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 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
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)
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
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.
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