Abstract
Abstract. We present an Observing System Simulation Experiment (OSSE) dedicated to the evaluation of the added value of the Sentinel-4 and Sentinel-5P missions for tropospheric nitrogen dioxide (NO2). Sentinel-4 is a geostationary (GEO) mission covering the European continent, providing observations with high temporal resolution (hourly). Sentinel-5P is a low Earth orbit (LEO) mission providing daily observations with a global coverage. The OSSE experiment has been carefully designed, with separate models for the simulation of observations and for the assimilation experiments and with conservative estimates of the total observation uncertainties. In the experiment we simulate Sentinel-4 and Sentinel-5P tropospheric NO2 columns and surface ozone concentrations at 7 by 7 km resolution over Europe for two 3-month summer and winter periods. The synthetic observations are based on a nature run (NR) from a chemistry transport model (MOCAGE) and error estimates using instrument characteristics. We assimilate the simulated observations into a chemistry transport model (LOTOS-EUROS) independent of the NR to evaluate their impact on modelled NO2 tropospheric columns and surface concentrations. The results are compared to an operational system where only ground-based ozone observations are ingested. Both instruments have an added value to analysed NO2 columns and surface values, reflected in decreased biases and improved correlations. The Sentinel-4 NO2 observations with hourly temporal resolution benefit modelled NO2 analyses throughout the entire day where the daily Sentinel-5P NO2 observations have a slightly lower impact that lasts up to 3–6 h after overpass. The evaluated benefits may be even higher in reality as the applied error estimates were shown to be higher than actual errors in the now operational Sentinel-5P NO2 products. We show that an accurate representation of the NO2 profile is crucial for the benefit of the column observations on surface values. The results support the need for having a combination of GEO and LEO missions for NO2 analyses in view of the complementary benefits of hourly temporal resolution (GEO, Sentinel-4) and global coverage (LEO, Sentinel-5P).
Highlights
Air pollution is responsible for one out of every nine deaths worldwide (WHO, 2016) and is one of the biggest environmental threats for our living planet
In the design of an Observing System Simulation Experiment (OSSE), it is important to demonstrate that the nature run (NR) exhibits the same statistical behaviour as the real atmosphere for aspects relevant to the observing system under study
Of S4, providing full daytime sampling, and (3) foreseen improvement in the instrument (for TROPospheric Ozone Monitoring Instrument (TROPOMI) an improvement of the slant column uncertainty of 30 %–40 % compared to Ozone Monitoring Instrument (OMI) has been reported) and improvements due to advances in the characterisation of aspects like clouds, albedo, and aerosol effects
Summary
Air pollution (indoor and outdoor) is responsible for one out of every nine deaths worldwide (WHO, 2016) and is one of the biggest environmental threats for our living planet. Outdoor air pollution alone causes about 3 million premature deaths per year (WHO, 2016). The main pollutant accountable for this significant health impact is particulate matter (PM or aerosols), consisting of small particles in the atmosphere that enter the lungs and blood stream and cause cardiovascular, cerebrovascular and respiratory problems. To allow the formulation of effective policy measures for reducing the exposure to air pollution, accurate knowledge of the sources and distribution of air pollutants is required. This knowledge is gained through observations of the atmospheric composition by ground-based and satellite instruments. The synergetic use of models with observations provides the best possible estimate of the three-dimensional distribution of air pollutants in the atmosphere in the past (reanalyses), present (nowcasts) and future (forecasts)
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