Analysis of the Interaction and Transport of Aerosols with Cloud or Fog in East Asia from AERONET and Satellite Remote Sensing: 2012 DRAGON Campaigns and Climatological Data
Wednesday, 16 December 2015
Poster Hall (Moscone South)
Ground-based remote sensing observations from Aerosol Robotic Network (AERONET) sun-sky radiometers have recently shown several instances where cloud-aerosol interaction had resulted in modification of aerosol properties and/or in difficulty identifying some major pollution transport events due to aerosols being imbedded in cloud systems. Major Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaigns involving multiple AERONET sites in Japan and South Korea during Spring of 2012 have yielded observations of aerosol transport associated with clouds and/or aerosol properties modification as a result of fog interaction. Analysis of data from the Korean and Japan DRAGON campaigns shows that major fine-mode aerosol transport events are sometimes associated with extensive cloud cover and that cloud-screening of observations often filter out significant pollution aerosol transport events. The Spectral De-convolution Algorithm (SDA) algorithm was utilized to isolate and analyze the fine-mode aerosol optical depth (AODf) signal from AERONET data for these cases of persistent and extensive cloud cover. Satellite retrievals of AOD from MODIS sensors (from Dark Target, Deep Blue and MAIAC algorithms) were also investigated to assess the issue of detectability of high AOD events associated with high cloud fraction. Underestimation of fine mode AOD by the Navy Aerosol Analysis and Prediction System (NAAPS) and by the NASA Modern-Era Retrospective Analysis For Research And Applications Aerosol Re-analysis (MERRAaero) models at very high AOD at sites in China and Korea was observed, especially for observations that are cloud screened by AERONET (Level 2 data). Additionally, multi-year monitoring at several AERONET sites are examined for climatological statistics of cloud screening of fine mode aerosol events. Aerosol that has been affected by clouds or the near-cloud environment may be more prevalent than AERONET data suggest due to inherent difficulty in observing aerosol properties near clouds from remote sensing observations.