dataesr/scientific-paragraphs-categorization huggingface dataset:
The PSC observed by the lidar is growing throughout the time period covered by this 5-day CCI-GOMOS grid cell, and the agreement is best for version 3. 00 in the latest co-location shown. This example illustrates the effect of sampling in the GOMOS dataset in case of a rapidly evolving dynamical pattern, which should be taken into consideration when interpreting the validation statistics presented here. For each station: First panel (co-located datasets): median (solid) and mean (dashed) values of CCI-GOMOS versions 2. 19 (red) and 3. 00 (blue), respectively; magenta and blue regions: 2sigma interval across the AerGOM median value, for versions 2. 19 and 3. 00, respectively; black solid and dashed curve: median and median of lidar dataset, respectively; error bars: 2sigma interval across the lidar values.
The CALIOP dataset were grouped into 16-days periods and the total attenuated backscatter and depolarization ratio profiles was averaged over 1degree intervals along the orbit track. Correction of the transmission for molecular scattering and ozone absorption was applied. The molecular backscatter is retrieved using air density from GMAO (Global Monitoring and Assimilation Office) data reanalysis and removed from the total backscatter measured by CALIOP to retrieve the aerosol backscatter coefficient. In order to correct a known calibration bias, the data have been recalibrated between 36 and 39 km, which is a cleaner region of the stratosphere compared to the 30-34 km altitude range used operationally. The treatment of the CALIOP level 1 data in the stratosphere can be found in  (Vernier et al.
This variability probably reflects differences in typical size and composition characteristics in the local aerosol content. Depending on altitude and latitude bands, E/B values vary between 25 and 53 sr (see Appendix B. 3). Such variability in the E/B values was already reported by  Vernier et al. (2011b) , although values shown here are lower than the averaged values derived by these authors. The variability found on E/B led to reconsider the validation of AerGOM (Level 2) and CCI-GOMOS (Level 3) datasets using lidar profiles, by addressing the choice of E/B values. As a next step, the statistical analysis was performed again for co-located CALIOP backscatter values and AerGOM extinction profiles between 2006 and 2009, using E/B values between 30 sr and 70 sr. The outcome of this study and comparison with results by  Vernier et al.
(2011b). It also leads to the suspicion that the first version of the volcanic sulfur emission inventory was not sufficient to assess the real volcanic contribution to the radiative forcing of the atmosphere due to lack of data. For this reason, the CCI-GOMOS dataset was used to revise this inventory, by filling gaps in the available datasets, improving estimates of emitted SO 2 for individual volcanic eruptions, and identifying new volcanic signatures in the CCI-GOMOS extinction time series. xIn this section, we show how the present work contributed to the improvement of the inventory of sulfur emissions by volcanoes and to the modelling of the radiative forcing by stratospheric aerosols. We demonstrate also that GOMOS points to the importance of seasonally varying desert dust in the lowermost stratosphere.
The MIPAS dataset As another atmospheric sounding instrument on ENVISAT, the limbscanning Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) analyzed the emission of atmospheric species active in the mid-infrared spectral region. Its high spectral resolution (0. 025 cm -1 and 0. 0625 cm -1 ), allowed the retrieval of volume mixing ratio profiles of sulfur dioxide (SO 2 ). Two different datasets of SO 2 derived from MIPAS exist: (1) one consisting of monthly 10-degree zonal mean profiles with a vertical coverage of 10-45 km which was derived from mean spectra  (Höpfner et al. , 2013) , and, (2) one dataset retrieved from single limb-scans with the altitude coverage up to 23 km  (Höpfner et al. , 2015). The latter data version is suited to analyze the vertical and horizontal evolution of SO 2 plumes from volcanic eruptions, and has been utilized in the study described below.
Improved inventory of volcanic eruptions. Including the CCI-GOMOS extinction dataset to the other data sources already used in its previous version  (Brühl et al. , 2015) , a new improved inventory was made for the volcanoes contributing to the stratospheric aerosol burden, now three-dimensional. This inventory is presented in Table  7. It contains now about 230 explosive eruptions reaching the tropopause region or higher altitudes for the period July 2002 to March 2012, versus 102 eruptions in the previous inventory  (Brühl et al. , 2015). Using these two limb viewing instruments covering the same ENVISAT time period was essential to fill data gaps of the individual instruments. In particular, several important eruptions in 2004, 2006 and 2007 could be identified using the CCI-GOMOS dataset, which were not seen by the MIPAS instrument due to lack of data at the corresponding location and time.
