Global Ozone Monitoring Experiment Instrument Research Articles

Increasing in Tropospheric Nitrogen Dioxide over the North-South Transect of Northeast Asia during 1996-2010

Measurements of the global distribution of the tropospheric NO2 column densities have become available with the GOME (Global Ozone Monitoring Experiment) and SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric Chartography) instruments. The spatial and temporal characters of NO2 over the North-South Transect of Northeast Asia (NSTNA) was analyzed using monthly averaged tropospheric NO2 column densities from GOME and SCIAMACHY measurements taken from April 1996 to December 2010. In addition to NO2 (1996-2010) column densities data, land-cover data and characters of NO2 concentration over different ecosystem have been analyzed over the NSTNA. The results indicated that the tropospheric NO2 column densities over the region show distinct regional and seasonal variation characteristics for between 1996 and 2009. The highest concentration was in winter and lowest in autumn. The data also presents zonal distribution and decreasing from northern to southern. Under the influence of the urbanization process, the highest NO2 column densities appeared at cities and metropolitan areas which are located in the north plain of China and the southern areas of the NSTNA. Moreover the lowest column densities appeared at the northern areas of the NSTNA. The tropospheric NO2 column densities have shown significant differences over other ecosystems. In addition, the higher NO2 column density was found over forest and grass land while lower values over lake, tundra and semi-desert. There are different characteristics in five land use types along with the NO2 column densities increasing year by year. The highest values appeared in winter over the forest, grass, lake and tundra while in summer over the semi-desert.

Total ozone column derived from GOME and SCIAMACHY using KNMI retrieval algorithms: Validation against Brewer measurements at the Iberian Peninsula

[1] This article focuses on the validation of the total ozone column (TOC) data set acquired by the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite remote sensing instruments using the Total Ozone Retrieval Scheme for the GOME Instrument Based on the Ozone Monitoring Instrument (TOGOMI) and Total Ozone Retrieval Scheme for the SCIAMACHY Instrument Based on the Ozone Monitoring Instrument (TOSOMI) retrieval algorithms developed by the Royal Netherlands Meteorological Institute. In this analysis, spatially colocated, daily averaged ground-based observations performed by five well-calibrated Brewer spectrophotometers at the Iberian Peninsula are used. The period of study runs from January 2004 to December 2009. The agreement between satellite and ground-based TOC data is excellent (R2 higher than 0.94). Nevertheless, the TOC data derived from both satellite instruments underestimate the ground-based data. On average, this underestimation is 1.1% for GOME and 1.3% for SCIAMACHY. The SCIAMACHY-Brewer TOC differences show a significant solar zenith angle (SZA) dependence which causes a systematic seasonal dependence. By contrast, GOME-Brewer TOC differences show no significant SZA dependence and hence no seasonality although processed with exactly the same algorithm. The satellite-Brewer TOC differences for the two satellite instruments show a clear and similar dependence on the viewing zenith angle under cloudy conditions. In addition, both the GOME-Brewer and SCIAMACHY-Brewer TOC differences reveal a very similar behavior with respect to the satellite cloud properties, being cloud fraction and cloud top pressure, which originate from the same cloud algorithm (Fast Retrieval Scheme for Clouds from the Oxygen A-Band (FRESCO+)) in both the TOSOMI and TOGOMI retrieval algorithms.

Long-term variation of bromine monoxide in India from GOME and SCIAMACHY instruments

The production of bromine monoxide (BrO) from seawater and its influence in catalytically depleting stratospheric and boundary layer ozone at high latitudes have been well established. In India the study of BrO has so far been limited to relatively short-term measurements, even though the Indian mainland has a coastline of length 6100 km and is surrounded by the Arabian Sea, the Indian Ocean and the Bay of Bengal, all probable major sources of BrO. In this study the variation in BrO vertical column density (VCD) was examined using data derived from Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments for the period from 1996 to 2008. BrO columns in India are found to be anticorrelated with total ozone column, increase with an increase in latitude, display a small interannual and seasonal variation, and an increasing trend over the Arabian Sea, Ahmedabad and Chennai, a mixed trend over the Bay of Bengal, the Indian Ocean, Jharia and Kolkata and a decreasing trend at New Delhi and Kanpur.

