Claus Zehner

Ten years of GOME/ERS-2 total ozone data- : The new GOME data processor (GDP) version 4: 1. Algorithm description

The Global Ozone Monitoring Instrument (GOME) was launched on European Space Agencys ERS-2 platform in April 1995. The GOME data processor (GDP) operational retrieval algorithm has generated total ozone columns since July 1995. In 2004 the GDP system was given a major upgrade to version 4.0, a new validation was performed, and the 10-year GOME level 1 data record was reprocessed. In two papers, we describe the GDP 4.0 retrieval algorithm and present an error budget and sensitivity analysis (paper 1) and validation of the GDP total ozone product and the overall accuracy of the entire GOME ozone record (paper 2). GDP 4.0 uses an optimized differential optical absorption spectroscopy (DOAS) algorithm, with air mass factor (AMF) conversions calculated using the radiative transfer code linearized discrete ordinate radiative transfer (LIDORT). AMF computation is based on the TOMS version 8 ozone profile climatology, classified by total column, and AMFs are adjusted iteratively to reflect the DOAS slant column result. GDP 4.0 has improved wavelength calibration and reference spectra and includes a new molecular Ring correction to deal with distortion of ozone absorption features due to inelastic rotational Raman scattering effects. Preprocessing for cloud parameter estimation in GDP 4.0 is done using two new cloud correction algorithms: OCRA and ROCINN. For clear and cloudy scenes the precision of the ozone column product is better than 2.4 and 3.3%, respectively, for solar zenith angles up to 80°. Comparisons with ground-based data are generally at the 1-1.5% level or better for all regions outside the poles.

Application of neural algorithms for a real-time estimation of ozone profiles from GOME measurements

The thermal structure of trace gases, their distribution in the atmosphere, and their circulation mechanisms result from a complex interplay between radiative, physical, and dynamical processes. Neural-network algorithms can be a useful tool to face such complexities in retrieval operations. In this paper, their potentialities have been exploited to design real-time procedures for the estimation of vertical profiles of ozone concentration from spectral radiances measured by GOME, the first instrument of the European Space Agency capable of monitoring global distribution of ozone and other trace gases.

Evaluation of Global Ozone Monitoring Experiment (GOME) ozone profiles from nine different algorithms

An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information and a priori constraints. In total nine different algorithms will be evaluated exploiting the Optimal Estimation (Royal Netherlands Meteorological Institute, Rutherford Appleton Laboratory, University of Bremen, National Oceanic and Atmospheric Administration, Smithsonian Astrophysical Observatory), Phillips-Tikhonov Regularization (Space Research Organization Netherlands), Neural Network (Center for Solar Energy and Hydrogen Research, Tor Vergata University), and Data Assimilation (German Aerospace Center) approaches. Analysis tools are used to interpret data sets that provide averaging kernels. In the interpretation of these data, the focus is on the vertical resolution, the indicative altitude of the retrieved value, and the fraction of a priori information. The evaluation is completed with a comparison of the results to lidar data from the NDSC (Network for Detection of Stratospheric Change) stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (USA), Lauder (New Zealand) and Dumont d’Urville (Antarctic) for the years 1997–1999. In total the comparison involves nearly 1000 ozone profiles, and allows the analysis of GOME data measured in different global regions and hence observational circumstances. The main conclusion of this paper is that unambiguous information on the ozone profile can at best be retrieved in the altitude range 15–48 km with a vertical resolution of 10 to 15 km, precision of 5–10%, and a bias up to 5% or 20% depending on the success of recalibration of the input spectra. The sensitivity of retrievals to ozone at lower altitudes varies from scheme to scheme and includes significant influence from a priori assumptions.

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.

