Evaluation of CMAQ using Satellite Remote Sensing Measurements

1
Evaluation of CMAQ using Satellite Remote Sensing
Measurements

• Krish Vijayaraghavan 1,
• Jianlin Hu 2, Yang Zhang 2,
• Xiong Liu 3, Kelly Chance 3, and Hilary E. Snell
  1
• 1 Atmospheric Environmental Research, Inc.
  (AER)
• 2 North Carolina State University
• 3 Harvard-Smithsonian Center for Astrophysics
• CMAS Conference, Oct 16-18, 2006
• Chapel Hill, NC

2
Study Overview

• Advantages of satellite retrievals vs. in-situ
  measurements
• More complete spatial coverage
• Vertically-integrated measure of air quality
• Measure of long-range transport
• Our earlier studies have compared satellite
  retrievals with CMAQ calculations of tropospheric
  columns of NO2/CO/HCHO, O3 residuals, and aerosol
  optical depth (Vijayaraghavan et al., 2006
  Zhang et al., 2005, 2006)
• Here we compare CMAQ results with satellite
  retrievals of tropospheric column ozone and
  carbon monoxide vertical profiles

3
CMAQ Application

• CMAQ version 4.4
• Simulation period 2001
• Modeling domain N. America
• Grid resolution 36 km x 36 km, 14 layers (up to
  15 km)
• Meteorology, Emissions, ICs/BCs from U.S. EPA
• Meteorology MM5-driven
• Emissions 1999 NEI MOBILE6/BEIS
  3.12/SMOKE1.4
• ICs/BCs from global model (GEOS-Chem)
• 10 day CMAQ spinup

4
Satellite Retrievals of Carbon monoxide from
MOPITT on Terra Satellite
MOPITT Measurements of Pollution in the
Troposphere (Emmons et al, 2004) Instrument
measures infrared radiation directed upwards from
the Earths surface CO mixing ratio profiles and
total column amounts are retrieved from the
radiances Horizontal resolution of 1x1o
(Level-3) Vertical resolution of 3-4 km CO
profiles at seven vertical pressure levels 1000
hPa, 850 hPa, 700 hPa, 500 hPa, 350 hPa, 250 hPa,
and 150 hPa
(Figure from http//www.space.gc.ca)
5
Averaging Kernels

• The averaging kernel represents the way in
  which the vertical
• structure of the atmosphere is mapped into
  the measured radiances
• x' A xtrue (I A) xa (Deeter et al.,
  2002)
• x' CO retrieved profile from MOPITT
• A Averaging Kernel matrix
• xtrue True CO profile
• I Identity matrix
• xa a priori CO profile
• MOPITT a priori CO profile obtained from
  global aircraft datasets (up to
• 400 mb) and MOZART simulated values of CO
  (at higher altitudes)
• Averaging Kernel A I CxCa-1
• Cx Retrieved error covariance matrix Ca
  a priori covariance matrix

6
CMAQ Retrievals ofVertical Profiles of Carbon
monoxide

• MOPITT has a finite vertical resolution
• CO retrieval assigned to a given vertical level
  includes information from a range of altitudes
  above and below the reported level
• Valid comparisons of model with retrievals must
  include a transformation
• of the model output into a vertically
  averaged quantity at each level
• Steps for converting CMAQ output before
  comparison to MOPITT
• Resampling Interpolate CMAQ output to MOPITT
  pressure grid
• Kernel Calculation Calculate averaging kernel
  using Cx and Ca
• Transformation Calculate a pseudo-retrieval x
  'CMAQ using the
• a priori , interpolated CMAQ values, and the
  averaging kernel

7
Annual Average CO Mixing Ratiosat Two Vertical
Pressure Levels
CMAQ
MOPITT
at 1000 hPa
at 250 hPa
CO mixing ratios from CMAQ and MOPITT are both
shown after applying averaging kernels
8
Annual Average CO Vertical Profiles
Chicago, IL
Los Angeles, CA
Houston, TX
9
Seasonal Average CO Vertical Profilesat Los
Angeles, CA
Winter (Jan, Dec)
Fall (Sep, Oct, Nov)
Summer (Jun, Jul, Aug)
Spring (Mar, Apr, May)
10
Statistical Performance of Annual Average CO
Profiles from CMAQ
Layer (hPa) Mean MOPITT (ppb) Mean CMAQ (ppb) Number Mean Bias (ppb) MeanError (ppb) NMB () NME () Corr.Coeff
1000 140.0 133.7 14898 -6 12 -5 9 0.95
850 131.2 123.7 16071 -8 10 -6 8 0.79
700 115.5 109.8 16072 -6 7 -5 6 0.83
500 100.5 98.1 16072 -2 4 -2 4 0.90
350 96.6 95.4 16072 -1 3 -1 3 0.91
250 86.4 86.1 16072 0 2 0 3 0.90
150 65.9 66.1 16072 0 2 0 3 0.89
All 104.8 101.5 111329 -3 6 -3 5 0.96
NMB Normalized mean bias NME Normalized mean
error
11
Satellite Retrievals ofTropospheric Column Ozone
(TCO)
Residual Method for calculating TCO

