Air pollution over the Ganges basin and northwest Bay of Bengal in the early postmonsoon season based on NASA MERRAero data

Abstract

The MERRA Aerosol Reanalysis (MERRAero) has been recently developed at NASA's Global Modeling Assimilation Office. This reanalysis is based on a version of the Goddard Earth Observing System-5 (GEOS-5) model radiatively coupled with Goddard Chemistry, Aerosol, Radiation, and Transport aerosols, and it includes assimilation of bias-corrected aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on both Terra and Aqua satellites. In October over the period [2002][2003][2004][2005][2006][2007][2008][2009], MERRAero showed that AOT was lower over the east of the Ganges basin than over the northwest of the Ganges basin: this was despite the fact that the east of the Ganges basin should have produced higher anthropogenic aerosol emissions because of higher population density, increased industrial output, and transportation. This is evidence that higher aerosol emissions do not always correspond to higher AOT over the areas where the effects of meteorological factors on AOT dominate those of aerosol emissions. MODIS AOT assimilation was essential for correcting modeled AOT mainly over the northwest of the Ganges basin, where AOT increments were maximal. Over the east of the Ganges basin and northwest Bay of Bengal (BoB), AOT increments were low and MODIS AOT assimilation did not contribute significantly to modeled AOT. Our analysis showed that increasing AOT trends over northwest BoB (exceeding those over the east of the Ganges basin) were reproduced by GEOS-5, not because of MODIS AOT assimilation but mainly because of the model capability of reproducing meteorological factors contributing to AOT trends. Moreover, vertically integrated aerosol mass flux was sensitive to wind convergence causing aerosol accumulation over northwest BoB.

Inter‐annual variability of aerosols and its relationship with regional climate over Indian subcontinent

“…Therefore, the EOF1 pattern of AI can be attributed to dust from Thar Desert as well as from the adjacent deserts, whereas EOF2 pattern is confined to the southern part of the NE India and IGP region, which is mostly dominated by the anthropogenic activities due to local emissions from biomass/biofuel burning, industrial activities, transport and so forth. Also, in addition to local emission sources, prevailing wind also plays an important role in aerosol distribution over eastern part of the Ganges basin (Kishcha et al , ). The time evolution of both EOF modes (PC1 and PC2) shows upward trend in aerosol loading in recent decades (Figure (g) and (h)).…”

Impact of aerosols on precipitation from deep convective clouds in eastern China

“…The spatial resolution of the data is 0.625° × 0.5° longitude by latitude, and the data length is from 2002 to the present. The evaluation and analyses of MERRAero data have been done using different satellite retrievals and observations [ Kishcha et al ., ; Buchard et al ., ; Nowottnick et al ., ]. Hourly total AOD at 550 nm data for the years 2005 to 2007 are used in this study.…”

Impact of planetary boundary layer turbulence on model climate and tracer transport

“…Aerosol optical thickness (AOT) data are available from the MODIS (Remer et al, 2005) and the Multi-angle Imaging SpectroRadiometer (MISR, Kahn et al, 2010) instruments for comparison with the free-running model. The MERRA Aerosol (MERRAero, Kishcha et al, 2014) reanalysis uses MERRA estimated meteorology, assimilates MODIS AOT data, and provides data on dust, sea salt, sulfates, and black and organic carbon. MERRAero data provide global aerosol concentrations at 3-hourly intervals.…”