dataesr/scientific-paragraphs-categorization huggingface dataset:
Although such measurements provide data over a limited geographical extent, they are based on different techniques resulting in validation data which are different regarding their limitations and biases. For this reason, lidar time series and balloon-borne campaigns (optical particle counting) are useful data sources for the validation of stratospheric data records. In this paper, we present new gridded stratospheric aerosol extinction datasets developed for chemistry-climate modelling applications using the GOMOS experiment. Therefore, we use the vertical extinction profiles retrieved using AerGOM, a recent alternative retrieval algorithm optimized for aerosol retrieval. We describe the development of this CDR, its validation, and its contribution to the assessment of the volcanic contribution to the aerosol burden during the post-2000 time period.
More specifically, we present the update of an inventory of volcanic eruptions published previously  (Brühl et al. , 2015) , and its use to determine more accurately the radiative forcing by stratospheric aerosols during the period 2002-2012, by numerical simulations using the EMAC chemistry climate model. Here, GOMOS is also used for validation and further development of the aerosol model. The paper is organized as follows: After presenting shortly the GOMOS experiments and the AerGOM retrieval algorithm in Chapter 2, we describe in Chapter 3 the processing of the CCI-GOMOS time series (Level 3) from the AerGOM dataset (Level 2). We show how we improved the extinction retrieval by AerGOM and how an appropriate selection of the star and occultation parameters increased the quality of the CCI-GOMOS time series.
The GOMOS instrument The GOMOS instrument was launched on March 1st, 2002 onboard ENVISAT in a sunsynchronous orbit, and provided about 10 years of data until the contact with ENVISAT was lost on April 8th, 2012. GOMOS is based on the stellar occultation principle. The abundance of stars (about 300 stars selected for use by GOMOS) results in a dramatic increase of the measurement rate with respect to solar occultation measurements. However, scintillation effects affect the weak light beam from the star during its propagation through the Earth's atmosphere, resulting in a limited signal-to noise ratio, which depends on the star properties (magnitude and temperature) and on the orbital parameters, such as its obliquity and the solar zenith angle. GOMOS is equipped with two spectrometers: spectrometer SPA, which measures in the UV-visible, covering the range 248-690 nm with a sampling resolution of 0.
From preliminary comparisons with the Stratospheric Aerosol and Gas Experiment (SAGE) II and III extinction datasets,  Robert et al. (2016)  found that a linear dependence (m = 1) might be a better choice than the quadratic one used here. However, the improvements brought to AerGOM (See more particularly Section 2. 3) improve the agreement between AerGOM and those datasets, and since the more versatile choice of a quadratic law might be more adequate for the use of extinction spectra in the retrieval of particle size distributions, we chose, at this stage, to keep this choice of parameterization. A detailed description of the AerGOM algorithm, version 1. 0 and of the main differences and improvements with respect to IPF is given in  (Vanhellemont et al. , 2016). In the present work, we make use of version 2.
Choice of the gas absorption cross-sections Another problem encountered was the frequent occurrence of negative values of the aerosol extinction coefficient, especially above about 30 km altitude. This issue was particularly pronounced at the longest wavelengths (about 700-750 nm). Although stray light contamination was suspected at first to be the main reason for these unphysical values, it appeared, after analysis of the cross-section database, that some gas cross-section spectra used for the retrieval of the gas species by AerGOM were not optimal in terms of available spectral range and temperature dependence. Hence, the problem could finally be controlled by a revision of the molecular absorption cross-section spectra used for the retrieval. To this end, the most recent absorption cross-section measurements for the gases of interest were investigated to improve the spectral range, the temperature dependence and the consistency of the absorption cross-section spectra.
High-resolution spectra were selected whenever it was possible. The most rigorous approach requires using the high-resolution spectra to calculate the forward spectral model before performing a convolution of the total input signal with the instrument point spread function. However, this approach is very demanding in terms of computer resources, and a common simplification consists of applying the convolution by the point spread function directly on the gas cross-section spectrum. Tests were made to verify that this simplified approach is sufficient, and indeed, they showed overall good results at least up to about 30 km. Hence, it was decided to opt for this faster approach and to use the gas cross-sections spectra at the resolution of the GOMOS spectrometers. The selection of gas absorption cross-section spectra and the set-up of the revised molecular absorption cross-section database are discussed in Appendix A.