Influence of turbidity and clouds on satellite total ozone data over Madrid (Spain)

Abstract. This article focuses on the comparison of the total ozone column data from three satellite instruments; Total Ozone Mapping Spectrometers (TOMS) on board the Earth Probe (EP), Ozone Monitoring Instrument (OMI) on board AURA and Global Ozone Monitoring Experiment (GOME) on board ERS/2, with ground-based measurement recorded by a well calibrated Brewer spectrophotometer located in Madrid during the period 1996–2008. A cluster classification based on solar radiation (global, direct and diffuse), cloudiness and aerosol index allow selecting hazy, cloudy, very cloudy and clear days. Thus, the differences between Brewer and satellite total ozone data for each cluster have been analyzed. The accuracy of EP-TOMS total ozone data is affected by moderate cloudiness, showing a mean absolute bias error (MABE) of 2.0%. In addition, the turbidity also has a significant influence on EP-TOMS total ozone data with a MABE ~1.6%. Those data are in contrast with clear days with MABE ~1.2%. The total ozone data derived from the OMI instrument show clear bias at clear and hazy days with small uncertainties (~0.8%). Finally, the total ozone observations obtained with the GOME instrument show a very smooth dependence with respect to clouds and turbidity, showing a robust retrieval algorithm over these conditions.

Satellite measurements of formaldehyde linked to shipping emissions

Abstract. International shipping is recognized as a pollution source of growing importance, in particular in the remote marine boundary layer. Nitrogen dioxide originating from ship emissions has previously been detected in satellite measurements. This study presents the first satellite measurements of formaldehyde (HCHO) linked to shipping emissions as derived from observations made by the Global Ozone Monitoring Experiment (GOME) instrument. We analyzed enhanced HCHO tropospheric columns from shipping emissions over the Indian Ocean between Sri Lanka and Sumatra. This region offers good conditions in term of plume detection with the GOME instrument as all ship tracks follow a single narrow track in the same east-west direction as used for the GOME pixel scanning. The HCHO signal alone is weak but could be clearly seen in the high-pass filtered data. The line of enhanced HCHO in the Indian Ocean as seen in the 7-year composite of cloud free GOME observations clearly coincides with the distinct ship track corridor from Sri Lanka to Indonesia. The observed mean HCHO column enhancement over this shipping route is about 2.0×1015 molec/cm2. Compared to the simultaneously observed NO2 values over the shipping route, those of HCHO are substantially higher; also the HCHO peaks are found at larger distance from the ship routes. These findings indicate that direct emissions of HCHO or degradation of emitted NMHC cannot explain the observed enhanced HCHO values. One possible reason might be increased CH4 degradation due to enhanced OH concentrations related to the ship emissions, but this source is probably too weak to fully explain the observed values. The observed HCHO pattern also agrees qualitatively well with results from the coupled earth system model ECHAM5/MESSy applied to atmospheric chemistry (EMAC). However, the modelled HCHO values over the ship corridor are two times lower than in the GOME high-pass filtered data. This might indicate uncertainties in the satellite data and used emission inventories and/or that the in-plume chemistry taking place in the narrow path of the shipping lanes are not well represented at the rather coarse model resolution.

Comparison of GOME-2/MetOp total ozone data with Brewer spectroradiometer data over the Iberian Peninsula

Abstract. The main objective of this article is to compare the total ozone data from the new Global Ozone Monitoring Experiment instrument (GOME-2/MetOp) with reliable ground-based measurement recorded by five Brewer spectroradiometers in the Iberian Peninsula. In addition, a similar comparison for the predecessor instrument GOME/ERS-2 is described. The period of study is a whole year from May 2007 to April 2008. The results show that GOME-2/MetOp ozone data already has a very good quality, total ozone columns are on average 3.05% lower than Brewer measurements. This underestimation is higher than that obtained for GOME/ERS-2 (1.46%). However, the relative differences between GOME-2/MetOp and Brewer measurements show significantly lower variability than the differences between GOME/ERS-2 and Brewer data. Dependencies of these relative differences with respect to the satellite solar zenith angle (SZA), the satellite scan angle, the satellite cloud cover fraction (CF), and the ground-based total ozone measurements are analyzed. For both GOME instruments, differences show no significant dependence on SZA. However, GOME-2/MetOp data show a significant dependence on the satellite scan angle (+1.5%). In addition, GOME/ERS-2 differences present a clear dependence with respect to the CF and ground-based total ozone; such differences are minimized for GOME-2/MetOp. The comparison between the daily total ozone values provided by both GOME instruments shows that GOME-2/MetOp ozone data are on average 1.46% lower than GOME/ERS-2 data without any seasonal dependence. Finally, deviations of a priori climatological ozone profile used by the satellite retrieval algorithm from the true ozone profile are analyzed. Although excellent agreement between a priori climatological and measured partial ozone values is found for the middle and high stratosphere, relative differences greater than 15% are common for the troposphere and lower stratosphere.