GOMOS extinctions were converted to SO 2 plumes by application of the parameterization of  Grainger et al. (1995)  and fit to analogue situations seen by MIPAS, taking into account zonal wind. The attribution to individual eruptions was done based on the Bulletin Reports on http://volcano. si. edu and nadir satellite observations. The SO 2 masses were calculated by integration over the plumes downwind of the volcanoes. Although quantitative estimates of the error are difficult to infer, the most important source of uncertainty resides in the data gaps and in some cases vague observations in the Smithonian bulletins, typical uncertainty is in the order of about ±30%. Table  7  indicates (last column) which eruptions could be identified and quantified using the MIPAS dataset (M, primary) and which ones, using the GOMOS dataset (G).
The simulations by  Brühl et al. (2015)  (black curve) are biased compared to  Solomon et al. (2011)  (green crosses) who estimated the radiative forcing from satellite extinction measurements (SAGE, CALIOP, GOMOS). Taking more volcanic eruptions into account in the estimate of the total stratospheric aerosol budget, especially during periods where data gaps affect the MIPAS dataset, improves the results considerably (blue curve). Use of a more appropriate scheme for desert dust (V2, red curve and purple crosses) almost removes the bias. The remaining slight bias in years influenced by high latitude eruptions can be removed by inclusion of lower stratospheric aerosol below 100 hPa, but this is also uncertain in  Solomon et al. (2011). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
e. typically for dim cool stars). Further, a revision of the molecular absorption cross-section spectra used for the spectral retrieval of the gas species led to a more realistic attribution of the extinction coefficient to aerosol and gas species, resulting in a significantly fewer negative values of the aerosol extinction coefficient, especially at high altitudes. The AerGOM (Level 2) dataset and the CCI-GOMOS (Level 3) dataset were evaluated successively using lidar profiles from 3 ground-based lidar station representative for different typical regions: Mauna Loa situated in the tropical region, Garmisch-Partenkirchen representative for northern mid-latitudes, and the Antarctic station of Dumont d'Urville. For this latter station, separate statistical analyses of Level 2 profiles were performed for background aerosols and for PSCs as identified by     7 ; in black: simulation with less eruptions as given in  Brühl et al.
(2015). Green crosses show annual average radiative forcing estimates derived from observations by  Solomon et al. , 2011. The red curve (simulated radiative forcing) and the purple symbols (corresponding annual average) show the results obtained using the V2-simulation (with entries of Table  7  and an improved desert dust scheme). the GOMOS aerosol type flag using a selection criterion based on the temperature. Intercomparisons using lidar profiles from the Garmisch-Partenkichen dataset show a better agreement in the case of the Level 3 evaluation than for the Level 2 assessment. Good agreement between AerGOM and lidar data was found in the case of Mauna Loa and Dumont d'Urville (uncertainty in the range 1%-35%) below 22 km. This evaluation shows, overall, similar results for the CCI-GOMOS time series in version 2.
About 230 explosive eruptions could be identified, and their impact on the aerosol load, quantified. An overview of these eruptions, including the plume height and an estimate of the emitted SO 2 content, is presented in Table  7. The updated inventory of volcanic emissions was then used in the EMAC model in order to simulate the evolution of the stratospheric aerosol concentration and to calculate the corresponding radiative forcing. Comparisons of the modelled aerosol burden with the CCI-GOMOS extinction field over the whole time period show a good agreement between both datasets, and the estimate of the radiative forcing from EMAC using the updated inventory better matches radiative forcing estimated from observations published by  Solomon et al. (2011) , than previous estimates published by  Brühl et al.
Abbreviations The As discussed in Section 2. 4, the gas absorption cross-sections spectra used for the retrieval of the gas species have been completely revised in order to improve their spectral range, temperature dependence, and to optimize the consistency of the data for the whole range of wavelengths and temperatures encountered in GOMOS measurements. The aim of this appendix is to provide details on the methodology used to elaborate the new crosssection spectra from the available datasets and for the different gas species retrieved so far by AerGOM: O 3 , NO 2 and NO 3. We limit here the discussion to the spectral range of interest for this paper, i. e. the spectral range covered by the GOMOS spectrometers SPA and SPB1 (See Section 2). A. 1. Ozone  Serdyuchenko et al. (2014)  published high-resolution ozone absorption cross-sections covering the 213-1100 nm spectral range over the temperature range 193-293 K which covers the whole range of experimental conditions encountered by GOMOS with a spectral resolution higher than the GOMOS resolution (See Section 2.
