GOME Data Processor Research Articles

An improved total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

Abstract. An improved algorithm for the retrieval of total and tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment-2 (GOME-2) is presented. The refined retrieval will be implemented in a future version of the GOME Data Processor (GDP) as used by the EUMETSAT Satellite Application Facility on Atmospheric Composition and UV Radiation (AC-SAF). The first main improvement is the application of an extended 425–497 nm wavelength fitting window in the differential optical absorption spectroscopy (DOAS) retrieval of the NO2 slant column density, based on which initial total NO2 columns are computed using stratospheric air mass factors (AMFs). Updated absorption cross sections and a linear offset correction are used for the large fitting window. An improved slit function treatment is applied to compensate for both long-term and in-orbit drift of the GOME-2 slit function. Compared to the current operational (GDP 4.8) dataset, the use of these new features increases the NO2 columns by ∼1–3×1014 molec cm2 and reduces the slant column error by ∼24 %. In addition, the bias between GOME-2A and GOME-2B measurements is largely reduced by adopting a new level 1b data version in the DOAS retrieval. The retrieved NO2 slant columns show good consistency with the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval with a good overall quality. Second, the STRatospheric Estimation Algorithm from Mainz (STREAM), which was originally developed for the TROPOspheric Monitoring Instrument (TROPOMI) instrument, was optimised for GOME-2 measurements to determine the stratospheric NO2 column density. Applied to synthetic GOME-2 data, the estimated stratospheric NO2 columns from STREAM shows good agreement with the a priori truth. An improved latitudinal correction is introduced in STREAM to reduce the biases over the subtropics. Applied to GOME-2 measurements, STREAM largely reduces the overestimation of stratospheric NO2 columns over polluted regions in the GDP 4.8 dataset. Third, the calculation of AMF applies an updated box-air-mass factor (box-AMF) look-up table (LUT) calculated using the latest version 2.7 of the Vector-LInearized Discrete Ordinate Radiative Transfer (VLIDORT) model with an increased number of reference points and vertical layers, a new GOME-2 surface albedo climatology, and improved a priori NO2 profiles obtained from the TM5-MP chemistry transport model. A large effect (mainly enhancement in summer and reduction in winter) on the retrieved tropospheric NO2 columns by more than 10 % is found over polluted regions. To evaluate the GOME-2 tropospheric NO2 columns, an end-to-end validation is performed using ground-based multiple-axis DOAS (MAXDOAS) measurements. The validation is illustrated for six stations covering urban, suburban, and background situations. Compared to the GDP 4.8 product, the new dataset presents improved agreement with the MAXDOAS measurements for all the stations.

The Global Ozone Monitoring Experiment: review of in-flight performance and new reprocessed 1995–2011 level 1 product

Abstract. The Global Ozone Monitoring Experiment (GOME) on-board the second European Remote Sensing satellite provided measurements of atmospheric constituents such as ozone and other trace gases for the 16-year period from 1995 to 2011. In this paper we present a detailed analysis of the long-term performance of the sensor and introduce the new homogenised and fully calibrated level 1 product that has been generated using the recently developed GOME Data Processor level-0-to-1b (GDP-L1) Version 5.1. By means of the various in-flight calibration parameters, we monitor the behaviour and stability of the instrument during the entire mission. Severe degradation of the optical components has led to a significant decrease in intensity, in particular in channels 1 and 2, which cover the spectral ranges of 240–316 and 311–405 nm, respectively. Thus, a soft correction based on using the Sun as a stable calibration source is applied. Revision and optimisation of other calibration algorithms such as the wavelength assignment, polarisation correction, and dark current correction resulted in an improved and homogeneous level 1 product that can be regarded as the European satellite reference data for successor atmospheric composition sensors and that provides an excellent prerequisite for further exploitation of GOME measurements.

Validation of GOME-2/Metop total column water vapour with ground-based and in situ measurements

Abstract. The total column water vapour product from the Global Ozone Monitoring Experiment-2 on board Metop-A and Metop-B satellites (GOME-2/Metop-A and GOME-2/Metop-B) produced by the Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) is compared with co-located radiosonde observations and global positioning system (GPS) retrievals. The validation is performed using recently reprocessed data by the GOME Data Processor (GDP) version 4.7. The time periods for the validation are January 2007–July 2013 (GOME-2A) and December 2012–July 2013 (GOME-2B). The radiosonde data are from the Integrated Global Radiosonde Archive (IGRA) maintained by the National Climatic Data Center (NCDC). The ground-based GPS observations from the COSMIC/SuomiNet network are used as the second independent data source. We find a good general agreement between the GOME-2 and the radiosonde/GPS data. The median relative difference of GOME-2 to the radiosonde observations is −2.7 % for GOME-2A and −0.3 % for GOME-2B. Against the GPS, the median relative differences are 4.9 % and 3.2 % for GOME-2A and B, respectively. For water vapour total columns below 10 kg m−2, large wet biases are observed, especially against the GPS retrievals. Conversely, at values above 50 kg m−2, GOME-2 generally underestimates both ground-based observations.

Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network

Abstract. The main goal of this paper is to validate the total water vapour column (TWVC) measured by the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor and generated using the GOME Data Processor (GDP) retrieval algorithm developed by the German Aerospace Centre (DLR). For this purpose, spatially and temporally collocated TWVC data from highly accurate sounding measurements for the period January 2009–May 2014 at six sites are used. These balloon-borne data are provided by the GCOS Reference Upper-Air Network (GRUAN). The correlation between GOME-2 and sounding TWVC data is reasonably good (determination coefficient, R2, of 0.89) when all available radiosondes (1400) are employed in the inter-comparison. When cloud-free cases (544) are selected by means of the satellite cloud fraction (CF < 5%), the correlation exhibits a remarkable improvement (R2 ~ 0.95). Nevertheless, the analysis of the relative differences between GOME-2 and GRUAN data shows a mean absolute bias error (weighted with the combined uncertainty derived from the estimated errors of both data sets) of 15% for all-sky conditions (9% for cloud-free cases). These results evidence a notable bias in the satellite TWVC data against the reference balloon-borne measurements, partially related to the cloudy conditions during the satellite overpass. The detailed analysis of the influence of cloud properties – CF, cloud top albedo (CTA) and cloud top pressure (CTP) – on the satellite-sounding differences reveals, as expected, a large effect of clouds in the GOME-2 TWVC data. For instance, the relative differences exhibit a large negative dependence on CTA, varying from −6 to −23% when CTA rises from 0.3 to 0.8. Furthermore, the satellite-sounding TWVC differences show a strong dependence on the satellite solar zenith angle (SZA) for values above 50°. Hence the smallest relative differences found in this satellite-sounding comparison are achieved for those cloud-free cases with satellite SZA below 50°. Finally, the relative differences also show a negative dependence on the reference TWVC values, e.g. changing from +10% (TWVC below 10 mm) to −10% (TWVC above 40 mm) when cloud-free conditions with SZA below 50° are selected. Overall, relative differences within ±10% with respect to reference sounding data for a large range of TWVC values can be considered as a good result for satellite retrievals.

Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

Abstract. Knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors aboard EUMETSAT's MetOp-A and MetOp-B satellites and perform an extensive inter-comparison in order to evaluate their consistency and temporal stability. For the analysis, the GOME-2 data sets are generated by DLR in the framework of the EUMETSAT O3M-SAF project using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines a H2O and O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O total column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. The overall consistency between measurements from the newer GOME-2 instrument on board of the MetOp-B platform and the GOME-2/MetOp-A data is evaluated in the overlap period (December 2012–June 2014). Furthermore, we compare GOME-2 results with independent TCWV data from the ECMWF ERA-Interim reanalysis, with SSMIS satellite measurements during the full period January 2007–June 2014 and against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project (January 2007–December 2008). Global mean biases as small as ±0.035 g cm−2 are found between GOME-2A and all other data sets. The combined SSM/I-MERIS sample and the ECMWF ERA-Interim data set are typically drier than the GOME-2 retrievals, while on average GOME-2 data overestimate the SSMIS measurements by only 0.006 g cm−2. However, the size of these biases is seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, which include only data over ocean. The seasonal behaviour is not as evident when comparing GOME-2 TCWV to the ECMWF ERA-Interim and the SSM/I+MERIS data sets, since the different biases over land and ocean surfaces partly compensate each other. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three data sets, especially for land areas, although some discrepancies (bias larger than ±0.5 g cm−2) over ocean and over land areas with high humidity or a relatively large surface albedo are observed.

