Abstract
Abstract. Atmospheric aerosols are an important element of Earth's climate system and have significant impacts on the environment and on human health. Global aerosol modeling has been increasingly used for operational forecasting and as support for decision making. For example, aerosol analyses and forecasts are routinely used to provide air quality information and alerts in both civilian and military applications. The growing demand for operational aerosol forecasting calls for additional observational data that can be assimilated into models to improve model accuracy and predictive skill. These factors have motivated the development, testing, and release of a new near real-time (NRT) level 2 (L2) aerosol product from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform. The NRT product capitalizes on the unique attributes of the MISR aerosol retrieval approach and product contents, such as reliable aerosol optical depth as well as aerosol microphysical information. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition of the satellite observations. Implications for the product quality and consistency are discussed and compared to the current operational L2 MISR aerosol product. Several ways of implementing additional use-specific retrieval screenings are also highlighted.
Highlights
Atmospheric aerosols have long been recognized to influence the climate, environment, and human health (e.g., IPCC, 2013; Lelieveld et al, 2015; Shindell et al, 2013; Turnock et al, 2020)
In addition to the cloud masks retrieved in the L1B processing (RCCM) and from the level 2 (L2) Cloud Detection and Classification algorithm (SDCM, Angular Signature Cloud Mask (ASCM)), the Multi-angle Imaging SpectroRadiometer (MISR) aerosol retrieval algorithm relies on three internal tests to further identify cloudy pixels that might have escaped earlier detection
version 23 (V23) aerosol optical depths (AODs) retrievals have remarkable accuracy compared against ground-based observations (Garay et al, 2020; Tao et al, 2020; Witek et al, 2019) and the product is more intuitive and easier to use than previous versions
Summary
Atmospheric aerosols have long been recognized to influence the climate, environment, and human health (e.g., IPCC, 2013; Lelieveld et al, 2015; Shindell et al, 2013; Turnock et al, 2020) They affect satellite remote sensing of important geophysical parameters such as ocean color (e.g., Frouin et al, 2019; Gordon, 1997) and greenhouse gas abundance (Butz et al, 2009; Frankenberg et al, 2012; Houweling et al, 2005).
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