A. 2. NO 2 The case of NO 2 requires the use of different sets of measurements. Vandaele et al. (2003)  propose a unified dataset set up from various previous measurements, resulting in two (medium resolution and a high resolution) recommended spectra. The medium resolution absorption spectrum spans the 13,200-42,000 cm -1 spectral range (238-757. 8 nm) with a spectral resolution of 2 cm -1 (0. 01-0. 11 nm), higher than the spectral resolution of the GOMOS SPA spectrometer. At this resolution, no pressure effect is detectable. The temperature is described using a linear dependence. By its composite character, this dataset is expected to be less sensitive to experimental noise due to the use of an average of many different profiles. All these features make it adequate for use in AerGOM in the range 240-695 nm.
Hence, the high-resolution NO 2 cross-section spectrum proposed by  Vandaele et al. (2003)  can be used in the 750-757. 8 nm range without taking pressure effects into account. Again, this dataset is preferred to  Vandaele et al. (2002)  because of the use of averaging making it less sensitive to experimental noise. Above 757. 8 nm, the only dataset available is the one from  Vandaele et al. (1998)  measured with a spectral resolution of 2 cm -1 (0. 11-0. 12 nm) and provided at temperatures of 220 and 294 K. Although this resolution is slightly lower than the SPB1 spectral resolution, it is the best choice available for this spectral range. The temperature dependence is calculated by linear interpolation of the two provided spectra. A. 3. NO 3  Orphal et al. (2003)  measured NO 3 high-resolution absorption spectra in the range 12,600-21,500 cm -1 (476-794 nm) at 294 K with a resolution of 0.
6 cm -1 (0. 01-0. 04 nm). The authors investigated in detail the temperature dependence around the absorption peak at 662 nm and proposed a model of temperature dependence valid around this peak (650-675 nm). This work improves the previous measurements in terms of calibration and spectral resolution, including measurements by  Yokelson et al. (1994)  at four temperatures over the range 400-694 nm and with a resolution of about 0. 1 nm. Also, measurements by  Sander (1986)  at 230 and 298 K covering the 400-691 nm range have a lower resolution (1 nm). Other NO 3 reference cross-section spectra were considered but appeared to be combinations and/or normalized spectra based on  (Sander, 1986). Below 400 nm, only very sparse measurements from  (Sander, 1986)  are available. In the revision of the cross-section database, the NO 3 high-resolution cross-section spectrum from  (Orphal et al.
, 2003)  is used in the whole available range. Orphal's temperature dependence model is also used between 650 nm and 675 nm. Between 476 and 694 nm, a choice must be made between Yokelson's dataset for which the temperature dependence is available and Orphal's dataset with improves the data quality, but is only given at 294 K. The decision was made to use, at each wavelength, a temperature dependence calculated from  (Yokelson et al. , 1994)  and to apply it to NO 3 cross-section profiles from  (Orphal et al. , 2003). Wherever no data are provided by  Yokelson et al. (1994)  in the spectral range 476-794 nm (i. e. in temperature range 200-220 K and the spectral range 694-794 nm), Orphal's values at 294 K are used at all temperatures. Between 440 and 476 nm, we dispose on data from  (Yokelson et al.
, 1994)  at four temperatures, and lower-resolution data at two temperatures from  (Sander, 1986). Comparisons between all available datasets showed that the cross-section spectra provided from  (Yokelson et al. , 1994)  diverge from the other available reference spectra (Sanders' data and other recommended spectra based on Sanders). Therefore, it was decided to use Sander's NO 3 cross-section spectra at 298 K and to apply again Yokelson's temperature dependence wherever it is available. Below 220 K, the temperature dependence is unknown and the cross-section values at 220 K are used down to 200 K. The effective spectral resolution in the range 440-476 nm is thus Sanders' low resolution of 1 nm, which is lower than the SPA resolution, but it was the best solution to avoid any significant jump in the cross-section spectrum.