GOME-2 total ozone columns from MetOp-A/MetOp-B and assimilation in the MACC system

Abstract. The two Global Ozone Monitoring Instrument (GOME-2) sensors operated in tandem are flying onboard EUMETSAT's (European Organisation for the Exploitation of Meteorological Satellites) MetOp-A and MetOp-B satellites, launched in October 2006 and September 2012 respectively. This paper presents the operational GOME-2/MetOp-A (GOME-2A) and GOME-2/MetOp-B (GOME-2B) total ozone products provided by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). These products are generated using the latest version of the GOME Data Processor (GDP version 4.7). The enhancements in GDP 4.7, including the application of Brion–Daumont–Malicet ozone absorption cross sections, are presented here. On a global scale, GOME-2B has the same high accuracy as the corresponding GOME-2A products. There is an excellent agreement between the ozone total columns from the two sensors, with GOME-2B values slightly lower with a mean difference of only 0.55±0.29%. First global validation results for 6 months of GOME-2B total ozone using ground-based measurements show that on average the GOME-2B total ozone data obtained with GDP 4.7 are slightly higher than, both, Dobson observations by about 2.0±1.0% and Brewer observations by about 1.0±0.8%. It is concluded that the total ozone columns (TOCs) provided by GOME-2A and GOME-2B are consistent and may be used simultaneously without introducing systematic effects, which has been illustrated for the Antarctic ozone hole on 18 October 2013. GOME-2A total ozone data have been used operationally in the Copernicus atmospheric service project MACC-II (Monitoring Atmospheric Composition and Climate – Interim Implementation) near-real-time (NRT) system since October 2013. The magnitude of the bias correction needed for assimilating GOME-2A ozone is reduced (to about −6 DU in the global mean) when the GOME-2 ozone retrieval algorithm changed to GDP 4.7.

Tropical tropospheric ozone column retrieval for GOME-2

Abstract. This paper presents the operational retrieval of tropical tropospheric ozone columns (TOCs) from the Second Global Ozone Monitoring Experiment (GOME-2) instruments using the convective-cloud-differential (CCD) method. The retrieval is based on total ozone and cloud property data provided by the GOME Data Processor (GDP) 4.7, and uses above-cloud and clear-sky ozone column measurements to derive a monthly mean TOC between 20° N and 20° S. Validation of the GOME-2 TOC with several tropical ozonesonde sites shows good agreement, with a high correlation between the GOME-2 and sonde measurements, and small biases within ~ 3 DU. The TOC data have been used in combination with tropospheric NO2 measurements from GOME-2 to analyse the effect of the 2009–2010 El Niño–Southern Oscillation (ENSO) on the tropospheric ozone distribution in the tropics. El Niño induced dry conditions in September–October 2009 resulted in relatively high tropospheric ozone columns over the southern Indian Ocean and northern Australia, while La Niña conditions in September–October 2010 resulted in a strong increase in tropospheric NO2 in South America, and enhanced ozone in the eastern Pacific and South America. Comparisons of the GOME-2 tropospheric ozone data with simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model for 2009 El Niño conditions illustrate the usefulness of the GOME-2 TOC measurements in evaluating chemistry climate models (CCMs). Evaluation of CCMs with appropriate satellite observations helps to identify strengths and weaknesses of the model systems, providing a better understanding of driving mechanisms and adequate relations and feedbacks in the Earth atmosphere, and finally leading to improved models.

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

AbstractWithin the European Space Agency's 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.

Operational total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

Abstract. This paper presents the algorithm for the operational near real time retrieval of total and tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME-2). The retrieval is performed with the GOME Data Processor (GDP) version 4.4 as used by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). The differential optical absorption spectroscopy (DOAS) method is used to determine NO2 slant columns from GOME-2 (ir)radiance data in the 425–450 nm range. Initial total NO2 columns are computed using stratospheric air mass factors, and GOME-2 derived cloud properties are used to calculate the air mass factors for scenarios in the presence of clouds. To obtain the stratospheric NO2 component, a spatial filtering approach is used, which is shown to be an improvement on the Pacific reference sector method. Tropospheric air mass factors are computed using monthly averaged NO2 profiles from the MOZART-2 chemistry transport model. An error analysis shows that the random error in the GOME-2 NO2 slant columns is approximately 0.45 × 1015 molec cm−2. As a result of the improved quartz diffuser plate used in the GOME-2 instrument, the systematic error in the slant columns is strongly reduced compared to GOME/ERS-2. The estimated uncertainty in the GOME-2 tropospheric NO2 column for polluted conditions ranges from 40 to 80 %. An end-to-end ground-based validation approach for the GOME-2 NO2 columns is illustrated based on multi-axis MAXDOAS measurements at the Observatoire de Haute Provence (OHP). The GOME-2 stratospheric NO2 columns are found to be in good overall agreement with coincident ground-based measurements at OHP. A time series of the MAXDOAS and the GOME-2 tropospheric NO2 columns shows that pollution episodes at OHP are well captured by GOME-2. Monthly mean tropospheric columns are in very good agreement, with differences generally within 0.5 × 1015 molec cm−2.

