dataesr/scientific-paragraphs-categorization huggingface dataset:
The second geographic information layer used refers to the river network provided by the BD CARTHAGE ® database. It contains the different hydrographic segments and their attributes. The road cuts (low water crossings) dataset is derived from the intersection of river and road networks and calibrated using an inventory of road flooding during the last 40 years provided by the Gard road management services. Based on this dataset,  Versini et al. (2010a)  identified 1970 road cuts in the Gard road network and produced a classification of these road sections according to their susceptibility to flooding (Fig. 4 ). The four susceptibility classes range from s 0 to s 3 , measuring 1093, 359, 297 and 221 points respectively. The "very low" susceptibility to flooding class s 0 corresponds to roadriver intersections that have empirical return periods of flooding exceeding 40 years.
Population mobility The study area resident population is 111 511 individuals. An overview of the population sociodemographic characteristics is displayed in Table  2. As explained in Sect. 3. 1, we used travel-activity data from the National Transport and Travel Survey to attribute programs of activities to the population in our study area. In order to respect the regional statistical representativeness of the survey sample and benefit from an extensive schedule library with satisfactory variability, we select travel-activity data corresponding to survey responders Figure  5. Probability tree diagram representing the method of measuring motorists' flood risk exposure. The highlighted (bold lines) path represents the example of a motorist who crossed 3 road cuts (rc) with the following probabilities of submersion: P (rc 1 = 0.
The next step of its development is to better reproduce the travel durations observed in the ENTD dataset. In fact, the activity-based mobility modeling approach requires data describing the location of different activities conducted by the individuals. Whereas work and school activity locations are identified based on census data, it is more complicated to locate secondary activities such as shopping and leisure activities. We assume for this first application that secondary activities are located within a buffer of 500 m around the place of residency. However, future efforts are needed to improve the secondary activity location rules by taking into consideration travel costs and places knowledge  (Marchal and Nagel, 2005). The buffer size used for secondary activity locations may affect the simulated travel durations.
This limitation is due to the difficulty in providing the necessary information because of the large number of parameters that need to be integrated regarding road infrastructures and geomorphologic specificities of road cuts. On the social side, understanding behavioral responses is key to the estimation of human impacts. Recently, this aspect has been taken into account in MobRISK, which now incorporates a decision-making module to consider possible activity rescheduling decisions and mobility adaptation to weather disruptions. The integration of individual decisions and coping capacities enables us to shift from exposure measurement to social vulnerability quantification  (Terti et al. , 2015). To advance in this direction, the use of welldescribed, geolocalized, time-stamped and reliable human impact datasets is needed for model verification  (Terti et al.
, 2017). While activity-based mobility models are using classical travel-activity pattern simulations, we opted for a schedule assignment method based on the effect of sociodemographics on activity sequence discrepancies. This choice is consistent with the main purpose of the MobiCLIMeX project, which aimed at understanding the driving forces of dynamic exposure over previous flash flooding events. Nevertheless, this tool could also be used to evaluate the longer-term evolution of human exposure related to climate change and its consequences in terms of extreme weather patterns. To move toward this direction, the module reproducing travel-activity patterns by schedule assignment would need to evolve toward the simulation of mobility scenarios. In terms of implementations of MobRISK, the next step is to extend the simulation of the 8-9 September 2002 flash flooding event to the entire Gard area, as nearly all the municipalities of Gard were impacted by this event.
Mo-bRISK simulates individual mobility using an activity-based approach and individual exposure to road submersion and benefits from previous works and existing datasets characterizing road network sensitivity to flash floods in the Gard area. The first application of the MobRISK simulation over the Alès area for the period of the 8-9 September 2002 flash flood event offers the possibility of identifying the road sections bearing a higher risk for the population in time and space, both in terms of submersion probability and traffic load. The results show that road submersion hazard was mainly located on principal roads connecting the Alès municipality to other major cities in the Gard area. The temporal analysis indicates that the highest road submersion hazards occurred at night at the end of a weekend, when traffic load is supposed to be lower.
