Simultaneous three-dimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth

• Published in: Journal of Geophysical Research: Atmospheres 2012-07, Vol.117 (D13), p.1L-n/a
• Main Authors: Schwartz, Craig S., Liu, Zhiquan, Lin, Hui-Chuan, McKeen, Stuart A.
• Format: Article
• Language: English
• Subjects: 3DVAR aerosol data assimilation; Aerosols; AOD data assimilation; Data collection; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geophysics; Optical analysis; Particulate matter; PM2.5 data assimilation
• ISSN: 0148-0227; 2169-897X; 2156-2202; 2169-8996
• DOI: 10.1029/2011JD017383

Total 550 nm aerosol optical depth (AOD) retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface fine particulate matter (PM2.5) observations were assimilated with the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three‐dimensional variational (3DVAR) data assimilation (DA) system. Parallel experiments assimilated AOD and surface PM2.5observations both individually and simultaneously. New 3DVAR aerosol analyses were produced every 6 h between 0000 UTC 01 June and 1800 UTC 14 July 2010 over a domain encompassing the continental United States. The analyses initialized Weather Research and Forecasting‐Chemistry (WRF‐Chem) model forecasts. Assimilating AOD, either alone or in conjunction with PM2.5 observations, produced better AOD forecasts than a control experiment that did not perform DA. Additionally, individual assimilation of both AOD and PM2.5 improved surface PM2.5 forecasts compared to when no DA occurred. However, the best PM2.5 forecasts were produced when both AOD and PM2.5 were assimilated. Considering the goodness of both AOD and PM2.5 forecasts, the results unequivocally show that concurrent DA of PM2.5 and AOD observations produced the best overall forecasts, illustrating how simultaneous DA of different aerosol observations can work synergistically to improve aerosol forecasts. Key Points Simultaneous 3DVAR data assimilation of MODIS AOD and surface PM2.5 observations Aerosol data assimilation substantially improves aerosol forecasts Forecasts are best when both AOD and surface PM2.5 are assimilated concurrently