Use of VIIRS Aerosol Products in a Regional Air Quality Model

Report
Use of Remote Sensing Information in
Regional Air Pollution Modeling:
Examples and Potential Use of VIIRS Products
Rohit Mathur
Atmospheric Modeling and Analysis Division, NERL, U.S. EPA
Acknowledgements: George Pouliot, Xing Jia, Robert Gilliam, Jon Pleim
VIIRS Aerosol Science and Operational Users Workshop, November 21-22, 2013, College Park, MD
Office of Research and Development
Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory
Motivation
• Applications of regional AQ models are continuously being extended to
address pollution phenomenon from local to hemispheric spatial scales
over episodic to annual time scales
• The need to represent interactions between physical and chemical
processes at these disparate spatial and temporal scales requires use of
observational data beyond traditional surface networks
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Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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Use of Remote Sensing Information
in Regional AQMs
• Evaluation/Verification of model results
– High spatial resolution over large geographic regions of remote sensing data
is attractive
• Improve estimates of model parameters
– Emissions (e.g, wildland fires, trends/accountability)
– Key meteorological parameters (e.g., SST)
– Lateral Boundary conditions (LRT effects)
– Location and effects of clouds (e.g., photolysis)
• Chemical data assimilation
– Improving short-term air quality forecasts
– Identification of model deficiencies
• Data Fusion/Reanalysis: combining model and observed fields
– For use in health, exposure and ecological studies (2012 NRC Report on
Exposure Science in the 21st Century)
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Improving Model Parameter Estimates:
Fire Emissions
Courtesy: A. Soja
Surface PM2.5 : June 10-17, 2008
Observed
No Fire
NEI-Smartfire
New Estimate
Fire detects have greatly helped with more accurate
spatial allocation of emissions, but challenges remain:
• Injection height/vertical distribution
• Emission factors (the new approach used soil carbon
content)
• Ground fire detection
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Courtesy: George Pouliot
4
Diagnosing Model Performance
Significant under-estimation:
July 19-24
Large under-estimation (>2x)
in OC in mid-July
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Evidence of Long-range transport from outside the modeled domain
Model picks up spatial signatures ahead of the front, but under-predictions behind the front (LBCs)
Further Evidence
7/13/04
7/14/04
7/15/04
7/16/04
Long Range Transport of Alaskan Plume
7/17/04
Distribution of measured carbonaceous
aerosol at STN sites within domain
Regional enhancement in TCM on July 17-20
suggests influence of wildfires on air masses
advected into the domain
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Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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Estimating the Impacts of Alaskan fires through
Assimilation of Satellite AOD Retrievals
Methodology
1.
Model based correlation between AOD and column
PM burden (July-August, 2004 data):
–
[PM]Col.Burden = f(AOD)
•
[PM]col. Burden = 9.065 AOD + 0.18 (r2 =0.9)
2.
Estimate inferred PM2.5 burden:
–
[PM]infer = f(AODMODIS)
3.
Estimate Difference in PM mass loading:
–
[PM]infer – [PM]BaseModel
Adjust Model Initial Conditions
16Z on July 19, 2004
Distribute PM2.5 mass difference vertically between
predefined altitudes
4.
•
Above BL: 2.2 – 4 km (based on Regional
East Atmospheric Lidar Mesonet (REALM)
data); layers 14-16
Speciation: EPA AP-42 emission factors for wildfires: OC
5.
(77%), EC(16%), SO42- (2%), NO3- (0.2%), Other(4.8%)
•
CO/PM2.5 = 10
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Mathur, 2008 (JGR)
8
Representing the 3D Transport Signature of the Alaskan Plume
CO Comparisons with NASA DC-8 Measurements during ICARTT
Assim-Base; 1700Z
Enhanced CO associated with concurrently enhanced acetonitrile (CH3CN) – chemical marker for BB
Assimilation helps improve the model predicted CO distributions
July 19-23, 04
Impact on Surface PM2.5 Model Performance
• Reduced Bias/Error
• Improved Correlation
July 20, 04
STN
AIRNOW
MODIS AOD Assimilation:
Domain median surface levels enhanced by 23 - 42% due to Alaskan fires on different days
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Air Quality – Climate Interactions
Establishing Confidence in Simulated Magnitude and Direction of Aerosol Feedbacks
• Large changes in emissions and
tropospheric aerosol burden have
occurred over the past two decades
– Title IV of the CAA achieved significant
reductions in SO2 and NOx emissions
– Large increase in emissions in Asia over the
past decade
1989-1991
2007-2009
• Is the signal (magnitude and direction)
detectable in the observations?
