Christophe Lerot

The GOME‐2 total column ozone product: Retrieval algorithm and ground‐based validation

The Global Ozone Monitoring Instrument (GOME-2) was launched on EUMESATs MetOp-A satellite in October 2006. This paper is concerned with the retrieval algorithm GOME Data Processor (GDP) version 4.4 used by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF) for the operational generation of GOME-2 total ozone products. GDP 4.4 is the latest version of the GDP 4.0 algorithm, which is employed for the generation of official Level 2 total ozone and other trace gas products from GOME and SCIAMACHY. Here we focus on enhancements introduced in GDP 4.4: improved cloud retrieval algorithms including detection of Sun glint effects, a correction for intracloud ozone, better treatment of snow and ice conditions, accurate radiative transfer modeling for large viewing angles, and elimination of scan angle dependencies inherited from Level 1 radiances. Furthermore, the first global validation results for 3 years (2007–2009) of GOME-2/MetOp-A total ozone measurements using Brewer and Dobson measurements as references are presented. The GOME-2/MetOp-A total ozone data obtained with GDP 4.4 slightly underestimates ground-based ozone by about 0.5% to 1% over the middle latitudes of the Northern Hemisphere and slightly overestimates by around 0.5% over the middle latitudes in the Southern Hemisphere. Over high latitudes in the Northern Hemisphere, GOME-2 total ozone has almost no offset relative to Dobson readings, while over high latitudes in the Southern Hemisphere GOME-2 exhibits a small negative bias below 1%. For tropical latitudes, GOME-2 measures on average lower ozone by 0% to 2% compared to Dobson measurements.

Global long-term monitoring of the ozone layer - a prerequisite for predictions

Although the Montreal Protocol now controls the production and emission of ozone depleting substances, the timing of ozone recovery is unclear. There are many other factors affecting the ozone layer, in particular climate change is expected to modify the speed of re-creation of the ozone layer. Therefore, long-term observations are needed to monitor the further evolution of the stratospheric ozone layer. Measurements from satellite instruments provide global coverage and are supplementary to selective ground-based observations. The combination of data derived from different space-borne instruments is needed to produce homogeneous and consistent long-term data records. They are required for robust investigations including trend analysis. For the first time global total ozone columns from three European satellite sensors GOME (ERS-2), SCIAMACHY (ENVISAT), and GOME-2 (METOP-A) are combined and added up to a continuous time series starting in June 1995. On the one hand it is important to monitor the consequences of the Montreal Protocol and its amendments; on the other hand multi-year observations provide the basis for the evaluation of numerical models describing atmospheric processes, which are also used for prognostic studies to assess the future development. This paper gives some examples of how to use satellite data products to evaluate model results with respective data derived from observations, and to disclose the abilities and deficiencies of atmospheric models. In particular, multi-year mean values derived from the Chemistry-Climate Model E39C-A are used to check climatological values and the respective standard deviations.

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.

Substantial Underestimation of Post-Harvest Burning Emissions in the North China Plain Revealed by Multi-Species Space Observations

The large-scale burning of crop residues in the North China Plain (NCP), one of the most densely populated world regions, was recently recognized to cause severe air pollution and harmful health effects. A reliable quantification of the magnitude of these fires is needed to assess regional air quality. Here, we use an eight-year record (2005–2012) of formaldehyde measurements from space to constrain the emissions of volatile organic compounds (VOCs) in this region. Using inverse modelling, we derive that satellite-based post-harvest burning fluxes are, on average, at least a factor of 2 higher than state-of-the-art bottom-up statistical estimates, although with significant interannual variability. Crop burning is calculated to cause important increases in surface ozone (+7%) and fine aerosol concentrations (+18%) in the North China Plain in June. The impact of crop fires is also found in satellite observations of other species, glyoxal, nitrogen dioxide and methanol, and we show that those measurements validate the magnitude of the top-down fluxes. Our study indicates that the top-down crop burning fluxes of VOCs in June exceed by almost a factor of 2 the combined emissions from other anthropogenic activities in this region, underscoring the need for targeted actions towards changes in agricultural management practices.

