institute for multidimensional air quality studies (imaqs) university of houston
DESCRIPTION
Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and Regional Air Quality Models. Daewon W. Byun, Nankyoung Moon, Heejin In. Institute for Multidimensional Air Quality Studies (IMAQS) University of Houston. Daniel Jacob, Rokjin Park. Harvard University. - PowerPoint PPT PresentationTRANSCRIPT
Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and
Regional Air Quality Models
Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and
Regional Air Quality Models
Institute for Multidimensional Air Quality StudiesInstitute for Multidimensional Air Quality Studies(IMAQS)(IMAQS)
University University ofof Houston Houston
Institute for Multidimensional Air Quality StudiesInstitute for Multidimensional Air Quality Studies(IMAQS)(IMAQS)
University University ofof Houston Houston
Daewon W. Byun, Nankyoung Moon, Heejin InDaewon W. Byun, Nankyoung Moon, Heejin InDaewon W. Byun, Nankyoung Moon, Heejin InDaewon W. Byun, Nankyoung Moon, Heejin In
Harvard UniversityHarvard UniversityHarvard UniversityHarvard University
Daniel Jacob, Rokjin ParkDaniel Jacob, Rokjin ParkDaniel Jacob, Rokjin ParkDaniel Jacob, Rokjin Park
IntroductionIntroduction
It could be different at each side of domain reflecting certain regional differences.
One of key problems of regional air quality models is finding accurate initial and boundary conditions for the simulations. Distribution of surface air chemistry and PM monitoring sites is limited both in the spatial density and in the physical and chemical details.
The fixed profile BCs are never accurate and cannot account for changes due to air pollution long-range transport events.
Some US regional air quality problems may be originated from long-range transport processes(eg. Transport of EC/OC/CO/dust from Sahara & biomass burning from Central America)
Current method: Run a regional air quality model at a coarser resolution with seasonal profile data and use emissions input for a long period for the spin-up process.
What is the sensitivity of the simulations to the different IC/BCs?
Study ObjectivesStudy ObjectivesProvide tools/methods to link regional and global Provide tools/methods to link regional and global modeling systemsmodeling systems
- Dynamic Representations in Global and Regional Models- Chemical Representations in Global and Regional Models- Mechanics of Linkage
-Linkage of scales: grid structure and scales of data representation (generation of IC/BCs)-Linkage of chemical species-Linkage of dynamics
Three Areas of Inter-linkage Issues
(e.g.) Set the boundary of the domain that outside areas (e.g.) Set the boundary of the domain that outside areas do not have much direct emissions and no high do not have much direct emissions and no high concentration blobs already existingconcentration blobs already existing
LAT-LON 2 degree X 2.5 degree
20 layers in Sigma P
LAMBERT CONFORMAL
108 km X 108 km
23 layers in Sigma Po
Initial & Boundary Condition
IO/API Format in 108 km resolution
GEOS-CHEM (Goddard Earth Observing System-CHEMisrty)
MODEL3 CMAQ(Multi-pollutant Air Quality model)
Mechanics of Linkage
Linkage of scales: Currently, grid structures of the global and regional models are not “consistent”
•Requires less preferable horizontal & vertical interpolationImplementation Example
Future – requires “geocentric” coordinates(from a flat-earth to a spherical earth, if not spheroid)
Horizontal distribution of O3 concentration from GEOS-CHEM global output at Layer 1
108km resolution2 X 2.5 degree resolution
For 2000 August episode
O3-NOX-Hydrocarbon chemistry : 24 species24 species
CMAQ
MAPPING Table
CB4O3-NOx-Hydrocarbon
chemistry
[NO2 ] [NOx ]-[NO]
[O3 ] [Ox ] - [NOx ]
[N2O5] [N2O5]
[HNO3] [HNO3]
[PNA ] [HNO4]
[H2O2] [H2O2]
[CO ] [CO ]
[PAN ] [PAN ] + [PMN ] + [PPN ]
[MGLY] [MP ]
[ISPD] [MVK ] + [MACR]
[NTR ] [R4N2]
[FORM] [CH2O]
[ALD2] [ALD2] + [RCHO]
[PAR ] [ALK4] + [C3H8] + [C2H6]
[OLE ] [PRPE]
[ISOP] [ISOP]
GEOS-CHEM
CB4 : 16 species16 species
Un-used species : ACET, ALD2
Chemical species:Currently, chemical mechanisms in global and regional models are not “consistent”
Mechanics of Linkage
Mapping Table
SAPRACO3-NOx-Hydrocarbon
chemistry
[NO2 ] [NOx ] – [NO]
[PAN] [PAN]
[CO] [CO]
[ALK3] [ALK4]+[ALK5 [ALK4]
[ISOPRENE ] [ISOP]
[HNO3] [HNO3]
[H2O2] [H2O2]
[ACET ] [ACET]
[MEK] [MEK]
[CCHO] [ALD2]
[RCHO] [RCHO]
[MRTHACRO] [MACR]
[MA_PAN] [PMN]
[MVK] [MVK]
[PAN2] [PPN]
SAPRACO3-NOx-Hydrocarbon
chemistry
[RNO3] [R4N2]
[OLE1] + [OLE2] [PRPE]
[ALK2] [C3H8]
[HCHO] [CH2O]
[ALK1] [C2H6]
[N2O5] [N2O5]
[HNO4] [HNO4]
[COOH ] [MP]
CMAQ
GEOS-CHEM
SAPRAC-99
Linkage of Chemistry
Emission comparison
VOC = C2H6 + C3H8 + ALK4 + PRPE + ISOP + CH2O + ALD2 + RCHOGEOS-CHEM
VOC = PAR + 2OLE + 2ETH + 2ALD2 + 7TOL + 8XYL + 5ISOP + FORMCB4
VOC DefinitionChemical Mechanism
The emissions inputs used for the GEOS-CHEM and CMAQ for the NOx and VOC species were compared.