Below 400 nm, the very sparse measurements from  (Sander, 1986)  are not considered and the NO 3 cross-section spectrum is kept constant and equal to its value at 400 nm. In order to definitely prevent any abrupt jumps between datasets, wavelengths over an interval equal to two times the resolution of the spectrometer (0. 8 nm for spectrometer SPA and 0. 13 nm for spectrometer SPB, see Section 2. 1) around the transition points were not taken into account.
19 and 3. 00 of the CCI-GOMOS CDR, as given in Table  5. This estimation of E/B is repeated for these two sets of selection criteria, and for the criteria used in the "BEST" case (test selection assuming very conservative observation selection criteria) defined in Section 5. 2. The observation selection criteria for the "BEST" case are: Star magnitude b 2 SZA N 105°and Star temperature N 5000 K. In all cases, we use here the AerGOM dataset, version 3. 0.
GARMISCH-PARTENKIRCHEN In the case of Garmisch-Partenkirchen (Table  B1 ), AerGOM is potentially overestimated with respect to the lidar dataset. From Table  B1 , the best choice of E/B is 70 sr at all altitudes. The results are very consistent between 2. 19 and 3. 00; at lower altitudes, the consistency is good between versions BEST and 2. 19, but less satisfactory with version 3. 00. The evaluation using Mauna Loa time series (Table  B2 ), shows the best agreement between lidar and AerGOM co-located events for E/B = 50 sr at all altitude ranges. AerGOM and the lidar dataset show a good agreement, and there is a good consistency between the statistics calculated for the 3 versions (2. 19, 3. 00 and BEST), which seems to confirm the appropriateness of the observation selection criteria in both versions 2.
B. 2. Statistics for level 3 comparisons Tables B6 to B8 show, for E/B = 30 sr, 50 sr, and 70 sr, similar quantities as previously (statistics on Level 2, Section B1) for the CCI-GOMOS level 3 dataset. Again, the statistical analyses refer to the lidar stations at Garmisch-Partenkirchen (Table  B6 ), Mauna Loa (Table  B7 ), and Dumont d'Urville (Table  B8 ) respectively. The comparisons are made for CCI-GOMOS level 3 datasets in versions 2. 19 and 3. 00, and are given as the median, interquartile mean (IQM) and interquartile range (IQR) of the relative difference between CCI-GOMOS and the lidar data (GOMOS-LIDAR)/LIDAR [%]. As before, averaging is performed over 3 altitude bands corresponding to the ranges 10-15 km, 16-21 km, and 22-27 km. For Garmisch-Partenkirchen (Table  B6 ), an E/B value of 70 sr gives the best agreement between lidar and CCI-GOMOS time series, consistently with the Level 2 statistical analysis.
B. 3. E/B values for GOMOS/CALIOP In order to get a better insight into the supposed variability of the E/B needed to convert the different lidar backscatter time series into extinction time series, we deduced the E/B values from the CCI-GOMOS and CALIOP datasets, both of them providing data on a global scale. This calculation was performed for three four-month time periods on the year 2008. Table  B9  shows E/B values for each considered time period (January-April, May-August and September-December 2008), three latitude bands between 50°S and 50°N and three altitude regions between 15 km and 30 km altitude. The median E/B values are calculated as the median of the ratio GOMOS/CALIOP. Values provided by  Vernier et al. (2011b)  for E/B, which are given for the same latitude/altitude range, are reproduced for comparison.
Overview of NDACC lidar sites providing reference datasets. The indicated available period is restricted to the period of interest for this work(April 2002-April 2012)and is given as month/year. The used parameters are indicated as follows: BSR: backscatter ratio, T: temperature, P: Pressure, log 10 N: log 10 of molecular number density, E/B: extinction-to-backscatter ratio, β aer : aerosol backscatter coefficient.
Supporting work for the development of GOMOS datasets was performed in the framework of a Marie Curie Career Integration Grant within the 7th European Community Framework Programme under grant agreement no. 293560. We thank the ICARE Data and Services Center for providing development support and tools. We thank Dr. Ann-Carine Vandaele for her support and fruitful discussions concerning the revision of the cross-section database. Parts of the modelling work were supported by the EU project STRATOCLIM. Lidar observations at Dumont d'Urville are supported by the French Polar Institute IPEV.