The GODFIT algorithm: a direct fitting approach to improve the accuracy of total ozone measurements from GOME

We present the total ozone retrieval algorithm GODFIT (GOME Direct-FITting). Applied to nadir backscattered measurements from the Global Ozone Monitoring Experiment (GOME), it is based on a direct-fitting approach by which spectral radiances simulated using the radiative transfer model LIDORT v3.3 (Linearized Discrete Ordinate Radiative Transfer) are adjusted to measurements in the 325–335 nm interval. Total O3 columns retrieved from GOME spectra have been compared not only to columns retrieved from Ozone Monitoring Instrument (OMI) measurements using the TOMS v8.5 algorithm, but also to correlative ground-based measurements from the GAW/NDACC networks (Global Atmosphere Watch/Network for the Detection of Atmospheric Composition Change). We show that GODFIT produces a significant reduction of the GOME ground-based differences and some of the associated dependencies, compared with the GOME Data Processor (GDP) 4.1 product. Version 5 of GDP, based on the GODFIT algorithm, will be released in spring 2010.

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.

Xe-broadening coefficients of 12CH3D: a test of theoretical line shapes

We present the Xe-broadening coefficients for six lines belonging to the ν3 band of 12CH3D measured at room temperature with a diode-laser spectrometer. The collisional widths are obtained from least-squares fitting of each absorption line with theoretical line shapes. We used the well-known Voigt profile, the soft and hard collision models considering the collisional narrowing and also line shapes taking into account the speed dependence of the collisional cross-section. The results derived by these different models are compared with each other and with theoretical broadening coefficients. The calculations are based on a semiclassical impact formalism that includes the atom–atom Lennard–Jones potential for CH3D–Xe interactions in which CH3D is considered as a linear molecule.

NILU-UV multi-filter radiometer total ozone columns: Comparison with satellite observations over Thessaloniki, Greece

This study aims to construct and validate a neural network (NN) model for the production of high frequency (~1min) ground-based estimates of total ozone column (TOC) at a mid-latitude UV and ozone monitoring station in the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki (LAP/AUTh) for the years 2005-2014. In the first stage of model development, ~30,000 records of coincident solar UV spectral irradiance measurements from a Norsk Institutt for Luftforskning (NILU)-UV multi-filter radiometer and TOC measurements from a co-located Brewer spectroradiometer are used to train a NN to learn the nonlinear functional relation between the irradiances and TOC. The model is then subjected to sensitivity analysis and validation. Close agreement is obtained (R2=0.94, RMSE=8.21 DU and bias=-0.15 DU relative to the Brewer) for the training data in the correlation of NN estimates on Brewer derived TOC with 95% of the coincident data differing by less than 13 DU. In the second stage of development, a long time series (≥1 million records) of high frequency (~1min) NILU-UV ground-based measurements are presented as inputs to the NN model to generate high frequency TOC estimates. The advantage of the NN model is that it is not site dependent and is applicable to any NILU input data lying within the range of the training data. GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura and GOME2/MetOp-A TOC records are then used to perform a precise cross-validation analysis and comparison with the NILU TOC estimates over Thessaloniki. All 4 satellite TOC dataset are retrieved using the GOME Direct Fitting algorithm, version 3 (GODFIT_v3), for reasons of consistency. The NILU TOC estimates within ±30min of the overpass times agree well with the satellite TOC retrievals with coefficient of determination in the range 0.88≤R2≤0.90 for all sky conditions and 0.95≤R2≤0.96 for clear sky conditions. The mean fractional differences are found to be -0.67%±2.15%, -1.44%±2.25%, -2.09%±2.06% and -0.85%±2.19% for GOME, SCIAMACHY, OMI and GOME2 respectively for the clear sky cases. The near constant standard deviation (~±2.2%) across the array of sensors testifies directly to the stability of both the GODFIT_v3 algorithm and the NN model for providing coherent and robust TOC records. Furthermore, the high Pearson product moment correlation coefficients (0.94<R<0.98) testify to the strength of the linear relationship between the satellite algorithm retrievals of TOC and ground-based estimates, while biases of less than 5 DU suggest that systematic errors are low. This novel methodology contributes to the ongoing assessment of the quality and consistency of ground and space-based measurements of total ozone columns.

Nine years of atmospheric remote sensing with sciamachy - atmospheric parameters and data products

The SCIAMACHY instrument on-board ENVISAT measures since 2002 trace gas constituents of the atmosphere in nadir, limb and occultation configuration. It is an imaging spectrometer with q spectral range from the UV/VIS to SWIR (212 nm – 2384nm). In this paper we describe shortly the current status of the operational processing chains from Level 0-1b and Level 1b-2 that deliver Earth radiances, solar irradiance, trace gas total columns and profiles as well as cloud characteristics on an orbital basis. An outlook for future operational products is also given.