Benefit of ozone observations from Sentinel-5P and future Sentinel-4 missions on tropospheric composition

Abstract

Abstract. We present an observing simulated system experiment (OSSE) dedicated to evaluate the potential added value from the Sentinel-4 and the Sentinel-5P observations on tropospheric ozone composition. For this purpose, the ozone data of Sentinel-4 (Ultraviolet Visible Near-infrared) and Sentinel-5P (TROPOspheric Monitoring Instrument) onboard a geostationary (GEO) and a low Earth orbit (LEO) platform, respectively, has been simulated for the summer 2003. To ensure the robustness of the results, the OSSE has been configured with conservative assumptions. We simulate the reality by combining two chemistry transport models (CTMs): the Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) and the Transport Model version 5 (TM5). The assimilation system is based on a different CTM, the MOdèle de Chimie Atmosphérique à Grande Echelle (MOCAGE), combined with the 3D variational technique. The background error covariance matrix does not evolve in time and its variance is proportional to the field values. The simulated data are formed of six eigenvectors to minimize the size of the dataset by removing the noise-dominated part of the observations. The results show that the satellite data clearly bring direct added value around 200 hPa for the whole assimilation period and for the whole European domain, while a likely indirect added value is identified but not for the whole period and domain at 500 hPa, and to a lower extent at 700 hPa. In addition, the ozone added value from Sentinel-5P (LEO) appears close to that from Sentinel-4 (GEO) in the free troposphere (200–500 hPa) in our OSSE.

The cost function of the data fusion process and its application

“…Let us assume to have N independent measurements of the vertical profile of an atmospheric target referring to the same space-time location. Performing the retrieval of the N measurements with the optimal estimation method (Rodgers, 2000), we obtain N vectorsx i (i = 1, 2, . .…”

“…., N ), here assumed to be estimates of the same profile made on a common vertical grid. The vectorsx i are characterized by the CMs S i and the averaging kernel matrices (AKMs) A i (Ceccherini et al, 2003;Ceccherini and Ridolfi, 2010;Rodgers, 2000). The CMs S i are each defined as σ i σ T i , where the vector σ i contains the error due to the propagation of the observation noise through the retrieval process (and differs from the total error, equal to the difference between true and retrieved vertical profiles, by the smoothing error due to the use of a constraint in the retrieval), the superscript T indicates the transpose of the vector and the symbol .…”

“…where S coin is the CM describing the variability of the true profiles of the fusing measurements. The CM associated with the forward model errors is given by (Rodgers, 2000)…”

“…Recalling that the trace of the AKM represents the number of degrees of freedom (DOFs), which is the number of independent parameters actually determined by the analysis (Rodgers, 2000), we see that the expected value of the cost function is equal to the following: a first term that counts the number of available measurements minus a second term that is the number of DOFs plus a third term that depends on the difference between the a priori profile and the true profile.…”