Fig. 1  shows three examples of vertical extinction profiles where the improvement of the aerosol retrieval after revision of the gas cross-sections is visible. The date, geolocation and main features of these three occultation measurements are given in Table  2. These examples illustrate how the revision of the gas cross-section database using more recent data with better temperature coverage, a more extended spectral range, a higher spectral resolution and, in the case of ozone, a maximal consistency, leads to a significant decrease of the number of negative values in the vertical aerosol extinction profile, even in cases where the star properties are less favourable for the aerosol retrieval (more particularly, cool and/or dim stars).
Development of gridded aerosol data records for Aerosol_cci GOMOS and the AerGOM algorithm have been selected to provide stratospheric aerosol datasets in the Aerosol_cci project. The high measurement rate (about a million occultations, of which N455. 000 dark limb measurements were successfully inverted over the whole mission) ensures global coverage with a good sampling, and the availability of the extinction spectral dependence enables the retrieval of size information and related parameters. Data retrieved with the GOMOS-AerGOM algorithm are used to produce gridded time series (1) to provide datasets to constrain or validate climate models; (2) to characterize the aerosol radiative forcing in the stratosphere; (3) to provide intercomparison datasets for validation of other remote sensing experiments.
Before computing the Level 3 gridded dataset from the Level 2 AerGOM data, an observation selection ensures a sufficient quality of the data, based on criteria applied to the star parameters and solar zenith angle of the observations. Since, in the case of GOMOS, there are large differences in data quality related to these parameters, care must be taken to choose the best trade-off between data quality and coverage, two major requirements for the use of the CDRs in climate modelling applications. The choice of selection criteria is discussed later in this section. It should be noted that observations made when the spacecraft was within the Southern Atlantic Anomaly (SAA, anomaly of the Earth's magnetic field) are removed to avoid contamination by noisy measurements. The CCI-GOMOS CDRs are designed as gridded fields resolving longitude, latitude, altitude and time (Level 3).
Therefore, in later versions, the grid cell was revised to an optimized choice of 5°latitude by 60°longitude by 5 days. This choice is more suitable to describe the zonal aerosol distribution. With respect Fig. 1. Examples of vertical aerosol extinction profiles retrieved using the GOMOS cross-section database (dotted lines) and using the revised gas cross-section spectra at 350 nm (blue, dashed-dotted), 550 nm (green, solid) and 750 nm (red, dashed); the date, geolocation and characteristics of the observations shown are specified in Table  2. It has to be noted that the first example is plotted using linear scales; in examples (b) and (c), a logarithmic scale is used for the extinction, and missing points in the square-dashed lines correspond to negative extinction values. to the latitudinal and temporal resolution, it also complies better with the most critical CGOS requirement for the stratospheric aerosol extinction coefficients (given in Table  1 ) of about 200-500 km horizontal (1 degree ~110 km) and weekly temporal resolution, while the weaker longitudinal resolution is not as critical given the typical zonal transport.
Typically, up to 20 observations are available per grid cell, depending on the location of the satellite and on the stars available. The number of observations is provided as additional field in the CCI-GOMOS dataset. Binning is performed on each grid cell using only quality-filtered data, and the variability is estimated for each bin from the spread of the measurement samples. The criteria used for the filtering, which are discussed in Section 4, concern the star selection and the solar zenith angle (SZA), the main factors influencing the signal-to-noise ratio (SNR). In order to maximize the number of data used to compute the binned values while not being too sensitive to outliers, we use a weighted average of all values comprised between percentiles 10 and 90. However, if b10 points are available in the bin sample, all the values are used for the averaging.