Seasonal variations of magnesium atoms in the mesosphere‐thermosphere

UV radiances from the Global Ozone Monitoring Experiment (GOME) spectrometer on the ERS‐2 satellite are used to determine long‐term dayside temporal variations of the total vertical column density below 795 km of the meteoric metal species Mg and Mg+ in the upper atmosphere. The GOME instrument has the ability to observe the ground state transition lines of Mg I at 285.2 nm and resolve the Mg II at 280 nm. A retrieval algorithm has been developed to determine column densities and applied to the years 1996–1997. Results show the middle latitude dayside Mg+ peaks in total vertical content during the summer, while neutral Mg demonstrates a much more subtle maximum in summer. Dayside trends are opposite previous midlatitude nighttime lidar observations, with the exception of calcium. The Mg+/Mg ratio has a seasonal variation, reaching a maximum in the summer. The total content of Mg+ is twice that of neutral Mg.

Evaluation of the GOME Water Vapor Climatology 1995–2002

An accurate knowledge of the 3‐D water vapor (WV) field is still limited, because of the limited capabilities of sensors in the past to cover the whole Earth’s surface and the lower part of the troposphere, as well as to measure over reasonably long time series. We show here water vapor total column retrieved from seven years of Global Ozone Monitoring Experiment (GOME) measurements collected from August 1995 until August 2002. Our aim is two‐fold: (1) to evaluate the accuracy and the limitations of the GOME water vapor total column and (2) to demonstrate its potential for climate studies. The column retrieval makes use of two innovative techniques operating in tandem, namely the University of Graz empirical air mass factor ratioing technique (IGAM) and the Spectral Structure Parameterization (SSP) retrieval method. The GOME instrument and its follow‐up instruments (SCIAMACHY and GOME‐2), using these algorithms, have the capability to cover nearly the whole globe in cloud‐free situations and collect robust WV total column information over more than 3 decades. In this work we evaluate the results for the first 7 years against independent in situ measurements from the operational WMO radiosonde network, against high spatial resolution water vapor columns from MERIS (the Medium Resolution Imaging Spectrometer on EnviSat) and also compare with ERA40 model results. The GOME water vapor total column exhibits a bias of less than 2.5% with an uncertainty of around 5 mm for collocated measurements against radiosonde and MERIS measurements. Spatial patterns and trends in the global distribution of WV total column fields from GOME against re‐analysis model results are well correlated with temperature in the tropics, and exhibit a lesser degree of correlation in the extra tropics. Cloud‐free total columns from GOME can be systematically lower by up to 5 mm in the sub‐tropics with respect to the all‐sky case. In contrast, the impact of the diurnal cycle on the monthly mean values is found to be very small.

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.

Assimilation of GOME ozone profiles and a global chemistry–transport model using a Kalman filter with anisotropic covariance

AbstractNadir ozone profiles retrieved from the Global Ozone Monitoring Experiment (GOME) instrument are assimilated with a global three‐dimensional (3D) atmospheric ozone model. The assimilation procedure is based on the Kalman filter equations, and is an extension of an existing assimilation procedure for total ozone columns. As a novelty, a 3D covariance model is developed using a single parametrization for correlations in all directions, instead of the usually applied separation in horizontal and vertical directions. The parametrization is anisotropic in all directions, accounting for the different correlation lengths of ozone with respect to altitude, latitude, and longitude. The assimilation procedure includes full use of the averaging kernel information provided with the GOME retrieval product. The averaging kernels account for the smaller sensitivity of the GOME instrument below the ozone maximum and the limited vertical resolution. A singular‐value decomposition of the kernels is used to reduce the large data volume. A one‐year dataset of GOME ozone profiles is assimilated for the year 2000. Independent data from ozonesondes are used to validate the results. A case‐study shows that the assimilation of GOME profiles is able to improve the simulation of the vertical ozone distribution even in the case of strong vertical gradients. Copyright © 2005 Royal Meteorological Society.

