Evaluation of Water Vapor Product from TROPOMI and GOME-2 Satellites against Ground-Based GNSS Data over Europe

Abstract

A novel integrated water vapor (IWV) product from TROPOspheric Monitoring Instrument (TROPOMI) is validated together with a Global Ozone Monitoring Instrument-2 (GOME-2) standard product. As reference, ground-based Global Navigation Satellite Systems (GNSS) IWV data in 235 European stations from May 2018 to May 2019 are used. Under cloud free situations, a general comparison is carried out. It suggests that TROPOMI IWV exhibits less bias than GOME-2 and better results in the dispersion and regression parameters. Moreover, TROPOMI presents more homogeneous results along the different stations. However, TROPOMI is found to be overestimating the IWV uncertainties and being, therefore, too conservative in the confidence interval considered. The dependence of satellite product performance on several variables is also discussed. TROPOMI IWV shows wet bias of 5.7% or less for IWV < 10 mm (TROPOMI) and dry bias of up to -3% (TROPOMI). In contrast, GOME-2 shows wet bias of 30% or less for IWV < 25 mm (GOME-2) and dry bias of -12.3% for IWV > 25 mm. In addition, relative standard deviation (rSD) increases as IWV increases. In addition, the dependence on solar zenith angle (SZA) was also analyzed, as solar radiation bands are used in the retrieval algorithm of both instruments. Relative mean bias error (rMBE) shows positive values for GOME-2, slightly increasing with SZA, while TROPOMI shows more stable values. However, under high SZA, GOME-2 IWV exhibits a steep increase in rMBE (overestimation), while TROPOMI IWV exhibits a moderate decrease (underestimation). rSD is slightly increasing with SZA. The influence of cloudiness on satellite IWV observations is such that TROPOMI tends to overestimate IWV more as cloudiness increases, especially for high IWV. In the case of GOME-2, the rSD slightly increases with cloudiness, but TROPOMI rSD has a marked increase with increasing cloudiness. TROPOMI IWV is an important source of information about moisture, but its algorithm could still benefit from further improvement to respond better to cloudy situations. View Full-Text 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.