So far, the uncertainty on each bin is computed as the average of all uncertainties between percentiles 25 and 75 (interquartile mean, IQM). This representation of the typical measurement uncertainty seems more realistic than the error of the mean, because it excludes outliers as can be found more particularly from occultations with a poor SNR (See Section 3. 1). Obviously, the primary factors influencing the quality of the CCI-GOMOS CDRs are the performance of the AerGOM retrieval algorithm and the amount of data available for binning. The very first task was hence to assess the performances of the retrieval algorithm and the outcome of this study can be found in  Robert et al. (2016). Several improvements of the AerGOM implementation led to a significant increase of the data quality, which had two positive consequences: (1) an increase of the number of observations improving the data coverage, and (2) the possibility to relax the observation selection criteria.
Star properties The early evaluation of the AerGOM algorithm showed the strong influence of star properties on the data quality. Both the temperature of the star (controlling the spectral range of its emission) and its magnitude (quantifying the overall brightness of the star) have an impact on the signal-to-noise ratio, and therefore, on the extinction uncertainty. In order to assess the exact impact of the star's properties on the quality of the retrieval and on the subsequent observation selection, the extinction uncertainty was studied for several classes of stars. In this paper, we restrict ourselves to the limiting cases of stars (brightest/dimmest; coolest/hottest), which are the most important for the choice of selection criteria. An exhaustive discussion of this study can be found in  Robert et al.
Solar zenith angle Another parameter having a strong influence on the data quality is the solar zenith angle (SZA). This angle is provided with a flag indicating the type of illumination conditions  (Bertaux et al. , 2010). While dark limb conditions with the satellite far enough from the solar terminator ensure good measurement conditions, both internal and external stray light may affect the quality of the measurement when the satellite is close to the terminator. The effect of stray light is corrected during the processing of the GOMOS Level 1 dataset, but it is likely that residual signatures of stray light remain, increasing the uncertainty of the retrieval. The range of SZA values, which depends on the position of the satellite along its orbit, is not equally distributed over the latitude range.
Aerosol_cci stratospheric data records Taking advantage of the successive improvements, of which the main ones were discussed in the previous section, different versions of the CCI-GOMOS CDR were developed, first for the reference year 2008, and later for the whole GOMOS dataset. Table  5  reports the features of the main versions produced for the CCI-GOMOS CDR spanning the whole ENVISAT period. This overview shows the gradual relaxation of the selection criteria reflecting the improvements discussed in the previous section. The transition from version 2. 14 to 2. 19 corresponds to the User's recommendation to adapt the grid and time resolutions to provide a more useful picture of the evolution of volcanic plumes. Version 3. 00 benefits mainly from the consolidation of the code, and provides several additional aerosol fields such as extinction coefficient at five wavelengths commonly used in other space-borne or groundbased instruments, but also size information and related parameters which are not within the scope of this paper.
As an illustration, the CCI-GOMOS time series of aerosol extinction coefficient at 550 nm and 17 km for the whole ENVISAT period  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)   7 ), the latter being converted to stratospheric aerosols and detected by instruments such as GOMOS. These signatures are hardly visible in the version 2. 19 data time series despite the strength of the emissions, due to a lack of data points. In version 3. 00, the amount of observations taken into account in the binning is much larger due to the relaxation of the selection criteria, and the increased data coverage is now sufficient to resolve these additional signatures. The use of this improved time series will be discussed in more detail in Section 6, and illustrated in Appendix C.
Validation The validation of the AerGOM (version 3. 0) and CCI-GOMOS extinction datasets has been the subject of various activities, in and outside the Aerosol_cci project. Robert et al. (2016)  performed extensive intercomparisons of the AerGOM dataset with several other satellite measurement datasets such as SAGE (Stratospheric Aerosol and Gas Experiment) 2 and 3, POAM (Polar Ozone and Aerosol Measurement), ACE-MAESTRO (Atmospheric Chemistry Experiment/Measurements of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation), OSI-RIS (Optical, Spectroscopic, and Infrared Remote Imaging System), and GOMOS-IPF. Recently, the CCI-GOMOS dataset also participated to the assessment of trace gases and aerosols in the framework of the SPARC Data Initiative (SPARC, 2017). Intercomparisons included the same datasets, as well as SCIAMACHY.
