Global Ozone Monitoring Experiment Data Research Articles

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.

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.

Three years of ground-based total ozone measurements in the Arctic: Comparison with OMI, GOME and SCIAMACHY satellite data

Continuous ground-based total ozone measurements were carried out at Ny-Ålesund (78.9°N, 11.9° E, Svalbard islands, Norway), by using a Brewer spectrophotometer, for almost three years (2007–2009). Given the scarcity of high-quality ground-based measurements performed at high latitudes, this dataset provides a rare opportunity to check the quality of satellite-based data recorded in Arctic. Considering the unprecedented Artic ozone loss recently derived from satellite observations, assessing the reliability of satellite readings at such high latitudes is a timely effort. This study focuses on the comparisons between ground-based Brewer total ozone measurements and satellite ozone readings retrieved from EOS Aura Ozone Measurement Instrument (OMI) (by using OMI-TOMS and OMI-DOAS algorithms), retrieved from ERS-2 Global Ozone Monitoring Experiment (GOME) (by using TOGOMI algorithm) and from Envisat SCanning Imaging Absorption spectrometer for Atmospheric CartographY (SCIAMACHY) (by using TOSOMI algorithm).Despite the extreme conditions that affect both ground- and satellite-based measurements, a good agreement was found (r=0.99 for OMI-TOMS, r=0.97 for both OMI-DOAS and GOME datasets and r=0.96 for SCIAMACHY). Nevertheless, space-based total ozone readings were found to underestimate ground-based data, in agreement with prior results. OMI-TOMS data showed the highest negative mean bias (MB=−2.03%) whereas DOAS-based datasets showed a better agreement (MB=−0.47, −0.69 and −0.01% for OMI-DOAS, GOME and SCIAMACHY, respectively) but a greater data scatter. On the other hand, DOAS-based datasets present an important seasonal dependence; an overestimation of the Brewer total ozone up to about 3% is found in April and September with respect to OMI-DOAS and SCIAMACHY while in September with respect to GOME. In contrast, differences between OMI-TOMS and Brewer total ozone do not present significant dependence on the season or on geometrical parameters (i.e., satellite solar and viewing zenith angles). Differences in total ozone values between ground-based and satellite measurements are further discussed attending to the algorithm differences in dealing with cloud cover under high surface albedo (i.e., snow) conditions.Extending the study to Sodankyla station (67.3°N, 26.6°E, Finland), similarities and differences between the two stations have been highlighted. The agreement between satellite and Brewer data slightly improved when Sodankyla station is considered. On the continent, the seasonal dependence and data scatter for DOAS-based datasets and the MB for OMI-TOMS are smaller with respect to Ny-Ålesund. Moreover, the roughly constant ozone summer offset (about 1%) between OMI-TOMS and OMI-DOAS, well evident for Ny-Ålesund, is not present for Sodankyla. Finally, some patterns in the geographic distributions of the satellite-Brewer total ozone differences are evident only for Ny-Ålesund, partly because of the peculiar characteristics of the surface albedo (higher than in Sodankyla, with a strong water/land contrast).

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.

Earth surface reflectance climatology from 3 years of OMI data

Global maps of the Earth's surface Lambertian equivalent reflectance (LER) are constructed using 3 years of Ozone Monitoring Instrument (OMI) measurements obtained between October 2004 and October 2007 at 23 wavelengths between 328 and 500 nm. The maps are constructed on a 0.5° by 0.5° longitude‐latitude grid for each calendar month using an algorithm based on temporal histograms of the observed LER values per geophysical location. The algorithm allows seasonal effects related to vegetation, snow, and ice but excludes statistical outliers. The maps show typical features like open ocean regions with high reflectivity indicative of low phytoplankton levels, coastal waters with high reflectance caused by silt, and oceanic regions with low reflectance correlated with chlorophyll. Open oceans in general have a higher reflectivity than does land up to 420 nm. The highest reflectivity values of oceans occur at 380 nm. Good agreement is found with a similar LER map based on data from the Total Ozone Mapping Spectrometer (TOMS) at 331, 340, 360, and 380 nm, which is 0.015 lower on average. The comparison with data from the Global Ozone Monitoring Experiment (GOME) at 335, 380, 440, and 494 nm is also satisfactory, being 0.005 lower on average. The LER derived from OMI data is approximately 0.02 higher than the black sky albedo as derived from the Moderate Resolution Imaging Spectroradiometer at 470 nm, which is partly related to viewing geometry effects of the bidirectional reflectance distribution function of the surface. The data set presented contains residual cloud features over tropical rain forest regions, has a higher spatial resolution than those created using TOMS and GOME data, and includes more wavelengths.