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

The Global Ozone Monitoring Instrument (GOME-2) was launched on EUMESAT's 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.

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.

Six years of total ozone column measurements from SCIAMACHY nadir observations

Abstract. Total O3 columns have been retrieved from six years of SCIAMACHY nadir UV radiance measurements using SDOAS, an adaptation of the GDOAS algorithm previously developed at BIRA-IASB for the GOME instrument. GDOAS and SDOAS have been implemented by the German Aerospace Center (DLR) in the version 4 of the GOME Data Processor (GDP) and in version 3 of the SCIAMACHY Ground Processor (SGP), respectively. The processors are being run at the DLR processing centre on behalf of the European Space Agency (ESA). We first focus on the description of the SDOAS algorithm with particular attention to the impact of uncertainties on the reference O3 absorption cross-sections. Second, the resulting SCIAMACHY total ozone data set is globally evaluated through large-scale comparisons with results from GOME and OMI as well as with ground-based correlative measurements. The various total ozone data sets are found to agree within 2% on average. However, a negative trend of 0.2–0.4%/year has been identified in the SCIAMACHY O3 columns; this probably originates from instrumental degradation effects that have not yet been fully characterized.

Ten years of GOME/ERS2 total ozone data—The new GOME data processor (GDP) version 4: 2. Ground‐based validation and comparisons with TOMS V7/V8

The atmospheric chemistry instrument Global Ozone Monitoring Experiment (GOME) was launched in April 1995 on the ERS‐2 platform. The GOME data processor (GDP) operational retrieval algorithm has produced total ozone columns since July 1995. With a data record of over ten years, GOME has become important for ozone trend analysis. In 2004, GDP was upgraded to version 4.0, a new validation was performed, and the entire GOME data record was reprocessed. In the preceding paper (Van Roozendael et al., 2006), the GDP 4.0 algorithm was described. In this paper, we deal with geophysical validation of the GDP 4.0 algorithm and the retrieved ozone products. We present results of a validation exercise involving comparisons of GDP 4.0 total ozone with the Network for Detection of Stratospheric Change (NDSC) and the World Meteorological Organization (WMO)/Global Atmospheric Watch (GAW) ground‐based networks. We compare these results with similar validations of earlier GDP ozone products. We also present ground‐based validation of TOMS versions 7 and 8 total ozone products, and we contrast these with GDP 4.0 values. On a global basis, GDP 4.0 total ozone results lie between −1% and +1.5% of ground‐based values for solar zenith angles less than 70°; accuracy is now comparable to that obtainable from ground‐based stations. At higher solar zenith angles in polar regions, larger discrepancies of up to +5% are found; in these regimes, errors on both satellite and ground‐based measurements are higher. The validation also showed marked improvement in TOMS total ozone performance for the version 8 algorithm.

GOME level 1-to-2 data processor version 30: 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.

Pole-to-pole validation of GOME WFDOAS total ozone with groundbased data

Abstract. This paper summarises the validation of GOME total ozone retrieved using the Weighting Function Differential Optical Absorption Spectroscopy (WFDOAS) algorithm Version 1.0. This algorithm has been described in detail in a companion paper by Coldewey-Egbers et al. (2005). Compared to the operational GDP (GOME Data Processor) V3, several improvements to the total ozone retrieval have been introduced that account for the varying ozone dependent contribution to rotational Raman scattering, includes a new cloud scheme, and uses the GOME measured effective albedo in the retrieval. In this paper the WFDOAS results have been compared with selected ground-based measurements from the WOUDC (World Ozone and UV Radiation Data Centre) that collects total ozone measurements from a global network of stations covering all seasons. From the global validation excellent agreement between WFDOAS and ground data was observed. The agreement lies within ±1%, and very little seasonal variations in the differences are found. In the polar regions and at high solar zenith angles, however, a positive bias varying between 5 and 8% is found near the polar night period. As a function of solar zenith angle as well as of the retrieved total ozone, the WFDOAS differences to ground polar data, however, show a much weaker dependence as compared to the operational GOME Data Processor Version 3 of GOME that represents a significant improvement. Very few stations carry out simultaneous measurements by Brewer and Dobson spectrometers over an extended period (three years or more). Simultaneous Brewer and Dobson measurements from Hradec Kralove, Czech Republic (50.2N, 15.8E) and Hohenpeissenberg, Germany (47.8N, 11.0E) covering the period 1996-1999 have been compared with our GOME results. Agreement with Brewers are generally better than with the simultaneous Dobson measurements and this may be explained by the neglect of stratospheric (ozone) temperature correction in the standard ozone retrieval from the ground.