50
aerosol loading and associated radiative
effects?
• Can the associated increase in surface
solar radiation be detected in the
measurements (“brightening effect”) and
be used to constrain model results?
SO2 annual emission (Tg)
• Can models capture past trends in
US
China
OECD+Central Europe
40
30
20
10
0
1990
1995
2000
2005
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Air Quality – Climate Interaction
Trend in Aerosol Optical Depth (AOD)
2000
JJA-average
MODIS+ SeaWiFS
 MODIS - level 3 Terra
 SeaWiFS - level 3 Deep
Blue
 Missing value in MODIS
(mostly in Sahara Desert)
was filled by SeaWiFS
(550nm)
WRF-CMAQ (sf)
533nm
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Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
2009
Trend in Aerosol Optical Depth (AOD)
WRF-CMAQ(sf)
MODIS+ SeaWiFS
JJA-average
(2009 minus 2000)
East China
from 1990 to 2009
East US
Europe
Trend in clear-sky shortwave radiation
JJA-average (2009 minus 2000)
WRF-CMAQ(sf)
WRF-CMAQ(nf)
Dimming
brightening
CERES
East China
from 1990 to 2009
East US
Europe
Improving Model Parameter Estimates:
Sea Surface Temperature
July 1-31: GHRSST - PathFinder
GHRSST
RMSE Change
T-2m
Increase in
Error
Reduction in
Error
•1-km horizontal resolution
global dataset
• Daily
RMSE and bias reduced with GHRSST. Reduction is
even greater compared to NAM 12-km SST data.
 Implications for representing Bay Breeze and
pollutant transport
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RMSE
Change
63%
Long-Range Transport and
“Background” Pollution Levels
• Added diagnostic tracers to track impact of lateral boundary conditions: surface-3km
(BL) and 3km-model top (FT)
– Quantify modeled “background” O3
Average: July-August, 2006
Modeled “background” O3
• Significant spatial variability
“FT” contribution to model background
• Background could constitute a
sizeable fraction of more stringent NAAQS
Accurate representation of aloft pollution critical for simulation of surface “background”
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Representing Impacts of Long-Range Transport
Transport of Saharan Dust: Summer 2006
Texas Sites
Surface PM concentration in the Gulf states
impacted by LRT during July 30-Aug 3
Regional Model Driven by
Hemispheric LBCs
Dust Transport: 850 mb
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Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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Representing Impacts of Long-Range Transport
Impact on Model Performance: July 30-August 3, 2006
Bias Difference:
Base LBCs Hemis. LBCs
Lower bias
in Hemis.
Lower bias
in Base
Vertically varying (time-dependent) LBCs are needed to accurately
quantify impacts of LRT on episodic regional pollution as well as
“background” pollution
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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Summary
• Air quality remote sensing data is useful for model evaluation and
improvements
– What level of quantitative agreement is acceptable?
– Need for harmonization between assumptions used in retrieval and
CTM process algorithms (e.g., AOD, NO2 columns) for more rigorous
quantitative use
• Columnar distributions are a good starting point, but there is a
need for better vertical resolution
– Discern between BL and FT
• Measurements to characterize transport aloft (and subsequent
downward mixing next morning) are needed
• Improving the characterization of FT predictions in regional AQMs
will result in improvements in surface-level predictions
• Potential for use in chemical data assimilation
– Simultaneous information on multiple chemical species
– Combining model and observed information on the chemical state of
the atmosphere has potential for both human-health and climate
relevant endpoints
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
19

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