Assessment of PM2.5 retrievals using a combination of satellite AOD and WRF PBL heights in compariso...
Download
1 / 24

Lina Cordero a Nabin Malakar a , Barry Gross a , Yonghua Wu a , Fred Moshary a , Mike Ku b - PowerPoint PPT Presentation


  • 57 Views
  • Uploaded on

Assessment of PM2.5 retrievals using a combination of satellite AOD and WRF PBL heights in comparison to WRF/CMAQ bias corrected outputs. Lina Cordero a Nabin Malakar a , Barry Gross a , Yonghua Wu a , Fred Moshary a , Mike Ku b

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Lina Cordero a Nabin Malakar a , Barry Gross a , Yonghua Wu a , Fred Moshary a , Mike Ku b' - kieran-witt


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Assessment of PM2.5 retrievals using a combination of satellite AOD and WRF PBL heights in comparison to WRF/CMAQ bias corrected outputs

Lina Corderoa

NabinMalakara, Barry Grossa, YonghuaWua, Fred Mosharya, Mike Kub

a NOAA-Cooperative Remote Sensing Science and Technology Center

bNew York State Department of Environmental Conservation (NYSDEC)

12th Annual CMAS Conference

Chapel Hill, NC – October 28-30, 2013


Motivation
Motivation satellite AOD and WRF PBL heights in comparison to

  • Fine particulate matter measurements (PM2.5) are essential for air quality monitoring; however, expensive ground-based monitors limits the spatial extent of air quality networks.

  • Environmental Protection Agency (EPA) mandates 24-hr PM2.5 standard (35 ug/m3).

  • Existing Air Quality models suitable for short range forecasting at different scales exist (GEOS-CHEM, CMAQ) but these models are complex and depend strongly on meteorology and emissions.

  • On the other hand, satellites provide column integrated information that can potentially be used to estimate PM2.5. However, a wide range of factors such as aerosols variability, meteorology and the vertical structure of aerosols, affect the PM2.5-AOD relationship.

  • Supported by NYSERDA, we explore the relative performance of both approaches for the New York State domain.

12th Annual CMAS Conference


Current cmaq performance
Current CMAQ Performance satellite AOD and WRF PBL heights in comparison to

  • CMAQ 2006-2007 obtained from EPA-RSIG portal.

  • NY state region emphasized.

  • Seasonal bias observed (especially Fall / Summer).

  • How does this compare to satellite based approaches?

12th Annual CMAS Conference


Current satellite approach
CURRENT Satellite Approach satellite AOD and WRF PBL heights in comparison to

  • GEOS-CHEM (global model) estimates the spatial relationship between PM2.5 forecast and column path AOD on a daily basis.

  • Currently being used by IDEA (Infusing satellite Data into Environmental air quality Applications) providing real time spatial maps of PM2.5 [van Donkelaar (2012)].

  • Combines MODIS Terra-AQUA data to generate average AOD.

  • PM2.5/AOD ratios from the GEOS-CHEM are obtained at low resolution.

  • Additional processing using inverse distance weighting (IDW) spatial interpolation to improve satellite coverage and bias corrections against field stations are applied.

12th Annual CMAS Conference


Problems with current approach
Problems with Current approach satellite AOD and WRF PBL heights in comparison to

  • GEOS-CHEM low resolution (0.5 x 0.5 degree) misses interesting urban/suburban transitions.

  • Clear spatial patterns in CMAQ PM2.5/AOD ratio connected to higher resolution land surface/emission inventories are important.

  • Strong differences seem to occur in the magnitude of the PM2.5/AOD ratio for the different approaches.

12th Annual CMAS Conference


Local study case
Local study case satellite AOD and WRF PBL heights in comparison to

  • Assessment of PM2.5 estimation in a local study for better understanding critical factors under more controlled conditions.

  • Use of neural network (NN) for potential PM2.5-AOD non-linearity.

  • Explore the effect of other factors along with AOD as NN regressors.

  • Explore the effect of seasonal variability.

  • AOD retrievals from AERONET.

  • PBL from direct Lidar observations.

