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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. - PowerPoint PPT Presentation
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Office of Research and DevelopmentAtmospheric Modeling and Analysis Division, National Exposure Research Laboratory
Use of Remote Sensing Information in Regional Air Pollution Modeling: Examples and Potential Use of VIIRS Products
Rohit MathurAtmospheric Modeling and Analysis Division, NERL, U.S. EPA
VIIRS Aerosol Science and Operational Users Workshop, November 21-22, 2013, College Park, MD
Acknowledgements: George Pouliot, Xing Jia, Robert Gilliam, Jon Pleim
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
2
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 DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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 DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
4
Improving Model Parameter Estimates: Fire Emissions
Courtesy: A. Soja
Surface PM2.5 : June 10-17, 2008
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
Courtesy: George Pouliot
ObservedNo FireNEI-SmartfireNew Estimate
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
5
Significant under-estimation:July 19-24
Large under-estimation (>2x)in OC in mid-July
Diagnosing Model Performance
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)
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
7
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
Further Evidence
7/14/04 7/15/04 7/16/047/13/04
Long Range Transport of Alaskan Plume
7/17/04
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
8
Estimating the Impacts of Alaskan fires through Assimilation of Satellite AOD Retrievals
Methodology1. 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
4. Distribute PM2.5 mass difference vertically between predefined altitudes
• Above BL: 2.2 – 4 km (based on Regional East Atmospheric Lidar Mesonet (REALM) data); layers 14-16
5. Speciation: EPA AP-42 emission factors for wildfires: OC (77%), EC(16%), SO4
2- (2%), NO3- (0.2%), Other(4.8%)
• CO/PM2.5 = 10
Adjust Model Initial Conditions 16Z on July 19, 2004
Mathur, 2008 (JGR)
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
Representing the 3D Transport Signature of the Alaskan Plume
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
10
MODIS AOD Assimilation: Impact on Surface PM2.5 Model Performance
• Reduced Bias/Error• Improved Correlation
AIR
NO
WJu
ly 1
9-23
, 04
STN
July
20,
04
Domain median surface levels enhanced by 23 - 42% due to Alaskan fires on different days
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Air Quality – Climate InteractionsEstablishing 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
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• Can models capture past trends in aerosol loading and associated radiative effects?
1989-1991 2007-2009
• Can the associated increase in surface solar radiation be detected in the measurements (“brightening effect”) and be used to constrain model results?
0
10
20
30
40
50
1990 1995 2000 2005
SO2
annu
al e
mis
sion
(Tg)
USChinaOECD+Central Europe
• Is the signal (magnitude and direction) detectable in the observations?
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Trend in Aerosol Optical Depth (AOD)2000 2009
MODIS+ SeaWiFS
WRF-CMAQ (sf)
JJA-average
MODIS - level 3 Terra SeaWiFS - level 3 Deep
Blue Missing value in MODIS
(mostly in Sahara Desert) was filled by SeaWiFS (550nm)
533nm
Air Quality – Climate Interaction
Trend in Aerosol Optical Depth (AOD)
MODIS+ SeaWiFS WRF-CMAQ(sf)
East China East US Europe
JJA-average
(2009 minus 2000)
from 1990 to 2009
Trend in clear-sky shortwave radiation
CERES
East China East US Europe
JJA-average (2009 minus 2000)
WRF-CMAQ(sf)
WRF-CMAQ(nf)
brig
hten
ing
Dim
min
g
from 1990 to 2009
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
RMSE Change
63%
July 1-31: GHRSST - PathFinder
Red
uctio
n in
Er
ror
Incr
ease
in
Erro
r
T-2mRMSE Change
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
GHRSST• 1-km horizontal resolution
global dataset• Daily
Improving Model Parameter Estimates: Sea Surface Temperature
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
• Added diagnostic tracers to track impact of lateral boundary conditions: surface-3km (BL) and 3km-model top (FT)– Quantify modeled “background” O3
“FT” contribution to model background
Average: July-August, 2006
Modeled “background” O3
Accurate representation of aloft pollution critical for simulation of surface “background”
Long-Range Transport and “Background” Pollution Levels
• Significant spatial variability• Background could constitute a sizeable fraction of more stringent NAAQS
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Representing Impacts of Long-Range TransportTransport of Saharan Dust: Summer 2006
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Surface PM concentration in the Gulf states impacted by LRT during July 30-Aug 3
Texas Sites
Dust Transport: 850 mb
Regional Model Driven by Hemispheric LBCs
Office of Research and DevelopmentAtmospheric Modeling & Analysis Division, National Exposure Research Laboratory
Representing Impacts of Long-Range TransportImpact on Model Performance: July 30-August 3, 2006
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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 DevelopmentAtmospheric 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
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