Table presents how total VOC in mechanisms are calculated and the values of NOx and VOC for GEIA represent smaller than NEI99-SMOKE in maximum values.
GEOS-CHEM OUTPUT, Layer-1, Ox
Summer
GEOS-CHEM OUTPUT, Top layer in CMAQ, Ox
Summer
GEOS-CHEM OUTPUT, Layer-1, OxWinter
GEOS-CHEM OUTPUT, Top layer in CMAQ, Ox
Winter
(Col:1,Row:88)
(Col:56,Row:114)
(Col:73,Row:1)
O3 and SO4 seasonal boundary condition time series
O3 time series at different vertical layers : Western Boundary
Summer Winter
O3 time series at different vertical layers : Southern Boundary
Summer Winter
O3 time series at different vertical layers : Northern Boundary
Summer Winter
SO
4
m
icro
gra
m/m
3
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Western BCSouthern BC Northern BC
June July August
SO4 seasonal boundary condition time series
Summer Winter
SO
4
m
icro
gra
m/m
3
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Western BCSouthern BC Northern BC
January February
CMAQ simulation CMAQ simulation
Emission
NEI99 ( SMOKE )
Emission
NEI99 ( SMOKE )
MET. DATA
MCIP (MM5)
MET. DATA
MCIP (MM5)
Chemical mechanism
CB4 / SAPRC99
Chemical mechanism
CB4 / SAPRC99
Domain
CONUS 36-km
Domain
CONUS 36-km
Simulation
IC & BC with Original profile dataIC & BC with GEOS-CHEM output
Simulation
IC & BC with Original profile dataIC & BC with GEOS-CHEM output
Comparison of simulated O3 concentration with AIRS
Profile Data Case GEOS-CHEM Data Case
Comparison of CMAQ results in different IC and BC (2000.08.25. 09, 21UTC)
03AM CST
03PM CST
In CMAQ simulations, the results using GEOS-CHEM output for boundary condition have smaller value from 16 ppb to 20 ppb than the results using profile data around western and northeast boundary area. On the other hand, there is opposite results at south boundary area, which is related with positive bios of GEOS-CHEM over the GULF of Mexico.It is necessary to investigate the chemical mechanism differences in CMAQ simulation with GEOS-CHEM boundary condition .
Comparison of O3 production rate
NOz (ppm)
0.000 0.005 0.010 0.015 0.020
O3
(p
pm
)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
CMAQ_SAPRC99GEOS-CHEM
NOz (ppm)
0.000 0.005 0.010 0.015 0.020
O3
(ppm
)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
PROFILE BCGEOS-CHEM
NOz (ppm)
0.000 0.005 0.010 0.015 0.020
O3
(p
pm
)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
GEOS-CHEM BCGEOS-CHEM
profile BC GEOS-CHEM BC
CMAQ/CB4 CMAQ/CB4
CMAQ/SAPRC
GEOS-CHEM BC
Comparison of wind field
This difference can be cause the uncertainty to regional air quality simulations.
MM5 NASA-GMAOGeneral patterns of wind fields are well
Some difference shows in circled area. - CMAQ/MM5 shows parallel to the grid - GEOS-CHEM/NASA-GMAO shows inflow
Let’s see how big the problem is:
GEOSCHEM : Easterly and northerlyMM5 : Clock wise rotation motion
MM5 GMAO
Study importance of the dynamic consistency Comparison of the first guess field used in MM5:
between ETA and GMAO
PREGRID MYPREGRID
REGRIDDER
EDAS DAO
Comparison of wind fields among three different MM5 results.
