The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light

Abstract

Abstract. Remote sensing of aerosol from space is a challenging and typically underdetermined retrieval task, requiring many assumptions to be made with respect to the aerosol and surface models. Therefore, the quality of a priori information plays a central role in any retrieval process (apart from the cloud screening procedure and the forward radiative transfer model, which to be most accurate should include the treatment of light polarization and molecular-aerosol coupling). In this paper the performance of various algorithms with respect to the of spectral aerosol optical thickness determination from optical spaceborne measurements is studied. The algorithms are based on various types of measurements (spectral, angular, polarization, or some combination of these). It is confirmed that multiangular spectropolarimetric measurements provide more powerful constraints compared to spectral intensity measurements alone, particularly those acquired at a single view angle and which rely on a priori assumptions regarding the particle phase function in the retrieval process.

AERONET-based models of smoke-dominated aerosol near source regions and transported over oceans, and implications for satellite retrievals of aerosol optical depth

“…For example, optical properties for inversions at Singapore are similar to the climatology for Moscow; as well as potential urban influence, this may reflect that smoke at Singapore is often a combination of peat and forest burning from Indonesia, e.g. Langmann and Heil (2004), similar to Moscow. Note that Sevastopol is also a close match to optical properties at Moscow; some of the Sevastopol cases corresponded to transported smoke from burning in August 2010 near Moscow.…”

“…Quantities such as aerosol optical depth (AOD) have generally been retrieved with lower uncertainties over oceans than land surfaces, due to the comparative homogeneity of open ocean surface properties and general lack of strong oceanic aerosol point sources. Despite this, significant differences can still exist between AOD retrieved using different instruments or algorithms, in both clean and polluted conditions, and for real measurements as well as simulated data, and uncertainty in other relevant aerosol properties can be larger Kokhanovsky et al, 2010;Sayer et al, 2012a).…”

Assessment of two aerosol optical thickness retrieval algorithms applied to MODIS Aqua and Terra measurements in Europe

“…Kokhanovsky et al (2010) compare several major aerosol retrieval algorithms by using a synthetic data set of TOA radiation for a model atmosphere and under the assumption of a black surface. In this way, the assumptions made in the different aerosol models that are incorporated in the various algorithms could be investigated.…”

Fast and simple model for atmospheric radiative transfer

“…These accurate but complex RTMs are frequently run in a forward mode, generating look-up tables (LUTs), which are later used during the inversion process for atmospheric compensation (Gao et al, 2009) or aerosol retrieval (Kokhanovsky, 2008;Kokhanovsky and Leeuw, 2009;Kokhanovsky et al, 2010), for instance. There are also a series of highly accurate, but computationally intensive Monte Carlo photon transport codes available.…”