Diego Loyola

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.

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.

Automatic cloud analysis from polar-orbiting satellites using neural network and data fusion techniques

This paper presents the automatic retrieval of cloud information combining two algorithms that use optical and spectral information. The first algorithm is based on data fusion techniques and determines the cloud fraction using optical information in the UV/VIS range. The second algorithm is based on neural networks techniques and determines the cloud-top height and cloud-top albedo using spectral information in the Oxygen A-band in and around 760 nm.

Space-based measurements of air quality during the World Expo 2010 in Shanghai

During the World Exposition 2010 (Expo, from May to October), emission control measures were implemented in Shanghai and surrounding areas to improve the air quality. To evaluate the effect of these measures, we use the tropospheric NO2 column, aerosol optical thickness (AOT) and CO concentration observations from the satellite instruments GOME-2, MODIS and MOPITT, respectively. The analysis shows about 8% and 14% reductions of tropospheric NO2 columns and AOT respectively over Shanghai during the Expo period, compared to the past three years. A 12% reduction of CO concentration at 700 hPa over Shanghai and surrounding areas is found during the Expo period. On the other hand, the satellite measurements show increases of NO2 by 20% and AOT by 23% over Shanghai urban areas after the Expo (November 2010–April 2011), when the short-term emission control measures were lifted. Our study indicates that the air quality measures were effective in Shanghai and surrounding provinces during the Expo period.

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.

GOME level 1-to-2 data processor version 3.0: a major upgrade of the GOME/ERS-2 total ozone retrieval algorithm

The global ozone monitoring experiment (GOME) was launched in April 1995, and the GOME data processor (GDP) retrieval algorithm has processed operational total ozone amounts since July 1995. GDP level 1-to-2 is based on the two-step differential optical absorption spectroscopy (DOAS) approach, involving slant column fitting followed by air mass factor (AMF) conversions to vertical column amounts. We present a major upgrade of this algorithm to version 3.0. GDP 3.0 was implemented in July 2002, and the 9-year GOME data record from July 1995 to December 2004 has been processed using this algorithm. The key component in GDP 3.0 is an iterative approach to AMF calculation, in which AMFs and corresponding vertical column densities are adjusted to reflect the true ozone distribution as represented by the fitted DOAS effective slant column. A neural network ensemble is used to optimize the fast and accurate parametrization of AMFs. We describe results of a recent validation exercise for the operational version of the total ozone algorithm; in particular, seasonal and meridian errors are reduced by a factor of 2. On a global basis, GDP 3.0 ozone total column results lie between -2% and +4% of ground-based values for moderate solar zenith angles lower than 70 degrees. A larger variability of about +5% and -8% is observed for higher solar zenith angles up to 90 degrees.

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.

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.