Comparison of total ozone from the satellite instruments GOME and TOMS with measurements from the Dobson network 1996–2000

Abstract. Over the last 3 decades, satellite data have been used to monitor long-term global changes in stratospheric ozone. The TOMS series (1978-present) and GOME (1995-present) are two very important instruments in this context. In this paper, TOMS total ozone and three approaches to derive total ozone from GOME measurements are validated with ground-based Dobson network data. Beyond the operational products of both instruments, e.g. TOMS version 7 and GOME Data Processor version 2.7, total ozone is calculated by integrating FURM ozone profiles and by applying the TOMS algorithm to the GOME spectra. All algorithms show in general good agreement with ground-based measurements. The operational GOME total ozone shows seasonal variations, most likely introduced by difficulties in the derivation of airmass factors, which convert measured slant columns into vertical columns. The TOMS algorithm estimates on average 2% higher total ozone in the southern hemisphere than in the northern for both instruments as compared to the ground-based data, indicating that the source of the observed hemispheric differences is in the TOMS algorithm. Both instruments show aging effects in 2000, leading to enhanced variability in the ozone column differences with respect to Dobson data. In addition, the integrated GOME ozone profiles and the TOMS algorithm applied to GOME data show larger mean deviations in 2000.

Wavelength calibration of spectra measured by the Global Ozone Monitoring Experiment by use of a high-resolution reference spectrum.

Earthshine spectra measured by the nadir-viewing Global Ozone Monitoring Experiment (GOME) spectrometer aboard the second European Remote Sensing (ERS-2) Satellite in the range of 240-790 nm are widely used for the retrieval of concentrations and vertical profiles of atmospheric trace gases. For the near-real-time delivery of ozone columns and profiles at the Royal Netherlands Meterological Institute, a tailor-made wavelength calibration method was developed. The method use a high-resolution (0.01-nm) solar spectrum as the reference spectrum and applies both a shift and a squeeze to the wavelengths in selected windows to find the optimal wavelength grid per window. This method provides a calibration accuracy of 0.002 nm below and 0.001 nm above 290 nm. The new wavelength calibration method can be used on any wavelength window, for example, to improve the calibration of spectra from the GOME Data Processor. A software package, GomeCal, which performs this recalibration, along with an improved polarization and radiometric correction, has been made and has been released via the World Wide Web. The method can be used for any high-resolution (ir)radiance spectrometer, such as the satellite instruments SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography), Ozone Monitoring Instrument, and GOME-2.

A Fast Delivery system for the retrieval of near real-time ozone columns from GOME data

A Fast Delivery (FD) system has been developed to provide GOME (Global Ozone Monitoring Experiment) total ozone columns within 3 h after observation. This meets the growing demand for near real-time ozone products for applications such as the assimilation of ozone in Numerical Weather Prediction (NWP) models, monitoring the status of the ozone layer and improving surface UV radiation forecasts. The FD system uses only a small part of the raw GOME data that are available in near real-time. The FD level 0 to 1 and level 1 to 2 processors are based on algorithms as used within the off-line ESA/DLR GOME Data Processor (GDP). However, several changes in the algorithms have been made to improve the retrieval of the ozone columns: (1) the use of effective ozone absorption cross-sections based on ECMWF (the European Centre for Medium-Range Weather Forecasts) temperature forecast in the ozone slant column fit, (2) the use of the DAK radiative transfer model and the Fortuin and Kelder ozone climatology for the air mass factor calculations and (3) the use of the FRESCO (Fast Retrieval Scheme for Cloud Observables) cloud algorithm. The accuracy of the FD ozone columns has been determined by a pseudo-global validation study involving ground-based total ozone observations from about 15 NDSC (Network for the Detection of Stratospheric Change) sites. There is a reasonable general agreement with the ground-based measurements (within 5% for low and mid latitudes, within m 10 to +5% for high latitudes) and the level of quality meets the requirements for near real-time applications.