Schwartz, Craig S., National Center for Atmospheric
ResearchÕs Earth System Laboratory/Mesoscale and Microscale Meteorology
Division, Zhiquan Liu, NCAR Earth System Laboratory/Mesoscale and Microscale
Meteorology Division, Hui-Chuan Lin, NCAR Earth System Laboratory/Mesoscale and
Microscale Meteorology Division, Stuart A. McKeen, National Oceanic and
Atmospheric Administration
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 PM2.5
observations both individually and simultaneously. New 3DVAR aerosol analyses were produced every 6-hrs 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 WRF-Chem
forecasts of AOD 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 WRF-Chem aerosol forecasts,
illustrating how simultaneous DA of different aerosol observations can work
synergistically to yield forecast improvements.