• O3 residual (TCO) Total column O3
  Stratospheric column O3 (SCO)
• Disadvantage
• Often assumes the distribution and variability of
  SCO

Direct retrieval of TCO

• The first directly retrieved global distribution
  of TCO from Global Ozone Monitoring Experiment
  (GOME) measurements was presented by Liu et al.
  (2005, 2006)

12
Global Ozone Monitoring Experiment (GOME)on the
ERS-2 Satellite
(Figure from http//earth.esa.int)

• GOME measures backscattered radiance spectra
  from the Earths
• atmosphere in 240-790 nm range
• High signal to noise ratio in the UV ozone
  absorption bands gt Can retrieve the
• tropospheric O3 vertical distribution (Chance
  et al., 1991, 1997 Liu et al., 2006)
• In this study, 2001 profiles of partial column
  O3 are retrieved at 24 layers with
• the tropopause as one of the levels
• Horizontal resolution 960 km x 80 km

13
Tropospheric Column Ozone (TCO)from CMAQ
f (O3,i, Ti, Pi, Dzi)

• CMAQ TCO calculated up to layer N
• N corresponds to the tropopause in the GOME
  retrievals
• N f (Location, Time)
• TCO in Dobson units (DU)
• Lat/Lon of GOME retrieval mapped to the CMAQ 36
  km grid
• GOME TCO sum of tropospheric partial columns
  up to the
• tropopause
• CMAQ TCO compared with GOME TCO

14
Annual Average of TCO
15
Trends in Monthly Averages at 3 Locations
16
Statistical Performance of CMAQ TCO
Variables Winter Spring Summer Fall Annual
   
Mean GOME (DU) 29.1 38.9 41.4 31.5 36.3
Mean CMAQ (DU) 36.3 46.5 38.3 35.1 39.2
Number 4674 8390 11272 9466 33802
Mean Bias (DU) 7.2 7.6 -3.1 3.6 2.9
Mean Error (DU) 8.3 10.5 10.0 6.4 8.9
NMB 25 20 -7 11 7
NME 29 27 24 20 25
Corr. Coeff. 0.29 0.31 0.02 0.22 0.26
NMB Normalized mean bias NME Normalized
mean error
17
Possible Reasons for Discrepanciesbetween CMAQ
and GOME TCO

• Uncertainty in CMAQ ozone boundary conditions
• CMAQ TCO calculated to the layer nearest the
  tropopause
• and not to the exact tropopause pressure
• Uncertainty in GOME retrievals and limited
  vertical
• resolutions
• GOME Averaging kernels (AK) were not applied to
  CMAQ
• TCO. Applying AK to CMAQ results after
  augmenting with
• other high-resolution data above the tropopause
  should
• improve model performance

18
Conclusions

• Satellite measurements offer some advantages
  over in-situ measurements
• for evaluating the performance of chemistry
  transport models such as CMAQ.
• CO vertical profiles and tropospheric column
  ozone (TCO) simulated by
• CMAQ at a 36 km horizontal resolution over the
  U.S. in 2001 were
• evaluated using MOPITT CO and GOME TCO
  satellite retrievals.
• CO vertical profiles and spatial distributions
  from CMAQ after applying
• averaging kernels are typically comparable to
  those from MOPITT
• (over all layers error 5, bias -3, r
  0.96).
• TCO from CMAQ exhibits a low positive bias and
  moderate error with respect
• to GOME, but correlation is low (annual avg.
  error 25, bias 7, r 0.26).
• Possible reasons for discrepancies between CMAQ
  and satellite retrievals
• include uncertainties in CMAQ inputs and
  species retrieval, limitations in
• satellite retrievals, and not applying
  averaging kernels to CMAQ TCO.

19
Acknowledgements

• Study funded by NASA Award No. NNG04GJ90G
• Research at the Smithsonian Astrophysical
  Observatory
• was supported by NASA and the Smithsonian
  Institution
• Input files from U.S. EPA
• (Kenneth Schere, Warren Peters, George Pouliot)

20
Evaluation of CMAQ using Satellite Remote Sensing
Measurements

• Krish Vijayaraghavan 1,
• Jianlin Hu 2, Yang Zhang 2,
• Xiong Liu 3, Kelly Chance 3, and Hilary E. Snell
  1
• 1 Atmospheric Environmental Research, Inc.
  (AER)
• 2 North Carolina State University
• 3 Harvard-Smithsonian Center for Astrophysics
• CMAS Conference, Oct 16-18, 2006
• Chapel Hill, NC

21
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