  • Meteorology from weather stations.

  • PM2.5 from TEOM monitor at CCNY.

12th Annual CMAS Conference


Additional factors assessment
Additional factors Assessment satellite AOD and WRF PBL heights in comparison to

  • PBL height found to be the most relevant variable.

  • Temperature shows some sensitivity (most likely indicative of the co-varying nature between temperature and PBL height).

12th Annual CMAS Conference


Seasonality assessment
Seasonality assessment satellite AOD and WRF PBL heights in comparison to

  • Further improvements can be observed if data are restricted by seasons. Biggest improvement in PBL height addition is observed in summer, consistent with stronger convective conditions associated with urban heat island.

12th Annual CMAS Conference


Regional study case
Regional study case satellite AOD and WRF PBL heights in comparison to

  • Local experiments indicate sensitivity of PM2.5 estimator to PBL height under certain conditions.

  • Therefore, it is reasonable to apply satellite AOD and WRF PBL heights into the NN to evaluate PM2.5 estimations.

  • Combined MODIS Terra and Aqua AOD.

  • WRF PBL data available from the EPA-RSIG portal.

  • AIRNow PM2.5 used to train the NN with focus on New York state.

12th Annual CMAS Conference


Preliminary pm2 5 products assessment
Preliminary pm2.5 products assessment satellite AOD and WRF PBL heights in comparison to

  • NN returns best estimations compared to in-situ measurements against other two approaches compared on the same dataset.

  • GEOS-CHEM product returns the largest errors which mainly come from fine PM overestimations (overly strong PM25/AOD ratios).

12th Annual CMAS Conference


Seasonality assessment1
Seasonality assessment satellite AOD and WRF PBL heights in comparison to

  • Smaller but positive improvements seen when WRF PBL is added to NN across all seasons.

  • Issues such as inaccurate WRF PBL heights need to be accounted for.

  • Satellite approaches seem to generally outperform CMAQ in study except spring / summer.

  • No comparisons in winter because of lack of satellite measurements.

  • Satellite approaches are limited to clear sky conditions but spatial processing can be used to improve spatial coverage.

12th Annual CMAS Conference


Implementing nn for pm2 5 mapping
Implementing NN for PM2.5 mapping satellite AOD and WRF PBL heights in comparison to

  • Use daily AOD and WRF PBL to illustrate our NN performance. However, cloud cover can notably reduce spatial coverage.

  • Iteratively apply IDW averages of the data to improve spatial coverage while using a 0.1 degree radial domain.

12th Annual CMAS Conference


Implementing nn for pm2 5 mapping1
Implementing NN for PM2.5 mapping satellite AOD and WRF PBL heights in comparison to

  • Good agreement between station and estimations data with low PM2.5 values observed in the non-urban region while high fine PM values are observed in the metropolitan area.

12th Annual CMAS Conference


Improving cmaq forecasts
Improving CMAQ forecasts satellite AOD and WRF PBL heights in comparison to

  • In current comparisons, satellite approach has the potential to improve on existing CMAQ forecasts under clear sky conditions.

  • However, CMAQ model approaches are still extremely valuable for all weather retrievals.

  • Therefore, CMAQ outputs can be improved regionally by quantifying bias sources and using machine learning tools (such as NN) to adjust for these biases.

  • Parameters such as relative humidity (RH), temperature, PBL height, pressure, wind-speed, urban / non-urban are explored.

12th Annual CMAS Conference


Relevant variables performance
Relevant Variables / performance satellite AOD and WRF PBL heights in comparison to

  • Most important meteorological / environmental variables found based on correlations between PM2.5 model bias and variables.

  • Top 4 cases used for NN building / testing.

  • Results fairly stable to number of NN nodes .

  • Training based on 70% of data and remaining 30% for testing (all points included in regressions).

  • Strongest improvement when adding temperature into model to correct bias.

R value between variable and bias

Performance for different case runs

12th Annual CMAS Conference


Cmaq bias dependence on meteorology
CMAQ Bias dependence on meteorology satellite AOD and WRF PBL heights in comparison to

  • Clear improvements although outliers still observed for some high in-situ values.

