TROPOMI/S5P total ozone column data: global ground-based validation and consistency with other satellite missions

Abstract

Abstract. In October 2017, the Sentinel-5 Precursor (S5P) mission was launched, carrying the TROPOspheric Monitoring Instrument (TROPOMI), which provides a daily global coverage at a spatial resolution as high as 7 km × 3.5 km and is expected to extend the European atmospheric composition record initiated with GOME/ERS-2 in 1995, enhancing our scientific knowledge of atmospheric processes with its unprecedented spatial resolution. Due to the ongoing need to understand and monitor the recovery of the ozone layer, as well as the evolution of tropospheric pollution, total ozone remains one of the leading species of interest during this mission. In this work, the TROPOMI near real time (NRTI) and offline (OFFL) total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, which form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith angle, viewing angle, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0 –1.5 % and 2.5 %–4.5 %, respectively. The mean bias that results from the comparisons is well within the S5P product requirements, while the mean standard deviation is very close to those limits, especially considering that the statistics shown here originate both from the satellite and the ground-based measurements. Additionally, the TROPOMI OFFL and NRTI products are evaluated against already known spaceborne sensors, namely, the Ozone Mapping Profiler Suite, on board the Suomi National Polar-orbiting Partnership (OMPS/Suomi-NPP), NASA v2 TOCs, and the Global Ozone Monitoring Experiment 2 (GOME-2), on board the Metop-A (GOME-2/Metop-A) and Metop-B (GOME-2/Metop-B) satellites. This analysis shows a very good agreement for both TROPOMI products with well-established instruments, with the absolute differences in mean bias and mean standard deviation being below +0.7 % and 1 %, respectively. These results assure the scientific community of the good quality of the TROPOMI TOC products during its first year of operation and enhance the already prevalent expectation that TROPOMI/S5P will play a very significant role in the continuity of ozone monitoring from space.

Impact of the June 2018 Saddleworth Moor wildfires on air quality in northern England

“…The approach of Pope et al (2018) is used to map TROPOMI TCCO data onto a 0.03°×0.03°grid over the UK, while the FRP data (Level 3 product) is on a 0.1°×0.1°grid. Garane et al (2019) find a typical global bias of 0%-1.5% between TROPOMI TCCO and surface validation sites. For the Saddleworth Moor fires, we see precision errors of approximately 3.3%-4.3%.…”

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

“…The near-real-time S5P total ozone product is based on an iterative DOAS/AMF algorithm (Loyola et al, 2020) and the current operational version (1.1.7) uses the OMI LER climatology (Kleipool et al, 2008). The median bias between near-real-time total ozone from S5P and reference data from Brewer, Dobson, and SAOZ sites is of the order of +1 % (Verhoelst et al, 2019;Garane et al, 2019). S5P near-real-time ozone agrees well with the Copernicus Atmosphere Monitoring Service (CAMS) analysis with the exception of some anomalies at high latitudes (Inness et al, 2019).…”

“…Machine learning can be used not only for forward problems (such as the parameterization of RTM simulations), but also for solving inverse problems; see for example (Loyola et al, 2006). Recently we have developed an approach called the "full-physics inverse learning machine" (FP_ILM) technique; this has been applied successfully for retrieving ozone profile shapes from GOME-2 (Xu et al, 2017) and retrieving SO 2 layer height from GOME-2 (Efremenko et al, 2017) and TROPOMI (Hedelt et al, 2019). 1 presents a flow diagram of the different steps of the FP_ILM algorithm and the following subsections describe in more detail how FP_ILM is tailored for the retrieval of GE_LER.…”

Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) to account for BRDF effects on UVN satellite measurements of trace gases, clouds and aerosols

“…The near-real-time S5P total ozone product is based on an iterative DOAS/AMF algorithm (Loyola et al, 2019) and the current operational version (1.1.5) uses the OMI LER climatology (Kleipool et al, 2008). The median bias between nearreal-time total ozone from S5P and reference data from Brewer, Dobson, and SAOZ sites is of the order of +1% (Verhoelst et al, 2018;Garane et al, 2019).…”