Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) daily maps from UVN satellite measurements

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.

TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020

“…TROPOMI version 1 reflectances for band 3 are within 5 %-10 % compared with OMI and OMPS (Rozemeijer and Kleipool, 2019a, b). It is expected that the upcoming version 2 of the TROPOMI level 1b product will solve inconsistencies of the radiometric calibration detected in the UV and UV-Vis spectrometers using in-flight measurements, and it will include degradation correction for the affected bands (Ludewig et al, 2020).…”

TROPOMI Aerosol Products: Evaluation and Observations of Synoptic Scale Carbonaceous Aerosol Plumes during 2018–2020

“…Future TropOMAER algorithm enhancement will explore the utilization of TROPOMI retrieved information on aerosol layer height (Nanda et al, 2019),CO (Martínez-Alonso et al, 2020), clouds (Loyola et al, 2018), geometrydependent effective LER (Loyola et al, 2020), as well as taking advantage of additional available spectral measurements for aerosol typing. Work is currently underway on the development of a higher spatial resolution surface albedo data.…”

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O<sub>2</sub>-O<sub>2</sub>, MODIS and Suomi-NPP VIIRS

“…Other complementary information about the surface properties have been initially estimated from a climatology. In the upcoming version 2 of the CLOUD processor, the geometry-dependent surface properties are retrieved directly from TROPOMI measurements within the ROCINN fitting window using the GE_LER algorithm (Loyola et al, 2020).…”