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

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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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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

)

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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

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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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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

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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”

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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

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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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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

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