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 system for monitoring NO2 emissions from biomass burning by using GOME and ATSR-2 data

In this paper we propose a system for monitoring abnormal NO 2 emissions in the troposphere by using remote-sensing sensors. In particular, the system aims at estimating the amount of NO 2 resulting from biomass burning by exploiting the synergies between the GOME and the ATSR-2 sensors mounted on board of the ERS-2 satellite. Two different approaches to the estimation of NO 2 are proposed. The former, which is the simpler one, assumes a linear relationship between the GOME and ATSR-2 measurements and the NO 2 concentration. The latter exploits a nonlinear and nonparametric method based on a radial basis function (RBF) neural network. The architecture of such a network is defined in order to retrieve the values of NO 2 concentration on the basis of the GOME and ATSR-2 measurements, as well as of other ancillary input parameters. Experimental results, obtained on a real data set, confirm the effectiveness of the proposed system, which represents a promising tool for operational applications.

Retrieval of ozone profiles by using GOME measurements and a neural network algorithm

GOME is the first instrument of ESA capable of monitoring global distribution of ozone and other trace gases. To extend the use of GOME products further towards more operational applications, it is needed to complement the off-line service with a product delivery which should be as fast as possible. The neural network-based approach proposed in this study gives a contribution to this direction. The retrieval algorithm, which also includes an appropriate normalization of the measured quantities, is presented and the results discussed.

Modification of local urban aerosol properties by long-range transport of biomass burning aerosol

During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the European Aerosol Research Lidar Network (EARLINET network) urban site in Warsaw, Poland. During 24–30 August 2016, the lidar-derived products (boundary layer height, aerosol optical depth, Angstrom exponent, lidar ratio, depolarization ratio) were analysed in terms of air mass transport (HYSPLIT model), aerosol load (CAMS data) and type (NAAPS model) and confronted with active and passive remote sensing at the ground level (PolandAOD, AERONET, WIOS-AQ networks) and aboard satellites (SEVIRI, MODIS, CATS sensors). Optical properties for less than a day-old fresh biomass burning aerosol, advected into Warsaw’s boundary layer from over Ukraine, were compared with the properties of long-range transported 3–5 day-old aged biomass burning aerosol detected in the free troposphere over Warsaw. Analyses of temporal changes of aerosol properties within the boundary layer, revealed an increase of aerosol optical depth and Angstrom exponent accompanied by an increase of surface PM10 and PM2.5. Intrusions of advected biomass burning particles into the urban boundary layer seem to affect not only the optical properties observed but also the top height of the boundary layer, by moderating its increase.

Global stratospheric ozone profiles from GOME in near-real time

The Global Ozone Monitoring Experiment (GOME) on the European Space Agencys (ESA) platform European Remote Sensing Satellite (ERS)-2 provides ozone column densities derived from ultraviolet (UV) Earth-shine spectra in nadir. The UV part of the spectrum also contains information on the vertical ozone distribution. However, until now, ozone profiles were not derived on an operational basis. Numerical weather prediction could benefit from assimilation of stratospheric ozone profiles, if they are retrieved with sufficient coverage of the Earth and within 3–4 h after observation. This requires a fast retrieval algorithm and near-real time (NRT) availability of the spectra. In this Letter, the first operational retrieval of global stratospheric ozone profiles in NRT is described. NRT availability of the spectra is assured by including carefully selected parts of the raw data in the GOME instrument monitoring files, which are obtained directly from the ESA ground stations. An existing off-line profile retrieval algorithm has been adapted to produce reliable stratospheric profiles within strict time constraints. The consequences for the quality of the profiles have been discussed.

ENVISAT-SCIAMACHY: instrument commissioning & early results

The Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) will be one of the atmospheric sounders on board Envisat, to be launched in early 2002. The instrument is designed to detect solar light scattered from the atmosphere in either nadir, limb or occultation geometry. Following the successful launch of Envisat and an initial outgassing phase activities will focus on instrument functional and performance verification, consolidation as well as geophysical validation of the operational data products. Activities will be conducted in a period 1...9 months after launch and involve a huge number of research teams, organised within the Envisat Atmospheric Chemistry Validation Team (ACVT).