1 Assimilate Column and Surface Data for Air Quality Forecasting and Chemical Reanalysis 13 th JCSDA May13-15, 2015, College Park Pius Lee 1 , Greg Carmichael 2 , Yongtao Hu 3 , Edward Hyer 4 , Yang Liu 5 , Dick McNider 6 , Brad Pierce 7 , Robert Atlas 8 , Ted Russell 3 , Sid Boukabara 9 , Youhua Tang 1,10 , Hyuncheol Kim 1,10 , Li Pan 1,10 , Sean Casey 9 and Daniel Tong 1,10 1 Air Resources Laboratory / NOAA, NCWCP, College Park, MD 2 College of Engineering, University of Iowa, Iowa City, IA 3 School of Civil and Environmental Engr., Georgia Institute of Technology, Atlanta, GA 4 Naval Research Laboratory, Monterey, CA 5 Department of Environmental Health, Emory University, Atlanta, GA 6 Department of Atmospheric Science, University Alabama, Huntsville AL 7 National Environmental Satellite and Information Service (NESDIS), Madison, WI 8 Atlantic Oceanographic & Meteorological Lab., Miami, FL 9 NOAA/NESDIS/Joint Center for Satellite Data Assimilation (JCSDA), College Park, MD 10 Cooperative Institute for Climate and Satellite, University of Maryland
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1 Assimilate Column and Surface Data for Air Quality Forecasting and Chemical Reanalysis 13 th JCSDA May13-15, 2015, College Park Pius Lee 1, Greg Carmichael.
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Assimilate Column and Surface Data for Air Quality Forecasting and Chemical Reanalysis
13th JCSDA May13-15, 2015, College Park
Pius Lee1, Greg Carmichael2, Yongtao Hu3, Edward Hyer4, Yang Liu5, Dick McNider6, Brad Pierce7, Robert Atlas8, Ted Russell3,
Sid Boukabara9, Youhua Tang1,10, Hyuncheol Kim1,10, Li Pan1,10, Sean Casey9 and Daniel Tong1,10
1Air Resources Laboratory / NOAA, NCWCP, College Park, MD 2College of Engineering, University of Iowa, Iowa City, IA
3School of Civil and Environmental Engr., Georgia Institute of Technology, Atlanta, GA4Naval Research Laboratory, Monterey, CA
5Department of Environmental Health, Emory University, Atlanta, GA6Department of Atmospheric Science, University Alabama, Huntsville AL
7National Environmental Satellite and Information Service (NESDIS), Madison, WI8Atlantic Oceanographic & Meteorological Lab., Miami, FL
9NOAA/NESDIS/Joint Center for Satellite Data Assimilation (JCSDA), College Park, MD 10Cooperative Institute for Climate and Satellite, University of Maryland College Park, MD
Courtesy: Dan Costa “New Directions in Air Quality Research at the US EPA”
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Dr. L. Uccellini, May14 2015 “Impact-based DSS”
13th JCSDA May13-15, 2015, College Park
Gridded forecast guidance products• On NWS servers: airquality.weather.gov and ftp-
Widely used data assimilation schemes in Air Quality (AQ):
3DVAR e.g. in NCEPEnsemble Adjustment Kalmar Filter (KF) e.g. in trained Bias Correction schemesOptimal Interpolation: is a truncated extended KF
Continuity equation for species φi in generalized co-ordinate(Byun and Schere 2006)
13th JCSDA May13-15, 2015, College Park
Optimal Interpolation (OI)OI simplifies the extended Kalman filter formulation
(Dee et al. Q. J. R. Meteor. Soc. 1998) by limiting the analysis problem to a subset of obs.
Obs far away (beyond background error correlation length scale) have no effect in the analysis.
Injection of Obs through OI takes place at 1800 UTC daily.
