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
2
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
5
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
7
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
10
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
11
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
Office of Research and Development
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
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
Office of Research and Development
Atmospheric Modeling & Analysis Division, National Exposure Research Laboratory
17
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
18
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