Satellite observations of atmospheric SO 2 from volcanic eruptions during the time-period of 1996–2002

In this article, we present satellite observations of atmospheric sulfur dioxide (SO 2) from volcanic eruptions. Global ozone monitoring experiment (GOME) data for the years 1996–2002 is analyzed using a DOAS based algorithm with the aim of retrieving SO 2 slant column densities (SCD). The retrieval of SO 2 SCD in the UV spectral region is difficult due to strong and interfering ozone absorptions. It is also likely affected by instrumental effects. We investigated these effects in detail to obviate systematic biases in the SO 2 retrieval. A quantitative study of about 20 volcanoes from Italy, Iceland, Congo/Zaire, Ecuador, Japan, Vanuatu Island and Mexico is presented. The focus is on both eruption and out gassing scenarios. We prepared a 7-year mean map (1996–2002) of SO 2 SCD observed by GOME and tabulated the ratios of the maximum SO 2 SCD observed to the average SO 2 SCD as seen in the 7-year mean map. The further aim of this study is to provide information about unknown volcanic eruptions, e.g., Bandai Honshu Japan, Central Islands Vanuatu, Piton de la Fournaise Réunion Island France, Kamchatka region of Russia and from Indonesia especially. The results demonstrate a high sensitivity of the GOME instrument towards SO 2 emissions during both eruption and degassing episodes.

Retrieval methods of effective cloud cover from the GOME instrument: an intercomparison

Abstract. The radiative scattering by clouds leads to errors in the retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites. Moreover, the presence of clouds changes the UV actinic flux and the photo-dissociation rates of various species significantly. The Global Ozone Monitoring Experiment (GOME) instrument on the ERS-2 satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. Four cloud fraction retrieval methods for GOME data that have been developed are discussed in this paper (the Initial Cloud Fitting Algorithm, the PMD Cloud Recognition Algorithm, the Optical Cloud Recognition Algorithm (an in-house version and the official implementation) and the Fast Retrieval Scheme for Clouds from the Oxygen A-band). Their results of cloud fraction retrieval are compared to each-other and also to synoptic surface observations. It is shown that all studied retrieval methods calculate an effective cloud fraction that is related to a cloud with a high optical thickness. Generally, we found ICFA to produce the lowest cloud fractions, followed by our in-house OCRA implementation, FRESCO, PC2K and finally the official OCRA implementation along four processed tracks (+2%, +10%, +15% and +25% compared to ICFA respectively). Synoptical surface observations gave the highest absolute cloud fraction when compared with individual PMD sub-pixels of roughly the same size.

Trace gas column retrieval – an error assessment study for GOME-2

As a follow-on of the successful GOME instrument aboard ERS-2 the GOME-2 series is planned to operate on METOP satellites after 2005. Focussing on meteorological applications GOME-2 is designed to measure the total column and profiles of atmospheric ozone and the distribution of other key atmospheric constituents like BrO and NO 2. The main differences to Global Ozone Monitoring Experiment (GOME) are the higher spatial resolution of 40×80 km 2 in combination with a larger swath width of 1920 km. In this study potential error sources have been analysed most relevant for high viewing angles or small ground pixel sizes as applicable to GOME-2. Instrumental and atmospheric error sources have been identified with respect to basic signal-to-noise errors based upon an instrument model and full-spherical atmospheric radiative transfer simulations. Potential error sources such as the reflectance of the atmosphere, refraction, bi-directional surface reflection, spatial aliasing effects due to inhomogeneities of the scene, and spectral resolution (undersampling, slit width) have been investigated. Effects due to changes in instrumental design, e.g. read-out time, slit width and spectral sampling, have been taken into account. Error sources identified as most significant for trace gas column retrieval are spectral signature in the diffuser plate reflectivity, sun-glint effects, undersampling residuals, and noise. An overview of the study results and conclusions for future GOME-2 retrieval and instrumental designs are presented.