Retrieval of cloud parameters from satellite‐based reflectance measurements in the ultraviolet and the oxygen A‐band

We present a new method for the retrieval of cloud fraction, cloud optical thickness and cloud top pressure from measurements of the Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), relevant for the retrieval of trace gas abundances in cloudy atmospheres. The presented algorithm makes use of the measurements of the oxygen A‐band between 758 and 770 nm and additionally of measurements in the ultraviolet between 350 and 390 nm. In addition to the cloud parameters, information about the surface reflection is retrieved. It is shown that measurements in the oxygen A‐band alone contain significant information about only two cloud parameters. By adding measurements in the ultraviolet, information about cloud fraction, cloud optical thickness, and cloud top pressure can be retrieved. The cloud retrieval algorithm was applied to GOME data, and the results are compared to cloud parameters retrieved from measurements of the Along Track Scanning Radiometer 2 (ATSR‐2). The cloud fractions, cloud optical thickness, and cloud top pressures retrieved by our algorithm and the corresponding ATSR‐2 values have a median difference of −0.01, 2.5 and −33 hPa, respectively, with 68% confidence intervals of ±0.11, ±7.5 and ±111 hPa, respectively.

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.

Improved ozone profile retrievals from GOME data with degradation correction in reflectance

Abstract. We present a simple method to perform degradation correction to Global Ozone Monitoring Experiment (GOME) reflectance spectra by comparing the average reflectance for 60° N–60° S with that at the beginning of GOME observations (July–December 1995) after removing the dependences on solar zenith angle and seasonal variation. The results indicate positive biases of up to ~15–25% in the wavelength range 289–370 nm during 2000–2002; the degradation also exhibits significant dependence on wavelength and viewing zenith angle. These results are consistent with previous studies using radiative transfer models and ozone observations. The degradation causes retrieval biases of up to ~3% (10 DU, 1 DU=2.69×1016 molecules cm−2), 30% (10 DU), 10%, and 40% in total column ozone, tropospheric column ozone, stratospheric ozone and tropospheric ozone, respectively, from our GOME ozone profile retrieval algorithm. In addition, retrieval biases due to degradation vary significantly with latitude. The application of this degradation correction improves the retrievals relative to Dobson and ozonesonde measurements at Hohenpeißenberg station during 2000–2003 and improves the spatiotemporal consistency of retrieval quality during 1996–2003. However, because this method assumes that the deseasonalized globally-averaged reflectance does not change much with time, retrievals with this correction may be inadequate for trend analysis. In addition, it does not correct for instrument biases that have occurred since launch.

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 Network for Detection of Stratospheric Change stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (Hawaii), 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.

The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data

Abstract. Information about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms.

Intercomparison of GOME, ozonesonde, and SAGE II measurements of ozone: Demonstration of the need to homogenize available ozonesonde data sets

