Applying FP-ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE-LER) daily maps from UVN satellite measurements

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.