Tianfeng Chai 1,2 Pius Lee1, Hyun-Cheol Kim1,2 and Li Pan1,2
1NOAA
OAR/ARL, NCWCP, College Park, MD
2Cooperative Institute for Climate and Satellites, University of Maryland, College
Park, MD
Yongtao Hu3, Talat Odman3, Ted Russell3
3Department
of Civil and Environmental Science, Georgia Institute of Technology
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Introduction
 CMAQ PM2.5 predictions are inferior to ozone predictions in
NAQFC experimental slot during previous years.
 Preliminary tests in assimilating AIRNow PM2.5 using GSI
protocol showed improvement, but impact short-lived.
 Our earlier MODIS AOD assimilation attempt using Optimal
Interpolation shows minimal impact on PM2.5 predictions
 This study represents an upgraded MODIS AOD assimilation
attempt with 4 new treatments. It showed promising results
Poster: Pius Lee – 4 km resolution AQ forecasting to support DISCOVER-AQ SJV
TTP: Jim Szykman – Open Geospatial Consortium (OGC) compliant web tool
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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CMAQ AOD assimilation setup
•Model:
CMAQ 4.7.1,
•Domain:
CONUS, 442x265 grid, 12 km, 22 layers
•Observations:
Terra AOD (total and fine mode)
•Time period:
Jul. 1, 2011 – Jul. 12, 2011
•Assimilation method: Optimal Interpolation
Major updates:
1. Updated model from CMAQ4.6 to CMAQ4.7.1 and
removed Raleigh scattering from CMAQ AOD (~0.15).
2. AOD retrieval re-gridding method was upgraded.
3. Applied constraint from AOD assimilation at 17 UTC daily
throughout the simulation period of 13 days.
4. Both fine mode and total AOD assimilation were
attempted
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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National correlation map between AIRNow measurement and MODIS AOD
Typically good correlation between
surface PM2.5 and AOD retrieved
by MODIS
MODIS (Moderate Resolution Imaging
Spectroradiometer) AOD
Orbit:
Swath
Dimensions:
Spatial
Resolution:
705 km, 10:30 a.m. descending
node (Terra) or 1:30 p.m.
ascending node (Aqua)
2330 km (cross track) by 10 km
(along track at nadir)
250 m (bands 1-2)
500 m (bands 3-7)
1000 m (bands 8-36)
Courtesy :NESDIS
http://terra.nasa.gov/About/
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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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.
X a = X b + BH T ( HBH T + O) −1 (Y − HX )
 Obs far away (beyond background error correlation
length scale) have no effect in the analysis.
 Injection of Obs through OI takes place at 1700 UTC
daily.
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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MODIS total and fine mode AOD
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Horizontal Error Statistics
AOD error statistics results w/ NMC
AOD error statistics results through
Hollingsworth-Lönnberg approach
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Scaling factors (analysis/forecast ) are used to
adjust 49 parameters in 22 layers
 ASO4I, ANO3I, ANH4I, AORGPAI,
AECI, ACLI (6)
 ASO4J, ANO3J, ANH4J, AORGPAJ,
AECJ, ANAJ, ACLJ, A25J (8)
 AORGAT: AXYL1J, AXYL2J, AXYL3J,
ATOL1J, ATOL2J, ATOL3J, ABNZ1J,
ABNZ2J, ABNZ3J, AALKJ, AOLGAJ
(11)
 AORGBT: AISO1J, AISO2J, AISO3J,
ATRP1J, ATRP2J, ASQTJ, AOLGBJ (7)
 AORGCT: AORGCJ (1)
 ASO4K, ANO3K, ANH4K, ANAK,
ACLK, ACORS, ASOIL (7)
 NUMATKN, NUMACC, NUMCOR (3)
 SRFATKN, SRFACC, SRFCOR (3)
 AH2OJ, AH2OI, AH2OK (3)
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Schematics of the OI Data Assimilation method
Observation
Input
7/2/2011
Background
Input
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
OI
Analysis output
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Snapshots at injection of analysis fields on 7/4/11
Base Case
Forecast
Analysis
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Snapshots at injection of analysis fields on 7/9/11
Base Case
