Transcript Use of VIIRS Aerosol Products in a Regional Air Quality Model
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
Office of Research and Development
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 21 st Century
)
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Improving Model Parameter Estimates: Fire Emissions
Courtesy: A. Soja Surface PM 2.5
: 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
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Courtesy: George Pouliot
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Significant under-estimation: July 19-24
Diagnosing Model Performance
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Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory 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 Long Range Transport of Alaskan Plume 7/16/04 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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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
[PM] = f(AOD) col. Burden = 9.065 AOD + 0.18 (r 2 =0.9)
2.
3.
4.
– Estimate inferred PM 2.5
[PM] infer = f(AOD
burden:
MODIS )
– Estimate Difference in PM mass loading:
[PM] infer – [PM]
BaseModel • Distribute PM 2.5
mass difference vertically between predefined altitudes Above BL: 2.2
data
– 4 km ( ); layers 14-16
based on Regional East Atmospheric Lidar Mesonet (REALM)
5.
• Speciation: EPA AP-42 emission factors for wildfires:
OC (77%), EC(16%), SO 4 2 (2%), NO 3 (0.2%), Other(4.8%) CO/PM 2.5
= 10
Adjust Model Initial Conditions 16Z on July 19, 2004 Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Mathur, 2008 (JGR)
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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 (CH 3 CN) – chemical marker for BB
Assimilation helps improve the model predicted CO distributions
MODIS AOD Assimilation: Impact on Surface PM 2.5
Model Performance
• •
Reduced Bias/Error Improved Correlation Domain median surface levels enhanced by 23 - 42% due to Alaskan fires on different days Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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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 SO 2 and NO x emissions – Large increase in emissions in Asia over the past decade •
Is the signal (magnitude and direction) detectable in the observations?
•
Can models capture past trends in 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?
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1989-1991 2007-2009
50 40 30 20 10 0 1990 US China OECD+Central Europe 1995 2000 2005
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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
Air Quality – Climate Interaction
Trend in Aerosol Optical Depth (AOD)
2000
JJA-average
2009
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Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Trend in Aerosol Optical Depth (AOD)
MODIS+ SeaWiFS WRF-CMAQ(sf)
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
)
CERES WRF-CMAQ(sf) WRF-CMAQ(nf)
East China from
1990 to 2009
East US Europe
Improving Model Parameter Estimates: Sea Surface Temperature
July 1-31: GHRSST - PathFinder
RMSE Change T-2m
GHRSST
•1-km horizontal resolution global dataset • Daily
RMSE Change 63%
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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” O 3
Average: July-August, 2006
Modeled “background” O 3
“FT” contribution to model background
• Significant spatial variability • 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 Office of Research and Development
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
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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, NO quantitative use 2 columns) for more rigorous •
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
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