)()( 1 HXYOHBHBHXX TTba
713th JCSDA May13-15, 2015, College Park
Estimate Model Error Statistics w/ Hollingsworth-Lonnberg Method
• At each data point, calculate differences between forecasts (B) and observations (O)
• Pair up data points, and calculate the correlation coefficients between the two time series
• Plot the correlation as a function of the distance between the two stations,
Alternatively 3DVar: M. Pagowski Q.J.R.Meteor. 2010 13th JCSDA May13-15, 2015, College Park
MODIS AOD & AIRNow PM2.5 assimilated
For initial condition adjustment
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00Z 06Z 12Z 19Z
AIRNOW PM2.5, PM10, Ozone
MODIS AOD (Terra and Aqua)
14Z 17Z
18Z
13th JCSDA May13-15, 2015, College Park
Total AOD assimilation tests
Day
AO
DR
MS
E
0 1 2 3 4 5 6 7 8 9 10 11 12 130
0.05
0.1
0.15
0.2
0.25
Base caseOI forecastOI analysis
Base case: CMAQ4.7.1 without any data assimilationOI forecast: CMAQ results after assimilating previous day AOD observationsOI Analysis: CMAQ results after assimilating same day AOD, for next day forecast
Day
AO
DR
MS
E
0 1 2 3 4 5 6 7 8 9 10 11 12 130
0.05
0.1
0.15
0.2
0.25
Base caseOI forecastOI analysis
Fine mode AOD assimilation Total AOD assimilation
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18Z
17Z 20Z OI: MODIS total AOD OI: AIRNow pm2.5
13th JCSDA May13-15, 2015, College Park
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Transmittance
AIRNow
Cloud-obs Photolysis rates
Isoprene& PAR
done
done
done
Yet to do
13th JCSDA May13-15, 2015, College Park
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Analysis field Verification: AIRNOW O3 & PM 2.5 over NE
13th JCSDA May13-15, 2015, College Park
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WRF 3.2.1 for meteorological fields• NCEP North American Regional Reanalysis
(NARR) 32-km resolution inputs• NCEP ADP surface and soundings observational
data • MODIS landuse data for most recent land cover
status• 3-D and surface nudging, Noah land-surface
modelSMOKE 2.6 for CMAQ ready gridded
emissions • NEI inventory projected to 2011 using EGAS growth and
existing control strategies • BEIS3 biogenic emissions based on BELD3 database• GOES biomass burning emissions:
ftp://satepsanone.nesdis.noaa.gov/EPA/GBBEP/ CMAQ 4.6 revised to simulate gaseous & PM
species• SAPRC99 mechanism, AERO4, ISORROPIA
thermodynamic, Mass conservation,• Updated SOA module (Baek et. al. JGR 2011) for
multi-generational oxidation of semi-volatile organic carbons
Analysis fields as IC and forecast with Total AOD Assimilation basis for LBCs
12 km
36 km
4 km
IC/BC of Base and AOD_DA cases
DISCOVER-AQGA_Tech4 km
13th JCSDA May13-15, 2015, College Park
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Analysis field Verification: AIRNOW O3 & PM 2.5 over BW SIP
13th JCSDA May13-15, 2015, College Park
PM2.5 24h avg Obs mean Mean bias RMSE Corr. Coef.
L42 12.3 -7.7 10.4 0.4
L42RAQMS 12.3 -5.4 8.5 0.5
L42-Fire1 12.3 -7.6 10.6 0.3
L42-Fire1-OI4 12.3 -1.11 6.65 0.61
CONUS
PM2.5 24h avg Obs mean Mean bias RMSE Corr. Coef.
Base 19.8 -0.67 24.18 0.49
With L42-Fire1-OI4 field to derive LBC
19.8 1.15 5.79 0.53
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DISCOVER-AQGA_Tech4 km
Application of analysis field for State Implementation Planning
operational in July-August 2012, with addition of Special Sensor Microwave Imager Sounder (SSMIS)-F16,F17,F18
• Random-errors added to all radiance observations using modified version of R. Errico’s (GMAO) error-addition code
• Two week spin-up, 47-day experiment period (20050716-20050831)• AIRS
• As CONTROL but w/AIRS_G13 (AIRS instrument in the location of GOES-13, 75°W)
• Simulated from T511 NR by Z. Li, U. Wisconsin, using Stand Alone Radiation Transfer Algorithm (SARTA) (compared to CRTM for JCSDA-simulated radiances)
• Random-errors added using expected error distribution for AIRS_AQUA
Atlas et al. BAMS 2011 doi:2010BAMS2946.1
13th JCSDA May13-15, 2015, College Park
Preliminary VSDB Results
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• Right: RMSE for 500 hPa geopotential height (forecast hour on horizontal axis)
• Lower figure: difference between mean RMSE, AIRS-CONTROL
• Red boxes: 95% confidence interval; counts outside these bounds are considered statistically significant
• Comparisons done with respect to T511 NR
• Here, the experiment with AIRS_G13 shows significant reduction in RMSE for days 1, 2
• Full results can be viewed on JCSDA website: http://www.jcsda.noaa.gov/vsdb/users/scasey/prs382hwa/vsdb.php
• I created a “rough scorecard” based on the Version 16 results (next slide)
Spatial distribution of correlation coefficient for surface O3 on Aug 3,5 2005
13th JCSDA May13-15, 2015, College Park
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Configured AQ forward-model vertical structure matching that of GFS The reanalysis forward model is tested and used to generate July 2011 analysis fields for MDE for SIP modeling – GATech partnered SIP modelers Data Set assimilated: RAQMS (MLS, OMI O3, MODIS AOD); HMS Fire; GOES
cloud fraction for photolytic rate correction; MODIS AOD; AIRNow O3, PM2.5
Next sets: lightning NOx; OMI SO2; PAR adjustment by retrievals Verification to include data from O3 lidar network Begin transition of assimilation algorithm to GSI Production mode generation of analysis field for 2010 (HTAP Collaboration)OSSE accomplishment: Performed GFS-WRF-CMAQ sensitivity runs to investigate AIRS-assisted
GFS AIR-assisted GFS-WRF-CMAQ does improve surface O3 and PM2.5
forecast The degree of improvement is small compared to Chemical Data
assimilation on CMAQ The Chemical Data Assimilation (CDA) on PM2.5 benefits stronger than that