Case 1; MM5 results with EDAS first guess
Case 2; MM5 results with ETA first guess and GMAO objective analysis
Case 3; MM5 results with GMAO first guess
~ trying to get closer wind fields to GMAO
CASE 1 CASE 2
CASE 3 GMAO
August 25. 00 UTCMM5 Results
According to the evaluation result of numerical models, RMSE was 1.63, 1.57 and 1.41 for wind speed and 68.37, 66.66 and 69.49 for wind direction for RAMS, MM5 and Meso-Eta respectively (Zhong and Fast, 2003). In that evaluation, RMSE was for observation data and simulation results for different meteorological model outputs.
Region Simulation CaseRMSE
Wind Speed Wind Direction
Whole Domain
Case 1 1.80 60.13
Case 2 1.63 54.34
Case 3 1.99 76.62
Western BC
Case 1 2.06 41.99
Case 2 1.54 37.59
Case 3 1.39 40.94
Eastern BC
Case 1 2.03 52.74
Case 2 1.73 47.82
Case 3 1.55 41.56
Northern BC
Case 1 1.64 55.35
Case 2 1.56 47.90
Case 3 1.57 57.31
Southern BC
Case 1 1.89 52.16
Case 2 1.68 44.02
Case 3 2.54 75.12
Comparison of Root Mean Square Error (RMSE)
RMSE is for MM5 result of each case and GMAO
• Case 2 (ETA first guess and objective analysis with GMAO)
shows the most closest results to GMAO filed in three cases
from RMSE analysis.
• Even if MM5 use GMAO data for the first guess in case3,
MM5 can not simulate closer values to initial filed (GMAO)
with the lower resolution of GMAO in time(6 hourly) and
space(2X2.5).
• The best case is use of ETA data of high resolution in time and space for the first guess and use of objective analysis with GMAO data in INTERPF.
CMAQ Simulation
Emission ; EPA-NEI99
Chemical Mechanism ; CB4
Meteorology ; MM5 results of three cases
(Each case has corresponding case of MM5)Comparison of Ozone difference
CASE2 – CASE1
; (ETA_first guess & GMAO_objective analysis) – (ETA_first guess)
CASE3 – CASE1
; (GMAO_first guess) – (ETA_first guess)
CMAQ Simulation Results: Ozone Concentration DifferencesMM5 with DAO - MM5 with EDAS for August 25, 2000
Case2 – Case1Comparison of O3 difference
Biomass burning due to ENSO-related drought in Mexico and Central America during April ~ June 1998
May 13 1998
May 14 1998
May 15 1998
May 16 1998
TOMS Aerosol Index
Aerosol species Mapping
36 km × 36 kmRambert Conformal
GEOS-CHEM CMAQ
AORGI+AORGJ+AORGPAI+AORGPAJAORGBI+AORGBJ
Coordinate transformation
OC_hydrophilic + OC_hydrophobic
EC_hydrophilic + EC_hydrophobic AECI+AECJ
OC
EC
23 Sigma vertical layer(Ptop= 50 mb)
30 vertical layer(Ptop= 10 mb)
2.5°× 2°Simple
Interpolation
GEOS2CMAQ Interface
GEOS2CMAQ InterfaceICON
BCON
GEOS-CHEM Global simulation ( 2.5°× 2° )
EC OC
DATA SOURCE : US EPA NEI 99Processed with SMOKE OCEC
EMISSION
Spatial Evolutional Feature of OC
CMAQ ver 4.3
Grids : 133 × 91 × 23 Resolution : 36 km × 36 km Science Process : CB4-AERO3- EBI solver
Meteorogical data from MM5 ver3.6
A
CB
B
EC OC
SimulatedMonthly CON
Evaluation byIMPROVEMonitoring
Difference
W/ GEOS-CHEM BC
W/ fixed profile BC
Daily concentrations of Simulated vs. Observed OC
WA OR
CA NV
CO
VTFL
UT
Region A
Region B
IMPROVE Network
Improving of OC concentration
AZ TX
TNAR
Region C
A
B
C
Daily Mean CON
OCEC
P
G
Observation
Sim
ula
tio
n
R= 0.59
R= 0.33
G
P
R= 0.60
R= 0.35
EC OC
Sim
ula
tio
n
Observation
G
G
P P
R= 0.75
R= 0.33
R= 0.79
R= 0.68
Monthly concentrations of Simulated vs. Observed
ConclusionLinkage issues between global tropospheric chemistry model and regional air quality model has been studied.
We observe significant differences between profile vs. GEOSCHEM IC/BC.
To investigate the effects of using GEOS-CHEM output as initial and boundary conditions instead of the profile data on regional simulations, we have conducted 4 sensitivity CMAQ simulations with the CB4 and SAPRC99 as the chemical mechanisms.
Global-regional scale linking is the best when direct emission source is little outside the regional domain boundary; e.g., US-continental domain.
It is necessary to quantify and minimize the effects of different dynamics between the global and regional meteorological data used and to study the issues of consistency in chemical mechanisms.