We investigate the large systematic biases, especially in the stratosphere, between ozone profiles retrieved from the Global Ozone Monitoring Experiment (GOME) and ozonesonde observations at some ozonesonde stations. GOME retrievals are intercompared with both ozonesonde data at 33 stations between 75°N and 71°S and Stratospheric Aerosol and Gas Experiment II (SAGE II) data during 1996–1999. GOME stratospheric column ozone (SCO) over the altitude range ∼15–35 km usually agrees with SAGE II SCO to within 2.5 DU (1.5%, 1 DU = 2.69 × 1016 molecules cm−2) without significant spatiotemporal dependence but is systematically larger than ozonesonde SCO by 8–20 DU (5–10%) over carbon iodine (i.e., an ozonesonde technique) stations and most stations within 30°N–30°S. Evaluation of GOME, SAGE II, TOMS, and Dobson data here demonstrates that those biases mainly originate from ozonesonde underestimates in the stratosphere. GOME retrievals also show large positive biases of 20–70% at carbon iodine stations (except for Syowa) and most stations within 30°N–30°S over ∼10–20 km, where ozone concentration is low, while the biases relative to SAGE II data over ∼15–20 km is usually 10–20%. The discrepancies over this altitude region reflect biases in GOME retrievals as well as ozonesonde measurements. In addition, GOME/sonde biases in both SCO and profiles (especially in the lower stratosphere and upper troposphere) vary from station to station and depend on sonde technique, instrument type, sensor solution, and data processing, demonstrating the need to homogenize available ozonesonde data sets and standardize future operational procedures for reliable and consistent satellite validation.

Intercomparison of reflectances observed by GOME and SCIAMACHY in the visible wavelength range

We compare the Earth reflectances of the spectrometers Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) over their overlapping wavelength range (240-800 nm). The goal is to investigate the quality of the radiometric calibration of SCIAMACHY using calibrated GOME data as a reference. However, severe degradation of the GOME instrument in the UV since 2001 prevents it from being a reliable reference below 500 nm. Above 500 nm, GOME is reliable and we find substantial disagreement between GOME and SCIAMACHY, of the order of 15%-20%, which we can attribute completely to the current calibration problems of SCIAMACHY. These numbers are supported by a previous study in which SCIAMACHY was compared with the imager Medium Resolution Imaging Spectrometer (MERIS) onboard the Envisat satellite.

Estimating the NO<sub>x</sub> produced by lightning from GOME and NLDN data: a case study in the Gulf of Mexico

Abstract. Nitrogen oxides (NOxNO+NO2) play an important role in tropospheric chemistry, in particular in catalytic ozone production. Lightning provides a natural source of nitrogen oxides, dominating the production in the tropical upper troposphere, with strong impact on tropospheric ozone and the atmosphere's oxidizing capacity. Recent estimates of lightning produced NOx (LNOx) are of the order of 5 Tg [N] per year with still high uncertainties in the range of one order of magnitude. The Global Ozone Monitoring Experiment (GOME) on board the ESA-satellite ERS-2 allows the retrieval of tropospheric column densities of NO2 on a global scale. Here we present the GOME NO2 measurement directly over a large convective system over the Gulf of Mexico. Simultaneously, cloud-to-ground (CG) flashes are counted by the U.S. National Lightning Detection Network (NLDNTM), and extrapolated to include intra-cloud (IC)+CG flashes based on a climatological IC:CG ratio derived from NASA's space-based lightning sensors. A series of 14 GOME pixels shows largely enhanced column densities over thick and high clouds, coinciding with strong lightning activity. The enhancements can not be explained by transport of anthropogenic NOx and must be due to fresh production of LNOx. A quantitative analysis, accounting in particular for the visibility of LNOx from satellite, yields a LNOx production of 90 (32-240) moles of NOx, or 1.3 (0.4-3.4) kg [N], per flash. If simply extrapolated, this corresponds to a global LNOx production of 1.7 (0.6-4.7)Tg [N]/yr.

Comparison of model-simulated tropospheric NO 2 over China with GOME-satellite data