A paper on the SPARC Data Initiative assessment is currently in preparation (Hegglin et al. , to be published in J. Geophys. Res. ) and this work will not be described here. Finally, the Aerosol_cci Validation Team of the project conducted intercomparisons using ground-based and spaceborne lidars. Although comparisons are more difficult between lidars and a dataset such as AerGOM because the quantities measured by both instruments are different, the interest of these intercomparisons resides in the fact that lidars provide time series of measurements with a very good temporal frequency and a high vertical resolution; they don't present the same kind of limitations or aging characteristics as spaceborne occultation of limb scattering measurements. Therefore, they can provide very valuable information on the quality and stability of GOMOS.
, 2012). An overview of these lidar stations and the corresponding dataset available for validation is given in Table  6. If not given as primary product, the aerosol backscatter coefficient β aer at wavelength λ = 532 nm and altitude z has been calculated from the backscatter ratio BSR and the molecular backscatter coefficient β ray as follows: β aer z ð Þ ¼ BSR-1 ð ÞÂβ ray z ð Þ ð4aÞ or BSR λ; z ð Þ¼ β aer z ð Þ þ β ray z ð Þ À Á = β ray z ð Þ À Á ð4bÞ The molecular backscatter coefficient β ray can be calculated using the ideal gas law from the altitude dependent molecular number density N(z) or the atmospheric temperature T(z) and pressure profile P(z) and the differential Rayleigh backscatter cross section (dσ ray (π)) / dΩ: β ray z ð Þ ¼ N z ð Þ Â dσ ray π ð Þ À Á =dΩ: ¼ N A Ã P z ð Þ ð Þ = R a Ã T z ð Þ ð ÞÂ dσ ray π ð Þ À Á =dΩ ð5Þ where the value π reflects the fact that the scattering angle is equal to 180 degrees, Ω is the solid angle for detection, N A , the Avogadro constant and R a , the gas constant for dry air.
g. Jäger et al. , 1995 , Chazette et al. , 1995 , Jäger and Deshler, 2002). For Polar stratospheric clouds (PSCs), E/B can vary depending on the PSC type. For PSC type I and II (PSC type II being ice PSCs)  Reichardt et al. (2004)  report E/B values between 56 and 135, and between 16 and 42, respectively. Several authors report E/B values for ice PSCs and cirrus clouds around 20 (e. g. Chen et al. , 2002 , Noel et al. , 2009). For the comparison between GOMOS and the NDACC lidar we chose E/B = 50 sr as representative for stratospheric background conditions, unless a time and altitude dependent value was provided in the NDACC dataset, like in the case of Garmisch-Partenkirchen. For that site E/B varies between 40 sr and 50 sr in the stratosphere and between 10 and 70 sr between 5 and 14 km altitude.
Validation of Level 2 extinction coefficient at 550 nm First, the AerGOM level 2 (L2) data, version 3. 0 have been compared to the NDACC lidar data. A spatial difference of 500 km and a temporal difference of 12 h have been used as co-location criteria. An example comparison is given in Fig. 5 , showing the AerGOM data (blue) and the lidar profiles (red). The profiles from the Mauna Loa observatory on 13 November 2008 show the increased aerosol layer remaining from the eruption of the Kasatochi volcano (52°N, 176°W) in August 2008  (Wang et al. , 2013). For the comparison, the lidar data have been smoothed when the altitude resolution was finer than 100 m, and interpolated to the AerGOM vertical grid. Further, the interpolated lidar profiles have been convolved with the AerGOM averaging kernel (shown on the left panel) and compared to the AerGOM profiles.
A statistical analysis has then been carried out for each of the three reference datasets (Garmisch-Partenkirchen, Mauna Loa and Dumont d'Urville) on a one-kilometer altitude grid. This analysis has been performed for E/B values of 30, 50 and 70 sr taking into account the observation selection criteria used in versions 2. 19, 3. 00 and for a "best case" defined as a test selection assuming very conservative selection criteria: star magnitude b 2, solar zenith angle N 105°, and star temperature N 5000 K. Selection criteria for versions 2. 19 and 3. 00 are given in Table  5. In the case of Garmisch-Partenkirchen, however, instead of the fixed value E/B = 50, we have used the E/B value given in the NDACC lidar dataset. In addition, a separate statistical analysis was performed for PSC observations, as identified by the AerGOM aerosol type flag using the temperature criterion T b 197 K.