Supporting work for the development of GOMOS datasets was performed in the framework of a Marie Curie Career Integration Grant within the 7th European Community Framework Programme under grant agreement no. 293560. We thank the ICARE Data and Services Center for providing development support and tools. We thank Dr. Ann-Carine Vandaele for her support and fruitful discussions concerning the revision of the cross-section database. Parts of the modelling work were supported by the EU project STRATOCLIM. Lidar observations at Dumont d'Urville are supported by the French Polar Institute IPEV. ADM atmospheric dynamics mission AerGOM GOMOS retrieval algorithm optimized for aerosols AOD aerosol optical depth ATSR along track scanning radiometer BSR backscatter ratio CALIOP cloud-aerosol lidar with orthogonal polarization Appendix A.
Elaboration of a new cross-section database for AerGOM CALIPSO cloud-aerosol lidar and infrared pathfinder satellite observa- tions CCI climate change initiative CDR climate data record DMS dimethyl sulfide DOAS differential optical absorption spectroscopy ECV essential climate variable EMAC ECHAM/MESSy atmospheric chemistry model ENVISAT ESA's environmental satellite ESA European space agency E/B extinction to backscatter ratio GCOS global climate observing system GMAO global monitoring and assimilation office GOME global ozone monitoring experiment GOMOS global ozone monitoring by occultation of stars IASI infrared atmospheric sounding interferometer IPF instrument processor facility; operational GOMOS retrieval algorithm IQM interquartile mean IQR interquartile range L2 level 2, satellite sensor projected data L3 level 3, gridded satellite dataset IPCC intergovernmental panel on climate change K Kelvin MAESTRO measurements of aerosol extinction in the strato- sphere and troposphere retrieved by occultation MESSy modular earth submodel system MIPAS Michelson interferometer for passive atmospheric sounding NDACC network for the detection of atmospheric composition change NO 2 nitrogen dioxide NO 3 nitrate OCS carbonyl sulfide OMI ozone monitoring instrument OSIRIS optical, spectroscopic, and infrared remote imaging system O 3 ozone POAM polar ozone and aerosol measurement PSC polar stratospheric cloud PSD particle size distribution SAA southern atlantic anomaly SAGE stratospheric aerosol and gas experiment SCIAMACHY scanning imaging absorption spectrometer for atmospheric chartography SNR signal-to-noise ratio SO 2 sulfur dioxide SPA GOMOS spectrometer A SPB GOMOS spectrometer B SPARC stratospheric processes and their role in climate SSiRC stratospheric sulfur and its role in climate SZA solar zenith angle TOMS total ozone mapping spectrometer UV ultraviolet V1, V2 different versions of the EMAC model (See  §6.
6 (7. 6-9. 8). After confirmation of RNA structural integrity, 5μg RNA was subjected to Microbe-Express and MicrobEnrich (Invirogen) protocols to remove bacterial rRNA and mammalian RNA, respectively. cDNA synthesis and sequencing cDNA libraries were constructed using the Ion Total RNA-Seq Kit v2 (Life). Templates for RNASeq were prepared with the Ion PI Hi-Q OT2 200 Kit (Life) using the Ion OneTouch 2 System for Ion Proton System semiconductor sequencing. These templates were sequenced using the Ion PI Hi-QSequencing 200 Kit and Ion PI Chip v2, on the Ion Proton Sequencer. Preliminary analysis of the ensuing sequencing data pertaining to quality of the run, read lengths, and coverage was performed using the Torrent Suite software. Total reads, usable reads, and mean read length for each sample are indicated in S1 Table.
Even after depletion of eukaryotic RNA, only 0. 81-6. 68% of in vivo sequencing reads mapped to the R. rickettsii genome. Reads that mapped to rRNA and tRNA were not analyzed, thereby leaving an average coverage depth of 19-128 reads per gene of the R. rickettsii protein coding sequence. As such, the results described herein represent a vigorous assessment of the transcriptional program of R. rickettsii within the infected mammal. The 1345 predicted protein-encoding open reading frames of R. rickettsii Sheila Smith were analyzed for total in vivo cDNA quantity and expression changes. The average expression value of individual genes was calculated for the four in vivo samples, and is expressed as average reads per kilobase per million reads (RPKM), a value that normalizes for gene length. Expression changes to individual genes were calculated in the DEseq2 software package, and are expressed as fold change of in vivo samples as compared to in vitro.