Tropospheric NO 2 column densities over China simulated with a regional model using different emission inventory input are compared with Global Ozone Monitoring Experiment (GOME) satellite data. These emission inventories include (i) emission estimates for the year 1995 from the Emission Database for Global Atmospheric Research (EDGAR), (ii) regional emission inventory used in the Transport and Chemical Evolution over the Pacific (TRACE-P) program with emission estimates for the year 2000 and (iii) national emission inventory used in the Chinese Ozone Research Programme (CORP) with emission estimates for the year 1995. Model simulations were performed for a summertime period and the results under clear-sky conditions were selected for comparison with GOME data of the years 1996 and 2000. The model generally reproduces high tropospheric NO 2 column densities in polluted areas of China that have been observed by GOME. However, the model simulations do not agree with the GOME measurements in a quantitative sense for some regions. Region-to-region comparisons show that with all the emission inventories the model underestimates the tropospheric NO 2 column density in remote and rural areas of China. It is found that TRACE-P underestimates the tropospheric NO 2 column density in all the regions with respect to the GOME measurements (by more than 50%). CORP and EDGAR appear to behave well for the model simulations in the North of China (within 15% deviations), but poorer for the model simulations in other regions (within 30–80% deviations). Linear regressions were performed with the NO 2 column densities available from GOME, Y, and the model, X, of each grid cell in selected regions. For all the region of China, comparison statistics are Y = 0.3 6 1 × X + 0.9 3 5 with r 2 = 0.3 6 0 and bias=−41% for EDGAR, Y = 1.1 2 4 × X + 0.9 2 0 with r 2 = 0.4 7 2 and bias=−67% for TRACE-P, and Y = 0.4 3 1 × X + 0.8 3 5 with r 2 = 0.5 0 9 and bias=−31% for CORP.

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.

Kelvin wave signatures in ECMWF meteo fields and Global Ozone Monitoring Experiment (GOME) ozone columns

This study investigates the vertical structure of the Kelvin wave signals previously found in total ozone column measurements from the Global Ozone Monitoring Experiment (GOME) instrument. For this, zonal wind and temperature measurements from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re‐Analysis data set are analyzed by using the same bidimensional spectral method as was used to analyze the GOME total ozone columns. These fields are available on 60 levels from the surface to 0.1 hPa. For the three high Kelvin wave activity periods identified in the GOME data we found spectral features in the ECMWF fields associated with Kelvin waves with zonal wave numbers 1 or 2 and periods around 15–20 days. These characteristics correspond to the characteristics of the Kelvin waves detected in GOME. The signals are significant throughout the lower stratosphere between ∼100 and 10 hPa and, depending on the period, are largest around 15, 45, or 65 hPa. There is a good correlation between the Kelvin wave signals in the ECMWF zonal wind and temperature and the GOME total ozone column. The induced fluctuations in zonal wind and temperature are, respectively, up to 8 m/s and 2 K. From these induced zonal wind fluctuations, expected total ozone column fluctuations of around 1 DU are calculated, corresponding to the ozone fluctuations found in the GOME data. The results indicate that the analyzed total ozone column fluctuations are mainly caused by transport effects in the lower stratosphere. This study shows that combined use of ECMWF Re‐Analysis data and GOME ozone columns provides a possibility to study the three‐dimensional structure of Kelvin wave activity.

Evaluation of long‐term tropospheric NO2 data obtained by GOME over East Asia in 1996–2002

Long‐term tropospheric nitrogen dioxide (NO2) column data obtained by the Global Ozone Monitoring Experiment (GOME) (G‐NO2) are evaluated to confirm the trends found in tropospheric NO2 abundances over East Asia between 1996 and 2002. For three locations in Central and East Asia, the G‐NO2 values are compared with tropospheric columns estimated from coincident observations of total NO2 by ground‐based UV/visible spectrometers and stratospheric NO2 by satellite solar occultation sensors (E‐NO2). The comparisons show a slight linear drift in G‐NO2 data from 1996 to 2002. However, it is much smaller than the standard deviation of the differences between G‐NO2 and E‐NO2 and much smaller than the increasing trends in NO2 seen by GOME over the industrial areas of China, demonstrating the validity of the trends estimated using the GOME data.