This last PSC validation was applied to the Dumont d'Urville dataset, the only one concerning the polar regions. The aim of this analysis using three different values of E/B is to take into account the possibility that the standard value of 50 sr is not optimal to describe specific aerosol types or altitude/latitude ranges. Performing the same statistical analysis for both CCI-GOMOS versions 2. 19 and 3. 00 is important to verify that the relaxation of the observation selection criteria (specified in Table  5 ) does not alter the accuracy of the extinction time series. A detailed presentation of this extended evaluation of the CCI-GOMOS CDRs is given in Appendix B. Fig. 6  shows an overview of the statistical analyses performed using the 3 reference lidar datasets, with in the case of the Dumont d'Urville lidar station, separate analyses for liquid sulfate aerosols and PSCs as identified by the AerGOM aerosol type flag.
In all four cases, two different statistical analyses are conducted using the CCI-GOMOS version 3. 00. The plots on the left hand side show a statistical analysis taking into account all AerGOM profiles relevant for the considered case. The AerGOM median and the spread of the distribution defined as an interval of two times the standard deviation centered on the median, are shown by the blue curve and the light blue region, respectively. The same quantities are plotted as black solid lines and error bars for the lidar measurements. Relative standard deviation are also plotted on the central panel for comparison with the relative difference between AerGOM and the lidar dataset (in green). This analysis shows that, for all four cases considered, AerGOM version 3. 0 and the lidar dataset agree within the spread of the distribution.
The plot shows the IQM (red line) and the IQR (rectangular box). The dashed lines show the range of values which are not considered as outliers, and the red crosses show, for each case, individual outliers encountered in the AerGOM data sampling. For this second statistical analysis, all cases show agreement between both AerGOM and lidar datasets, except in the case of Garmisch-Partenkirchen, where the Level 2 profile comparison shows a bias at all altitudes, and agreement within the IQR only below 19 km. AerGOM values are higher on average, which can indicate the presence of a real bias, an underestimation of the E/B for the lidar profile conversion, or a combination of both factors. Both datasets agree within the error bars, representing two times the standard deviation. The lidar station at the Mauna Loa observatory provides data down to 15 km altitude.
Validation of Level 3 extinction coefficient at 550 nm The evaluation of the GOMOS level 3 dataset was applied to CCI-GOMOS CDRs in versions 2. 19 and 3. 00, of which the characteristics are given in Table  5. Lidar profiles were selected within the bin spread (time period of 5 days, latitude and longitude intervals of 5°and 60°r espectively). Fig. 7a ,b illustrates the statistical analysis of Level 3 comparisons for the CCI-GOMOS extinction dataset in version 2. 19 and 3. 00. For Garmisch-Partenkirchen, the agreement improves with respect to the Level 2 intercomparisons: the variability in L3 is integrated within a bin cell, making it closer to the lidar profiles on average. This averaging is particularly effective in case of rapidly evolving structures, such as PSCs. Level 3 intercomparisons for Mauna Loa give quite similar results as for the Level 2 evaluation.
In this case where no particular rapidly evolving patterns are expected in the background aerosol layer, the averaging does not bring additional statistical benefit from integration. Finally, for the lidar station at Dumont D'Urville, the statistical analysis on the Level 3 extinction dataset includes all cases, covering background sulfate aerosol and PSCs. Both AerGOM and lidar datasets agree well within the uncertainties. Fig. 8  shows a time series of PSC observations made at Dumont D'Urville in the 5-day CCI-GOMOS time interval 12-17 September 2007. For this time interval, four co-located lidar profiles were found, and compared to the same CCI-GOMOS binned extinction profile. Lidar data are shown in black, while the CCI-GOMOS data in versions 3. 00 and 2. 19 are coloured in magenta and blue, respectively.