Transport and build-up of tropospheric trace gases during the MINOS campaign: comparision of GOME, in situ aircraft measurements and MATCH-MPIC-data

Abstract. The MINOS (Mediterranean INtensive Oxidant Study) campaign was an international, multi-platform field campaign to measure long-range transport of air-pollution and aerosols from South East Asia and Europe towards the Mediterranean basin during August 2001. High pollution events were observed during this campaign. For the Mediterranean region enhanced tropospheric nitrogen dioxide (NO2) and formaldehyde (HCHO), which are precursors of tropospheric ozone (O3), were detected by the satellite based GOME (Global Ozone Monitoring Experiment) instrument and compared with airborne in situ measurements as well as with the output from the global 3D photochemistry-transport model MATCH-MPIC (Model of Atmospheric Transport and CHemistry - Max Planck Institute for Chemistry). The increase of pollution in that region leads to severe air quality degradation with regional and global implications.

Near‐real‐time approach to assimilation of satellite‐retrieved 3D ozone fields in a global model using a simplified Kalman filter

AbstractIn this paper, a near‐real‐time approach to data assimilation for the estimation of the three‐dimensional global ozone distribution based on the transport model TM3 is presented. TM3 uses the meteorological data from European Centre for Medium‐Range Weather Forecasts to transport the ozone field. The ozone profiles used in the assimilation are retrieved from measurements of the Global Ozone Monitoring Experiment instrument (GOME) on the European Space Agency's European Remote Sensing‐2 satellite. A simplified Kalman filter technique is chosen as a data assimilation technique since it not only estimates the ozone distribution globally but also quantifies the uncertainty in its estimate. The NRT data assimilation technique is demonstrated for the period of March–June 2000. The results presented in this paper show that the estimated profiles deviate from the measurements by, on a global average, 15% of the ozone values in pressure layer 163–119 hPa and ranging from 6% to 2% of the ozone values between 119 hPa and 40 hPa. The deviation of the estimate from the measurements decreases with altitude and reaches 0.05% of the ozone value in the layer 2–0 hPa. Comparison with ozone‐sonde observations shows that the model driven by observed meteorological fields is capable of describing the main spatial and temporal variations. Comparison with data from the Halogen Occultation Experiment instrument on the Upper Atmosphere Research Satellite shows that assimilating the retrieved profiles from GOME improves the vertical description of ozone profiles mainly in the stratosphere and the upper troposphere. Moreover, the integrated ozone‐profile estimate is compared with the total‐column ozone measurements made by the Total Ozone Mapping Spectrometer. This shows that assimilating the ozone profiles results in a better description of the total column than the ten‐day forecast. Copyright © 2003 Royal Meteorological Society

Near-real-time approach to assimilation of satellite-retrieved 3D ozone fields in a global model using a simplified Kalman filter

In this paper, a near-real-time approach to data assimilation for the estimation of the three-dimensional global ozone distribution based on the transport model TM3 is presented. TM3 uses the meteorological data from European Centre for Medium-Range Weather Forecasts to transport the ozone field. The ozone profiles used in the assimilation are retrieved from measurements of the Global Ozone Monitoring Experiment instrument (GOME) on the European Space Agency's European Remote Sensing-2 satellite. A simplified Kalman filter technique is chosen as a data assimilation technique since it not only estimates the ozone distribution globally but also quantifies the uncertainty in its estimate. The NRT data assimilation technique is demonstrated for the period of March–June 2000. The results presented in this paper show that the estimated profiles deviate from the measurements by, on a global average, 15% of the ozone values in pressure layer 163–119 hPa and ranging from 6% to 2% of the ozone values between 119 hPa and 40 hPa. The deviation of the estimate from the measurements decreases with altitude and reaches 0.05% of the ozone value in the layer 2–0 hPa. Comparison with ozone-sonde observations shows that the model driven by observed meteorological fields is capable of describing the main spatial and temporal variations. Comparison with data from the Halogen Occultation Experiment instrument on the Upper Atmosphere Research Satellite shows that assimilating the retrieved profiles from GOME improves the vertical description of ozone profiles mainly in the stratosphere and the upper troposphere. Moreover, the integrated ozone-profile estimate is compared with the total-column ozone measurements made by the Total Ozone Mapping Spectrometer. This shows that assimilating the ozone profiles results in a better description of the total column than the ten-day forecast. Copyright © 2003 Royal Meteorological Society

Assimilation of GOME total‐ozone satellite observations in a three‐dimensional tracer‐transport model