GOME Observations of Stratospheric Trace Gas Distributions during the Splitting Vortex Event in the Antarctic Winter of 2002. Part I: Measurements

Abstract Measurements from the Global Ozone Monitoring Experiment (GOME) are used to study the chemical evolution of the stratosphere during the unusual 2002 winter in the Southern Hemisphere. The results show that chlorine activation as indicated by OClO columns was similar to previous years in the vortex until the major warming on 26 September 2002 after which it decreased rapidly. Similarly, NO2 columns were only slightly larger than in previous years before the warming, indicating strong denoxification and probably also denitrification. After the warming, very large NO2 columns were observed for a few days, which then decreased again as the vortex reestablished itself until the final warming. Ozone columns were much larger than in any previous year from September onward, mainly as a result of the unusual dynamical situation. Analysis of the global long-term time series of GOME measurements since 1996 provides a unique opportunity to set the austral winter 2002 into perspective. The GOME data reveal the large difference in variability of chlorine activation between the two hemispheres, whereas denoxification shows surprisingly little variation from year to year in both hemispheres. However, NO2 depletion in the Southern Hemisphere is usually sustained for about one month longer in the Antarctic stratosphere as a result of the stable vortex. Compared to the observations in the Northern Hemisphere, the austral winter 2002 was still stable and cold and had a high potential for chemical ozone destruction.

Highly resolved global distribution of tropospheric NO<sub>2</sub> using GOME narrow swath mode data

Abstract. The Global Ozone Monitoring Experiment (GOME) allows the retrieval of tropospheric vertical column densities (VCDs) of NO2 on a global scale. Regions with enhanced industrial activity can clearly be detected, but the standard spatial resolution of the GOME ground pixels (320x40km2) is insufficient to resolve regional trace gas distributions or individual cities. Every 10 days within the nominal GOME operation, measurements are executed in the so called narrow swath mode with a much better spatial resolution (80x40km2). We use this data (1997-2001) to construct a detailed picture of the mean global tropospheric NO2 distribution. Since - due to the narrow swath - the global coverage of the high resolution observations is rather poor, it has proved to be essential to deseasonalize the single narrow swath mode observations to retrieve adequate mean maps. This is done by using the GOME backscan information. The retrieved high resolution map illustrates the shortcomings of the standard size GOME pixels and reveals an unprecedented wealth of details in the global distribution of tropospheric NO2. Localised spots of enhanced NO2 VCD can be directly associated to cities, heavy industry centers and even large power plants. Thus our result helps to check emission inventories. The small spatial extent of NO2 "hot spots" allows us to estimate an upper limit of the mean lifetime of boundary layer NOx of 17h on a global scale. The long time series of GOME data allows a quantitative comparison of the narrow swath mode data to the nominal resolution. Thus we can analyse the dependency of NO2 VCDs on pixel size. This is important for comparing GOME data to results of new satellite instruments like SCIAMACHY (launched March 2002 on ENVISAT), OMI (launched July 2004 on AURA) or GOME II (to be launched 2005) with an improved spatial resolution.

Influence of stratospheric airmasses on tropospheric vertical O<sub>3</sub> columns based on GOME (Global Ozone Monitoring Experiment) measurements and backtrajectory calculation over the Pacific

Abstract. Satellite based GOME (Global Ozone Measuring experiment) data are used to characterize the amount of tropospheric ozone over the tropical Pacific. Tropospheric ozone was determined from GOME data using the Tropospheric Excess Method (TEM). In the tropical Pacific a significant seasonal variation is detected. Tropospheric excess ozone is enhanced during the biomass burning season from September to November due to outflow from the continents. In September 1999 GOME data reveal an episode of increased excess ozone columns over Tahiti (18.0° S; 149.0° W) (Eastern Pacific) compared to Am. Samoa (14.23° S; 170.56° W) and Fiji (18.13° S; 178.40° E), both situated in the Western Pacific. Backtrajectory calculations show that none of the airmasses arriving over the three locations experienced anthropogenic pollution (e. g. biomass burning). Consequently other sources of ozone have to be considered. One possible process leading to an increase of tropospheric ozone is stratosphere-troposphere-exchange. An analysis of the potential vorticity along trajectories arriving above each of the locations reveals that airmasses at Tahiti are subject to enhanced stratospheric influence, compared to Am. Samoa and Fiji. As a result this study shows clear incidents of transport of airmasses from the stratosphere into the troposphere.