ALBERT

Description: 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.

Description: We present the new GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record which has been created within the framework of the European Space Agency's Climate Change Initiative (ESA-CCI). Total ozone column observations – based on the GOME-type Direct Fitting version 3 algorithm – from GOME (Global Ozone Monitoring Experiment), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY), and GOME-2 have been combined into one homogeneous time series, thereby taking advantage of the high inter-sensor consistency. The data record spans the 15-year period from March 1996 to June 2011 and it contains global monthly mean total ozone columns on a 1° × 1° grid. Geophysical ground-based validation using Brewer, Dobson, and UV-visible instruments has shown that the GTO-ECV level 3 data record is of the same high quality as the equivalent individual level 2 data products that constitute it. Both absolute agreement and long-term stability are excellent with respect to the ground-based data, for almost all latitudes apart from a few outliers which are mostly due to sampling differences between the level 2 and level 3 data. We conclude that the GTO-ECV data record is valuable for a variety of climate applications such as the long-term monitoring of the past evolution of the ozone layer, trend analysis and the evaluation of Chemistry–Climate Model simulations.

Description: The main goal of this article 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 Center (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 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), the correlation exhibits a remarkable improvement (R2 ~ 0.95). Nevertheless, analyzing the six datasets together, the relative differences between GOME-2 and GRUAN data shows mean values (in absolute term) of 19% for all-sky conditions and 14% for cloud-free cases, which evidences a notable bias in the satellite TWVC data against the reference balloon-borne measurements. The satellite-sounding TWVC differences show a strong solar zenith angle (SZA) dependence for values above 50° with a stable behaviour for values below this zenith angle. The smallest relative differences found in the inter-comparison (between −5 and +3%) are achieved for those cloud-free cases with SZA below 50°. Furthermore, 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 +5 to −20% when CTA rises from 0.3 to 0.9. Finally, the satellite-sounding differences also show a negative dependence on the reference TWVC values, changing from +10% (TWVC below 10 mm) to −20% (TWVC above 50 mm).

Description: 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 assessment 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 MAX-DOAS 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 MAX-DOAS 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.

Description: The main aim of the paper is to assess the consistency of five years of Global Ozone Monitoring Experiment-2/Metop-A [GOME-2] total ozone columns and the long-term total ozone satellite monitoring database already in existence through an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. The behaviour of the GOME-2 measurements is being weighed against that of GOME (1995–2011), Ozone Monitoring Experiment [OMI] (since 2004) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY [SCIAMACHY] (since 2002) total ozone column products. Over the background truth of the ground-based measurements, the total ozone columns are inter-evaluated using a suite of established validation techniques; the GOME-2 time series follow the same patterns as those observed by the other satellite sensors. In particular, on average, GOME-2 data underestimate GOME data by about 0.80%, and underestimate SCIAMACHY data by 0.37% with no seasonal dependence of the differences between GOME-2, GOME and SCIAMACHY. The latter is expected since the three datasets are based on similar DOAS algorithms. This underestimation of GOME-2 is within the uncertainty of the reference data used in the comparisons. Compared to the OMI sensor, on average GOME-2 data underestimate OMI_DOAS (collection 3) data by 1.28%, without any significant seasonal dependence of the differences between them. The lack of seasonality might be expected since both the GOME data processor [GDP] 4.4 and OMI_DOAS are DOAS-type algorithms and both consider the variability of the stratospheric temperatures in their retrievals. Compared to the OMI_TOMS (collection 3) data, no bias was found. We hence conclude that the GOME-2 total ozone columns are well suitable to continue the long-term global total ozone record with the accuracy needed for climate monitoring studies.

Description: This paper describes the comparison of the variability of total column ozone inferred from the three independent multi-year data records, namely, (i) Solar Backscatter Ultraviolet Instrument (SBUV) v8.6 profile total ozone, (ii) GTO (GOME-type total ozone), and (iii) ground-based total ozone data records covering the 16-year overlap period (March 1996 through June 2011). Analyses are conducted based on area-weighted zonal means for 0–30° S, 0–30° N, 50–30° S, and 30–60° N. It has been found that, on average, the differences in monthly zonal mean total ozone vary between −0.3 and 0.8 % and are well within 1%. For GTO minus SBUV, the standard deviations and ranges (maximum minus minimum) of the differences regarding monthly zonal mean total ozone vary between 0.6–0.7% and 2.8–3.8% respectively, depending on the latitude band. The corresponding standard deviations and ranges regarding the differences in monthly zonal mean anomalies show values between 0.4–0.6% and 2.2–3.5%. The standard deviations and ranges of the differences ground-based minus SBUV regarding both monthly zonal means and anomalies are larger by a factor of 1.4–2.9 in comparison to GTO minus SBUV. The ground-based zonal means demonstrate larger scattering of monthly data compared to satellite-based records. The differences in the scattering are significantly reduced if seasonal zonal averages are analyzed. The trends of the differences GTO minus SBUV and ground-based minus SBUV are found to vary between −0.04 and 0.1% yr−1 (−0.1 and 0.3 DU yr−1). These negligibly small trends have provided strong evidence that there are no significant time-dependent differences among these multi-year total ozone data records. Analyses of the annual deviations from pre-1980 level indicate that, for the 15-year period of 1996 to 2010, all three data records show a gradual increase at 30–60° N from −5% in 1996 to −2% in 2010. In contrast, at 50–30° S and 30° S–30° N there has been a levelling off in the 15 years after 1996. The deviations inferred from GTO and SBUV show agreement within 1%, but a slight increase has been found in the differences during the period 1996–2010.

Description: 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.

Description: 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.

Description: 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.

Description: The three Global Ozone Monitoring Experiment-2 instruments will provide unique and long data sets for atmospheric research and applications. The complete time period will be 2007–2022, including the period of ozone depletion as well as the beginning of ozone layer recovery. Besides ozone chemistry, the GOME-2 (Global Ozone Monitoring Experiment-2) products are important e.g. for air quality studies, climate modelling, policy monitoring and hazard warnings. The heritage for GOME-2 is in the ERS/GOME and Envisat/SCIAMACHY instruments. The current Level 2 (L2) data cover a wide range of products such as ozone and minor trace gas columns (NO2, BrO, HCHO, H2O, SO2), vertical ozone profiles in high and low spatial resolution, absorbing aerosol indices, surface Lambertian-equivalent reflectivity database, clear-sky and cloud-corrected UV indices and surface UV fields with different weightings and photolysis rates. The Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) processes and disseminates data 24/7. Data quality is guaranteed by the detailed review processes for the algorithms, validation of the products as well as by a continuous quality monitoring of the products and processing. This paper provides an overview of the O3M SAF project background, current status and future plans for the utilisation of the GOME-2 data. An important focus is the provision of summaries of the GOME-2 products including product principles and validation examples together with sample images. Furthermore, this paper collects references to the detailed product algorithm and validation papers.