AbstractA data‐assimilation scheme to assimilate the Global Ozone Monitoring Experiment (GOME) total‐ozone data is described. The corresponding software (called TM3DAM) has been operational since early 2000 and is used to produce daily ozone analyses and five‐day ozone forecasts. The model is a tracer‐transport model with a parametrized description of stratospheric gas‐phase and heterogeneous ozone chemistry. It is driven by operational meteorological fields from the ECMWF numerical weather‐prediction model. TM3DAM analyses near‐real‐time level‐2 ozone data from the GOME instrument on the ESA ERS‐2 satellite. The focus of this paper is on the data‐assimilation aspects and the analysis results. The assimilation approach is based on the Kalman‐filter equations and provides detailed and realistic maps of the forecast error. The analysis scheme is nevertheless computationally efficient. The forecast‐minus‐observation statistics, accumulated over a two‐year period, are described in detail. A comparison with TOMS and Brewer observations shows good agreement. Copyright © 2003 Royal Meteorological Society

A database of spectral surface reflectivity in the range 335–772 nm derived from 5.5 years of GOME observations

A global database of Lambert‐equivalent reflectivity (LER) of the Earth's surface has been constructed by analyzing observations of the reflectivity at the top of the atmosphere made by the Global Ozone Monitoring Experiment (GOME). Since its launch on board the ERS‐2 satellite in April 1995, the GOME instrument has been measuring spectra of the Earth between 237 and 794 nm, with a spectral resolution between 0.2 and 0.4 nm and a spatial resolution between 40 × 80 and 40 × 320 km2. The LER database covers eleven 1‐nm‐wide wavelength bins centered at 335, 380, 416, 440, 463, 494.5, 555, 610, 670, 758, and 772 nm, which were selected for various retrieval applications. The database has a spatial resolution of 1° × 1°, is made for each month of the year, and pertains to the period June 1995–December 2000. Typical spectra of various surface types are presented. Attention is paid to instrument degradation and residual cloud contamination. We have found satisfactory agreement between our database at 380 nm and the Total Ozone Mapping Spectrometer (TOMS) LER database at 340–380 nm, with negligible average difference and a standard deviation of 0.013. The database presented here can be used to improve retrievals of trace gases, clouds and aerosols from GOME, Scanning Imaging Absorption Spectrometer or Atmospheric Cartography (SCIAMACHY), Ozone Monitoring Instrument (OMI), and GOME‐2.

Global stratospheric ozone profiles from GOME in near-real time

The Global Ozone Monitoring Experiment (GOME) on the European Space Agency's (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.

An improved retrieval of tropospheric nitrogen dioxide from GOME

We present a retrieval of tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument that improves in several ways over previous retrievals, especially in the accounting of Rayleigh and cloud scattering. Slant columns, which are directly fitted without low‐pass filtering or spectral smoothing, are corrected for an artificial offset likely induced by spectral structure on the diffuser plate of the GOME instrument. The stratospheric column is determined from NO2 columns over the remote Pacific Ocean to minimize contamination from tropospheric NO2. The air mass factor (AMF) used to convert slant columns to vertical columns is calculated from the integral of the relative vertical NO2 distribution from a global 3‐D model of tropospheric chemistry driven by assimilated meteorological data (Global Earth Observing System (GEOS)‐CHEM), weighted by altitude‐dependent scattering weights computed with a radiative transfer model (Linearized Discrete Ordinate Radiative Transfer), using local surface albedos determined from GOME observations at NO2 wavelengths. The AMF calculation accounts for cloud scattering using cloud fraction, cloud top pressure, and cloud optical thickness from a cloud retrieval algorithm (GOME Cloud Retrieval Algorithm). Over continental regions with high surface emissions, clouds decrease the AMF by 20–30% relative to clear sky. GOME is almost twice as sensitive to tropospheric NO2 columns over ocean than over land. Comparison of the retrieved tropospheric NO2 columns for July 1996 with GEOS‐CHEM values tests both the retrieval and the nitrogen oxide radical (NOx) emissions inventories used in GEOS‐CHEM. Retrieved tropospheric NO2 columns over the United States, where NOx emissions are particularly well known, are within 18% of GEOS‐CHEM columns and are strongly spatially correlated (r = 0.78, n = 288, p < 0.005). Retrieved columns show more NO2 than GEOS‐CHEM columns over the Transvaal region of South Africa and industrial regions of the northeast United States and Europe. They are lower over Houston, India, eastern Asia, and the biomass burning region of central Africa, possibly because of biases from absorbing aerosols.