseS=0.339 R=0.273

MB= -3.24S=0.270 R=0.336

MB= -6.08

CMAQ with GFS-GOCART LBC

S=0.396 R=0.315MB= -2.73

S=0.375 R=0.447 MB= -5.36

CMAQ with RAQMS LBC

S=0.494 R=0.289 MB= -1.10

S=0.326 R=0.281 MB= -3.97

CMAQ +GOCARTS=0.459 R=0.347

MB= -2.46S=0.492 R=0.480

MB= -4.34

Period of 20060729 to 20060807

Period of 20060827 to 20060902

S is regression slope, R is correlation coefficient, and MB is mean bias in μg/m3

Other than the circled cases, the regional predictions coupled with global models show improvement over the CMAQ base prediction.

Summary• Appropriate LBCs are necessary for successful regional

PM prediction during dust intrusion events. For summer 2006 events, dust LBC sometimes dominated the influence on regional PM prediction.

• The model results shows that there is strong sensitivity of the surface PM prediction to the entry height of the dust intrusion. (elevated lower troposphere versus near surface)

• These coupling experiments mainly reflect the long-range transport impact on certain local receptors. The model prediction can be very sensitive to accuracies of dynamical, physical and chemical processes in both global and regional models.