Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data

Abstract

This paper presents two algorithms used to derive Aerosol Optical Depth (AOD) from a synergy of satellite and ground-based observations, as well as aerosol transport model output. The Spinning Enhanced Visible Infrared Radiometer (SEVIRI) instrument on board Meteosat Second Generation (MSG) allows us to monitor aerosol loading over land at high temporal and spatial resolution. We present the algorithms which were fed with the data acquired via the SEVIRI channel 1, and also channels 1 and 3 in conjunction. In both cases, the surface reflectance is the most important parameter that should be estimated during the retrieval process. The surface properties are estimated during days with a low AOD (less than 0.1 at 500 nm) based on the radiance measured by the SEVIRI detector and aerosol optical properties modeled with the aerosol transport model or measured by the MODIS sensor. For data from the model and the MODIS, ground-based stations equipped with a sun photometer have been applied to correct the AOD fields using the optimal interpolation method. By assuming that surface reflectance at the SEVIRI resolution changes slowly over time, the AOD has been computed. Comparison of the SEVIRI AOD with the sun photometer observations shows good agreement/correlation. The mean bias is small (an order of 0.01-0.02) and the root mean square (rms) is about 0.05 for both one-and two-channel methods. In addition, the rms for the one-channel method does not change with the AOD.

Modification of Local Urban Aerosol Properties by Long-Range Transport of Biomass Burning Aerosol

“…Such weather dynamics provide an excellent test-bed for performing quasi-continuous ground-based passive and/or active remote sensing of optical properties of aerosols suspended in the air above the measurement site, whether on a local [3][4][5] or regional scale [6,7]. Such conditions also give great opportunities for retrieval of aerosol optical properties from satellite remote sensing (MODIS, SEVIRI), which are obviously hindered by cloud occurrence [8,9]. On the other hand, before/after anticyclones, cloud systems may form relatively slowly, which allows for remote sensing studies of aerosol-tocloud transitions [10].…”

Effect of Heat Wave Conditions on Aerosol Optical Properties Derived from Satellite and Ground-Based Remote Sensing over Poland

“…The SEVIRI collects data in 12 spectral channels (in a range of 635 nm to 13.4 µm) with spatial resolution for a sub-satellite point of 3 km for channels 1-11 and 1 km in channel 12 (High Resolution Visible channel). Several approaches to the SEVIRI AOD daytime and night-time retrieval have been reported (e.g., [28][29][30]). In this paper we use the prototype version of the SEVIRI AOD algorithm developed in-house [28] that has been modified to provide AOD retrieval in near real-time over the territory of Poland.…”

“…Several approaches to the SEVIRI AOD daytime and night-time retrieval have been reported (e.g., [28][29][30]). In this paper we use the prototype version of the SEVIRI AOD algorithm developed in-house [28] that has been modified to provide AOD retrieval in near real-time over the territory of Poland. The algorithm is fed with the high temporal and spatial resolution radiance measurements from the SEVIRI visible channel 1 (635 nm) and allows for deriving AOD at 635 nm, at a spatial resolution of about 5.5 × 5.5 km 2 (due to the scan geometry for the area of Poland), at a frequency of 15 min.…”

“…In the algorithm used here, instead of the golden section search method and parabolic interpolation, an approach based on applying the optimal interpolation method [43] is used with the additional ground based sources of AOD data, that allow a reduction of the typical uncertainties related to the aerosol background estimation, and hence improve the AOD SEVIRI retrieval. A detailed description of the algorithm and uncertainty estimation was presented in [28].…”

“…The challenge here is not solely appropriate cloud mask filtering [26,27]. The satellite retrieval of aerosol properties over land is a difficult task, mainly due to estimation of the surface reflectance (in terms of its high temporal variability and spatial inhomogeneity) and the anisotropic bi-directional reflectance of land surfaces (inducing higher uncertainties of retrieved parameters) [28][29][30]. However, the satellite-derived AODs are recognized as an important predictor of ground-level particulate matter (PM), which empowers the air pollution research [31,32].…”