Melanie Coldewey-Egbers

Homogenized total ozone data records from the European sensors GOME/ERS-2, SCIAMACHY/Envisat, and GOME-2/MetOp-A

Within the European Space Agencys Climate Change Initiative, total ozone column records from GOME (Global Ozone Monitoring Experiment), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY), and GOME-2 have been reprocessed with GODFIT version 3 (GOME-type Direct FITting). This algorithm is based on the direct fitting of reflectances simulated in the Huggins bands to the observations. We report on new developments in the algorithm from the version implemented in the operational GOME Data Processor v5. The a priori ozone profile database TOMSv8 is now combined with a recently compiled OMI/MLS tropospheric ozone climatology to improve the representativeness of a priori information. The Ring procedure that corrects simulated radiances for the rotational Raman inelastic scattering signature has been improved using a revised semi-empirical expression. Correction factors are also applied to the simulated spectra to account for atmospheric polarization. In addition, the computational performance has been significantly enhanced through the implementation of new radiative transfer tools based on principal component analysis of the optical properties. Furthermore, a soft-calibration scheme for measured reflectances and based on selected Brewer measurements has been developed in order to reduce the impact of level-1 errors. This soft-calibration corrects not only for possible biases in backscattered reflectances, but also for artificial spectral features interfering with the ozone signature. Intersensor comparisons and ground-based validation indicate that these ozone data sets are of unprecedented quality, with stability better than 1% per decade, a precision of 1.7%, and systematic uncertainties less than 3.6% over a wide range of atmospheric states.

Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from different satellite sensors. The resulting NNs are then used to sequentially adjust the satellite data for the creation of a global homogeneous long-term climate data record. The proposed technique has successfully been applied to the merging of ozone data from three European satellite sensors covering together a time period of more than 16 years. The resulting long-term ozone data record has an excellent long-term stability of 0.2 ± 0.2% per decade and can therefore be used for ozone and climate studies.

Evaluating a new homogeneous total ozone climate data record from GOME/ERS-2, SCIAMACHY/Envisat and GOME-2/MetOp-A†

The European Space Agencys Ozone Climate Change Initiative (O3-CCI) project aims at producing and validating a number of high-quality ozone data products generated from different satellite sensors. For total ozone, the O3-CCI approach consists of minimizing sources of bias and systematic uncertainties by applying a common retrieval algorithm to all level 1 data sets, in order to enhance the consistency between the level 2 data sets from individual sensors. Here we present the evaluation of the total ozone products from the European sensors Global Ozone Monitoring Experiment (GOME)/ERS-2, SCIAMACHY/Envisat, and GOME-2/MetOp-A produced with the GOME-type Direct FITting (GODFIT) algorithm v3. Measurements from the three sensors span more than 16 years, from 1996 to 2012. In this work, we present the latest O3-CCI total ozone validation results using as reference ground-based measurements from Brewer and Dobson spectrophotometers archived at the World Ozone and UV Data Centre of the World Meteorological Organization as well as from UV-visible differential optical absorption spectroscopy (DOAS)/Systeme D′Analyse par Observations Zenithales (SAOZ) instruments from the Network for the Detection of Atmospheric Composition Change. In particular, we investigate possible dependencies in these new GODFIT v3 total ozone data sets with respect to latitude, season, solar zenith angle, and different cloud parameters, using the most adequate type of ground-based instrument. We show that these three O3-CCI total ozone data products behave very similarly and are less sensitive to instrumental degradation, mainly as a result of the new reflectance soft-calibration scheme. The mean bias to the ground-based observations is found to be within the 1 ± 1% level for all three sensors while the near-zero decadal stability of the total ozone columns (TOCs) provided by the three European instruments falls well within the 1–3% requirement of the European Space Agencys Ozone Climate Change Initiative project.

A new health check of the ozone layer at global and regional scales

In this study, we provide a new perspective on the current state of the ozone layer using a comprehensive long-term total ozone data record which has been recently released within the framework of the European Space Agencys Climate Change Initiative. Based on a multivariate regression analysis, we disentangle various aspects of ozone change and variability on global and regional scales, thus enabling the monitoring of the effectiveness of the Montreal Protocol. Given dominant natural variability the expected midlatitude onset of ozone recovery is still not significant and would need additional 5 years of observations to be unequivocally detectable. A regional increase in the tropics is a likely manifestation of a long-term change in El Nino–Southern Oscillation intensity over the last two decades induced by strong El Nino in 1997/1998 and strong La Nina in 2010/2011.

Long-term analysis of GOME in-flight calibration parameters and instrument degradation

Since 1995, the Global Ozone Monitoring Experiment (GOME) has measured solar and backscattered spectra in the ultraviolet and visible wavelength range. Now, the extensive data set of the most important calibration parameters has been investigated thoroughly in order to analyze the long-term stability and performance of the instrument. This study focuses on GOME in-flight calibration and degradation for the solar path. Monitoring the sensor degradation yields an intensity decrease of 70% to 90% in 240-316 nm and 35% to 65% in 311-415 nm. The spectral calibration is very stable over the whole period, although a very complex interaction between predisperser temperature and wavelength was found. The leakage current and the pixel-to-pixel gain increased significantly during the mission, which requires an accurate correction of the measured radiance and irradiance signals using proper calibration parameters. Finally, several outliers in the data sets can be directly assigned to instrument and satellite anomalies.

On the Cause of Recent Variations in Lower Stratospheric Ozone

We use height‐resolved and total column satellite observations and 3‐D chemical transport model simulations to study stratospheric ozone variations during 1998–2017 as ozone‐depleting substances decline. In 2017 extrapolar lower stratospheric ozone displayed a strong positive anomaly following much lower values in 2016. This points to large interannual variability rather than an ongoing downward trend, as reported recently by Ball et al. (2018, https://doi.org/10.5194/acp‐18‐1379‐2018). The observed ozone variations are well captured by the chemical transport model throughout the stratosphere and are largely driven by meteorology. Model sensitivity experiments show that the contribution of past trends in short‐lived chlorine species to the ozone changes is small. Similarly, the potential impact of modest trends in natural brominated short‐lived species is small. These results confirm the important role that atmospheric dynamics plays in controlling ozone in the extrapolar lower stratosphere on multiannual time scales and the continued importance of monitoring ozone profiles as the stratosphere changes.