Forecast
Analysis
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Total AOD assimilation tests
Base case: CMAQ4.7.1 without any data assimilation
OI forecast: CMAQ results after assimilating previous day AOD observations
OI Analysis: CMAQ results after assimilating same day AOD, for next day forecast
Fine mode AOD assimilation
0.05
Total AOD assimilation
0.05
Base case
OI forecast
OI analysis
0
AOD Bias
AOD Bias
0
-0.05
-0.1
-0.15
-0.1
Base case
OI forecast
OI analysis
0
1
2
3
4
5
6
7
8
9
10
11
12
-0.05
13
-0.15
0
1
2
Day
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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4
5
6
7
8
9
10
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12
Day
12
13
Total AOD assimilation tests
Base case: CMAQ4.7.1 without any data assimilation
OI forecast: CMAQ results after assimilating previous day AOD observations
OI Analysis: CMAQ results after assimilating same day AOD, for next day forecast
0.25
0.2
0.2
AOD RMSE
AOD RMSE
Fine mode AOD assimilation
0.25
0.15
0.1
Base case
OI forecast
OI analysis
0.05
0
0
1
2
3
4
5
6
Total AOD assimilation
0.15
0.1
Base case
OI forecast
OI analysis
0.05
7
8
9
10
11
12
13
0
0
1
2
Day
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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4
5
6
7
8
9
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Day
13
13
PM2.5 Predictions after AOD assimilation
20
18
Mean Obs: CONUS and daily
averaged AIRNow
observations
16
PM2.5 (ug/m3)
14
Mean_base: Base case
without assimilation
12
10
8
6
Mean Obs
Mean_base
Mean_OI_f
Mean_OI_t
4
2
0
182
184
186
188
190
Mean_OI_f: After
assimilation of fine mode
AOD
Mean_OI_t: After
assimilation of total AOD
192
Jday
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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Reanalysis fields as IC and forecast with Total AOD Assimilation basis for LBCs
WRF 3.2.1 for meteorological fields
4 km
•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 model
SMOKE 2.6 for CMAQ ready gridded emissions
12 km
36 km
•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 multigenerational oxidation of semi-volatile organic carbons
Afternoon Nov 29thTalk by Ted Russell: CMAQ results during DISCOVER-AQ
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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0.0
% NOx emission
change w.r.t. days of
week and holiday
over July 2011
-5.0
-10.0
-15.0
-20.0
-25.0
-30.0
%
NOx
Mon
-25.8
Tue
-18.9
Wed
-17.3
Thur
-17.1
Fri
-18.9
0.0
% NOx emission change
w.r.t. regions
over July 2011
Sat
-3.7
Sun
-8.1
4-Jul
-12.0
-5.0
-10.0
-15.0
Duncan et al. “Idiot’s Guide ..”
-20.0
Evaluate GOME-2 obs
Poster: Pius Lee – GOES-R
% -25.0
NOx
4th
Conus
North EastSouth East
Upper Middle
Lower Middle
Rocky Mountain
Pacific Coast
-15.7
AQAST Meeting, Sacramento, CA November 29-30, 2012
-16.2
-17.1
-20.7
-11.4
-16.1
-18.8
Summary and future work
 Assimilating MODIS AOD using OI method is able to
improve AOD predictions/forecasts
 AOD assimilation helps improve PM2.5 simulations
 In terms of their impact on PM2.5 predictions during the
test period, assimilation of total AOD has a better
performance than assimilation of fine mode AOD
 Assimilation of both AIRNow surface measurement and
MODIS AOD will be tested
 Utilization of aerosol speciation and their vertical profiles
in chemical data assimilation will be explored
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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BACKUP SLIDES
4th AQAST Meeting, Sacramento, CA November 29-30, 2012
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CMAQ AOD from Mie & Recon
1.2
AOD_Recon
1
0.8
0.6
0.4
AOD_Recon = 1.07 * AOD_Mie - 0.017
0.2
R^2 = 0.859, R= 0.927
0
0
0.2
0.4
0.6
0.8
AOD_Mie
4th
AQAST Meeting, Sacramento, CA November 29-30, 2012
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1.2
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