Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States

Abstract We use surface fine particulate matter (PM2.5) measurements collected by the United States Environmental Protection Agency (US EPA) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks as independent validation for Version 1 of the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero) developed by the Global Modeling Assimilation Office (GMAO). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua satellites. By combining the spatial and temporal coverage of GEOS-5 with observational constraints on AOD, MERRAero has the potential to provide improved estimates of PM2.5 compared to the model alone and with greater coverage than available observations. Importantly, assimilation of AOD data constrains the total column aerosol mass in MERRAero subject to assumptions about optical properties for each of the species represented in GOGART. However, single visible wavelength AOD data does not contain sufficient information content to correct errors in either aerosol vertical placement or composition, critical elements for a proper characterization of surface PM2.5. Despite this, we find that the data-assimilation equipped version of GEOS-5 better represents observed PM2.5 between 2003 and 2012 compared to the same version of the model without AOD assimilation. Compared to measurements from the EPA-AQS network, MERRAero shows better PM2.5 agreement with the IMPROVE network measurements, which are composed essentially of rural stations. Regardless the data network, MERRAero PM2.5 are closer to observation values during the summer while larger discrepancies are observed during the winter. Comparing MERRAero to PM2.5 data collected by the Chemical Speciation Network (CSN) offers greater insight on the species MERRAero predicts well and those for which there are biases relative to the EPA observations. Analysis of this speciated data indicates that the lack of nitrate emissions in MERRAero and an underestimation of carbonaceous emissions in the Western US explains much of the reanalysis bias during the winter. To further understand discrepancies between the reanalysis and observations, we use complimentary data to assess two important aspects of MERRAero that are of relevance to the diagnosis of PM2.5, in particular AOD and vertical structure.

[1]  M. Brauer,et al.  Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter , 2014, Environmental health perspectives.

[2]  Dick Dee,et al.  Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology , 1999 .

[3]  D. Jacob,et al.  Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. , 2005, Environmental science & technology.

[4]  Dan Chen,et al.  The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States , 2014 .

[5]  R. Koster,et al.  The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4. Volume 38; Technical Report Series on Global Modeling and Data Assimilation , 2015 .

[6]  Becky Alexander,et al.  Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations , 2010 .

[7]  James R. Campbell,et al.  Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis , 2014 .

[8]  R. Martin,et al.  Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing , 2006 .

[9]  Ping Yang,et al.  Response of Aerosol Direct Radiative Effect to the East Asian Summer Monsoon , 2015, IEEE Geoscience and Remote Sensing Letters.

[10]  M. Brauer,et al.  Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application , 2010, Environmental health perspectives.

[11]  Lorraine A. Remer,et al.  Impact of satellite viewing-swath width on global and regional aerosol optical thickness statistics and trends , 2013 .

[12]  Richard Neale,et al.  Toward a Minimal Representation of Aerosols in Climate Models: Description and Evaluation in the Community Atmosphere Model CAM5 , 2012 .

[13]  Lorraine Remer,et al.  Machine Learning and Bias Correction of MODIS Aerosol Optical Depth , 2009, IEEE Geoscience and Remote Sensing Letters.

[14]  Jeffrey S. Reid,et al.  MODIS aerosol product analysis for data assimilation: Assessment of over‐ocean level 2 aerosol optical thickness retrievals , 2006 .

[15]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[16]  Robert C. Levy,et al.  Remote sensing of surface visibility from space: A look at the United States East Coast , 2013 .

[17]  Arlindo da Silva,et al.  An adaptive buddy check for observational quality control , 2013 .

[18]  Zhiquan Liu,et al.  Assimilating aerosol observations with a “hybrid” variational‐ensemble data assimilation system , 2014 .

[19]  D. Winker,et al.  Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms , 2009 .

[20]  N. Krotkov,et al.  Evaluation of GEOS-5 sulfur dioxide simulations during the Frostburg, MD 2010 field campaign , 2013 .

[21]  J. Reid,et al.  Impact of data quality and surface-to-column representativeness on the PM 2.5 / satellite AOD relationship for the contiguous United States , 2013 .

[22]  D. Winker,et al.  Initial performance assessment of CALIOP , 2007 .

[23]  Yafang Cheng,et al.  Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations , 2014 .

[24]  M. Chin,et al.  Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth , 2010 .

[25]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[26]  S. Gong,et al.  A parameterization of sea‐salt aerosol source function for sub‐ and super‐micron particles , 2003 .

[27]  P. Gupta,et al.  Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach , 2009 .

[28]  R. Purser,et al.  Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances , 2002 .

[29]  Richard H. Moore,et al.  Factors that influence surface PM 2.5 values inferred from satellite observations: perspective gained for the US Baltimore-Washington metropolitan area during DISCOVER-AQ , 2013 .

[30]  P. Gupta,et al.  Particulate Matter Air Quality Assessment using Integrated Surface, Satellite, and Meteorological Products , 2009 .

[31]  Mark R. Schoeberl,et al.  Transport of smoke from Canadian forest fires to the surface near Washington, D.C.: Injection height, entrainment, and optical properties , 2004 .

[32]  Ricardo Todling,et al.  The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0 , 2008 .

[33]  Jun Wang,et al.  Intercomparison between satellite‐derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies , 2003 .

[34]  P. Colarco,et al.  Use of the CALIOP vertical feature mask for evaluating global aerosol models , 2014 .

[35]  Zhijin Li,et al.  A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM 2.5 prediction , 2012 .

[36]  D. Dockery,et al.  Air pollution and life expectancy in China and beyond , 2013, Proceedings of the National Academy of Sciences.

[37]  Teruyuki Nakajima,et al.  Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements , 2002 .

[38]  Wayne C. Welch,et al.  Airborne high spectral resolution lidar for profiling aerosol optical properties. , 2008, Applied optics.

[39]  Majid Ezzati,et al.  Fine-particulate air pollution and life expectancy in the United States. , 2009, The New England journal of medicine.

[40]  M. Chin,et al.  Sources and distributions of dust aerosols simulated with the GOCART model , 2001 .

[41]  W. Malm,et al.  Spatial and seasonal trends in particle concentration and optical extinction in the United States , 1994 .

[42]  Jassim A. Al-Saadi,et al.  Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization , 2006 .

[43]  W. Malm,et al.  Uncertainties in PM2.5 Gravimetric and Speciation Measurements and What We Can Learn from Them , 2011, Journal of the Air & Waste Management Association.

[44]  P. Alpert,et al.  Air pollution over the Ganges basin and northwest Bay of Bengal in the early postmonsoon season based on NASA MERRAero data , 2014 .

[45]  Raymond M Hoff,et al.  Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality , 2004, Journal of the Air & Waste Management Association.

[46]  G. Leeuw,et al.  Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands , 2008 .