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Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000 to 2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry-climate model simulations, which assume emissions reductions similar to those caused by the COVID-19 crisis. COVID-19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020.

A reanalysis dataset produced by the Copernicus Atmosphere Monitoring service (CAMS reanalysis, 2003 - present day) augmented by ERA5 data for the years before 2003 is used to describe the evolutio...

In 2002, the European Space Agency (ESA) launched the ENVIromental SATellite (ENVISAT), which carried ten instruments to provide continuous observation of Earth's land, atmosphere, oceans and ice caps. During the satellite's lifetime, the European Centre for Medium Range Weather Forecasts (ECMWF) has routinely monitored a variety of products from many of its instruments. A subset of these data has also been assimilated in the ECMWF operational system, along with two of its applications: the reanalysis and the Monitoring Atmospheric Composition and Climate (MACC) systems. This article reviews those activities and summarizes the lessons learnt in monitoring and assimilating data from a research satellite within a numerical weather prediction system. The value of continuous data monitoring and the benefits that a close collaboration between data provider and data user can bring to both parties are highlighted. For observations that were assimilated, impact assessments on the ECMWF products have been performed periodically. Two cases are presented in this article. The first one shows that the assimilation of ocean wave information can reduce the wave-height model random error by up to 8% at the analysis time, with benefits at later forecast times extending to up to 5 days in the Tropics. The second example shows that the assimilation of two ENVISAT ozone products improves the agreement of the ozone analyses with independent ozone observations obtained from sondes and the Microwave Limb Sounder. Finally, the use of ENVISAT reprocessed data is presented, with an emphasis on the importance of data reprocessing and long-term data preservation as key activities to ensure the future usage of these datasets.