Discrimination of biomass burning smoke and clouds in MAIAC algorithm

Abstract

Abstract. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the "smoke test" to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 µm as compared to 0.47-0.67 µm due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements.

Validation of high‐resolution MAIAC aerosol product over South America

“…The suite of atmospheric products includes cloud mask, AOD at 0.47 and 0.55 μm gridded at high resolution (1 km), and column water vapor from MODIS near‐infrared (NIR) bands at 0.940 μm. Since the publication of Lyapustin et al [], MAIAC algorithm has added capability for smoke (dust) detection [ Lyapustin et al ., ], improved aerosol retrieval over bright deserts, improved cloud and snow mask, added aerosol retrievals and atmospheric correction over inland, coastal, and open ocean water, and has undergone considerable changes for global application. Storing multiday records from MODIS, the algorithm adds the knowledge of time series to decouple surface and aerosol information using the following assumption: aerosol events are extremely variable during the daytime and homogeneous at small areas (~30 km 2 ), while the land surface is typically stable over a short time scale and heterogeneous spatially.…”

“…Particularly, it offers an advantage of prior knowledge of surface properties to overcome empirical assumptions from previous standard algorithms. Furthermore, AOD retrievals at 1 km resolution provides fine‐scale variability required for many applications, as smoke plume detection [ Lyapustin et al ., ] and air pollution studies [ Kloog et al ., ].…”

Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing

“…The Multi‐Angle Implementation of Atmospheric Correction (MAIAC) algorithm uses a physical atmosphere‐surface model where the model parameters are defined from measurements (Lyapustin, Martonchik, et al, ; Lyapustin, Wang, et al, ; Lyapustin, Korkin, et al, ). Instead of swath‐based processing, MAIAC starts by gridding MODIS L1B measurements to a fixed 1‐km grid and by accumulating a time series of data for up to 16 days using a sliding window technique.…”

Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts

“…We directly used the MODIS resolution (10×10 km 2 ) in assimilation without thinning or re-gridding, showing that data assimilation on fine resolution models is feasible with positive impacts. This becomes important as newer products are available at higher resolutions (e.g., Lyapustin et al, 2012;Munchak et al, 2013). Even though we performed assimilations on a region densely populated by monitoring networks, we assimilated satellite retrievals only; thus, this method should be able to be applied anywhere in the world.…”