Abstract
Abstract. The retrieval of trace gas, cloud, and aerosol measurements from ultraviolet, visible, and near-infrared (UVN) sensors requires precise information on surface properties that are traditionally obtained from Lambertian equivalent reflectivity (LER) climatologies. The main drawbacks of using LER climatologies for new satellite missions are that (a) climatologies are typically based on previous missions with significantly lower spatial resolutions, (b) they usually do not account fully for satellite-viewing geometry dependencies characterized by bidirectional reflectance distribution function (BRDF) effects, and (c) climatologies may differ considerably from the actual surface conditions especially with snow/ice scenarios. In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors; the algorithm is based on the full-physics inverse learning machine (FP_ILM) retrieval. Radiances are simulated using a radiative transfer model that takes into account the satellite-viewing geometry, and the inverse problem is solved using machine learning techniques to obtain the GE_LER from satellite measurements. The GE_LER retrieval is optimized not only for trace gas retrievals employing the DOAS algorithm, but also for the large amount of data from existing and future atmospheric Sentinel satellite missions. The GE_LER can either be deployed directly for the computation of air mass factors (AMFs) using the effective scene approximation or it can be used to create a global gapless geometry-dependent LER (G3_LER) daily map from the GE_LER under clear-sky conditions for the computation of AMFs using the independent pixel approximation. The GE_LER algorithm is applied to measurements of TROPOMI launched in October 2017 on board the EU/ESA Sentinel-5 Precursor (S5P) mission. The TROPOMI GE_LER/G3_LER results are compared with climatological OMI and GOME-2 LER datasets and the advantages of using GE_LER/G3_LER are demonstrated for the retrieval of total ozone from TROPOMI.
Highlights
The lack of knowledge of the magnitude of surface reflectance and the neglect of surface anisotropic effects are the two major error sources in the retrieval of trace gas, cloud, and aerosol information from ultraviolet, visible, and near-infrared (UVN) satellite measurements (Vasilkov et al, 2018; Lorente et al, 2018; Lin et al, 2014; Seidel et al, 2012; Zhou et al, 2010)
The agreement with Copernicus Atmosphere Monitoring Service (CAMS) improves considerably at all latitudes: the difference in the total ozone for the region 80– 60◦ S is reduced from −2.61 ± 2.22 % using OMI Lambertian equivalent reflectivity (LER) to 0.74 ± 2.43 % using TROPOMI G3_LER; for 60◦ S–50◦ N, the difference remains at the same level with a small increase from 0.23 ± 1.14 % to −0.38 ± 1.13 %; in the region 50–70◦ N, the difference is reduced from 1.24 ± 2.45 % to −0.79 ± 1.98 %; and for 70–90◦ N the difference is −1.001 ± 2.58 % compared to −1.35 ± 2.5 %
We have developed a novel algorithm for the accurate and fast retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors based on the full-physics inverse learning machine (FP_ILM) technique
Summary
The lack of knowledge of the magnitude of surface reflectance and the neglect of surface anisotropic effects are the two major error sources in the retrieval of trace gas, cloud, and aerosol information from ultraviolet, visible, and near-infrared (UVN) satellite measurements (Vasilkov et al, 2018; Lorente et al, 2018; Lin et al, 2014; Seidel et al, 2012; Zhou et al, 2010). The WFDOAS (Coldewey-Egbers et al, 2005) algorithm retrieves the effective LER at 377 nm, while the GODFIT (Lerot et al, 2010) and SAGE III (Rault and Taha, 2007) approaches both retrieve simultaneously the effective LER and other parameters along with total ozone Another approach used for NO2 and cloud retrievals involved the computation of LER from bidirectional reflectance distribution function (BRDF) data obtained from other satellite sensors with higher spatial resolution. The main drawbacks of LUTs with high dimensionality (common in atmospheric composition retrievals) are that the memory requirements increase exponentially with the number of input dimensions, the interpolation/extrapolation in this multidimensional space is computationally expensive, and interpolation/extrapolation errors can be significant To avoid these LUT issues, during the last 2 decades, the DLR team has developed machine learning techniques for the optimal generation of RTM samples (Loyola et al, 2016) and the accurate parameterization of RTM simulations using artificial neural networks (NNs).
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