  • If all variables added, significant reduction in bias and RMSE spread is also observed.

  • One issue to assess is the robustness of the NN.

  • Does the testing data performance have the same quality?

12th Annual CMAS Conference


Robustness of nn 2 cases
Robustness of NN (2 cases) satellite AOD and WRF PBL heights in comparison to

Testing and training results nearly identical (Robust)

12th Annual CMAS Conference


Conclusions
conclusions satellite AOD and WRF PBL heights in comparison to

  • Adding lidar derived PBL improved PM2.5 estimations for local CCNY site.

  • Combining satellite AOD and WRF PBL height in a regionally trained NN performed better with much less over-bias at low PM2.5 values compared against GEOS-CHEM ratio approach used in IDEA

  • Daily PM2.5 maps based on the NN approach using high resolution AOD and PBL grids for the NY state region (applying IDW) showed reasonableagreement with station data.

  • On the other hand, we find that significant improvement can be made to CMAQ PM2.5 by performing bias correction on the model outputs.

  • Temperature was the dominant bias factor but additional variables such as RH, wind-speed and PBL height also contributed.

  • NN results were shown to be robust by comparing Testing and Training sets which performed very similarly.

12th Annual CMAS Conference


Acknowledgments
acknowledgments satellite AOD and WRF PBL heights in comparison to

  • This project was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership Program award NA11SEC4810004 as well as NYSERDA under grant # 22885. Its contents are solely the responsibility of the award recipient and do not necessarily represent the official views of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration (NOAA) or New York State Energy Research and Development Authority (NYSERDA).

12th Annual CMAS Conference


Questions

Questions ?? satellite AOD and WRF PBL heights in comparison to

12th Annual CMAS Conference


Additional slides

Additional Slides satellite AOD and WRF PBL heights in comparison to


References
references satellite AOD and WRF PBL heights in comparison to

  • Boyouk, N., J. F. Leon, H. Delbarre, T. Podvin and C. Deroo, 2010: Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness. J Atmos. Environ., 44, 271-277.

  • Hoff, R.M. and S. A. Christopher, 2009: Remote sensing of particulate pollution from space: have we reached the promised land? J. Air. & Waste Manage. Assoc., 59, 645-675.

  • Hogrefe. C., P. Doraiswamy, B. Colle, K. Demerjian, W. Hao, M. Beauharnois, M. Erickson, M. Souders and J.-Y. Ku, 2011: Effects of Grid Resolution and Perturbations in Meteorology and Emissions on Air Quality Simulations Over the Greater New York City Region. 10th Annual CMAS Conference, Chapel Hill, NC.

  • Hu, X., J. W. Nielsen-Gammon and F. Zhang, 2010: Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model. J. Appl. Meteor. Climatol., 49, 1831–1844.

  • Pope, C. A., III, R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito, et al. 2002: Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. of the American Medical Association, 287, 1132−1141.

  • T.-C. Tsai, et al. 2009: Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008.  Atmos. Environ. 45, 4777-4788.

  • U. S. Environmental Protection Agency. Air quality criteria for particulate matter. 2004. EPA/600/P-99/002aF, Research Triangle Park, N. C.

  • van Donkelaar, A., R. V. Martin, A. N. Pasch, J. J. Szykman, L. Zhang, Y. X. Wang and D. Chen, 2012: Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America. Environ. Science & Technology,46, 11971-11978.

  • Zhang, H., R. M. Hoff and J. A. Engel-Cox, 2009: The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by EPA regions. J. Air & Waste Manage. Assoc.59, 1358-1369.

12th Annual CMAS Conference


Additional satellite products
additional satellite products satellite AOD and WRF PBL heights in comparison to

  • NN trained with satellite variables.

  • Highest correlation with in-situ PM2.5 in comparison to the other methods.

  • Further investigations infusing surface as well as other meteorological information are ongoing

12th Annual CMAS Conference


Various combination of satellite and cmaq
Various Combination of satellite AOD and WRF PBL heights in comparison tosatellite and cmaq

12th Annual CMAS Conference


ad