Acknowledgements. The work developed in this paper was funded by the  French national research agency ANR  under two complementary projects ADAPTFlood and MobiCLIMeX respectively  ANR-09-RPDOC-001-01  and  ANR-12-SENV-0002-01. This work was also supported by the  INSU  through MISTRALS funding. It is part of the  Hydrometeorological Experiment in the Mediterranean (HyMeX) initiative. Edited by:  Margreth Keiler Reviewed  by: two anonymous referees.
Data availability. Data associated with this work are described in Sect. 3 and publicly available through data providers as INSEE (www. insee. fr), IGN (http: //professionnels. ign. fr/gratuite-des-donnees), OHMCV (http://ohmcv. osug. fr/spip. php?article30). Road cuts and road flooding susceptibility data were published in Versini et al. (2010a)   and  Naulin et al. (2012).
The GOMOS ozone data are of high quality in the stratosphere and the mesosphere, but the current operational retrieval algorithm (IPF v6) is not optimized for retrievals in the upper troposphere-lower stratosphere (UTLS). In particular, validation of GOMOS profiles against ozonesonde data has revealed a substantial positive bias (up to 100 %) in the UTLS region. The retrievals in the UTLS are challenging because of low signal-to-noise ratio and the presence of clouds. In this work, we discuss the reasons for the systematic uncertainties in the UTLS with the IPF v6 algorithm or its modifications based on simultaneous retrievals of several constituents using the full visible wavelength range. The main reason is high sensitivity of the UTLS retrieval algorithms to an assumed aerosol extinction model.
We have developed a new ozone profile inversion algorithm for GOMOS data (ALGOM2s version 1. 0), which is optimized in the UTLS and uses IPF v6 advantages in the middle atmosphere. The ozone retrievals in the whole altitude range from the troposphere to the lower thermosphere are performed in two steps, as in the operational algorithm: spectral inversion followed by the vertical inversion. The spectral inversion is enhanced by using a DOAS-type method at visible wavelengths for the UTLS region. This method uses minimal assumptions about the atmospheric profiles. The vertical inversion is performed as in IPF v6 with the Tikhonov-type regularization according to the target resolution. The validation of new retrieved ozone profiles with ozonesondes shows a dramatic reduction of GOMOS ozone biases in the UTLS.
In the spectral inversion, horizontal column density of ozone, NO 2 , NO 3 , and aerosol extinction are retrieved simultaneously from UV-VIS spectrometer data in the wavelength range 250-675 nm, for each tangent altitude  (Kyrölä et al. , 2010). The spectral inversion relies on the standard maximum likelihood method, which is enhanced by the socalled full-covariance-matrix inversion in order to take into account uncertainties due to incomplete scintillation correction  (Sofieva et al. , 2010). The vertical inversion is linear; it is performed for each constituent separately. The vertical inversion is stabilized by a Tikhonov-type regularization according to the target resolution  (Sofieva et al. , 2004; Tamminen et al. , 2004). Since the aerosol extinction spectrum is not known a priori, a polynomial of wavelength is used for the description of the aerosol extinction in the GOMOS retrievals.
In comparisons with ozonesondes at two polar stations,  Tamminen et al. (2006)  found ∼ 10 % bias for Sodankylä (67. 4 • N, 23. 6 • E) and over 20 % negative bias for Marambio (64. 3 • S, 56. 7 • W), for altitudes below 15 km. For GOMOS ozone profiles processed with IPF v5,  van Gijsel et al. (2010)  have performed an analysis analogous to  Meijer et al. (2004)  but on a significantly larger dataset and found large GOMOS positive bias, over 40 %, in the tropical UTLS and below. This conclusion is in full agreement with the validation work of  Mze et al. (2010)  using ozone soundings from eight SHADOZ stations. The validation studies performed so far (which are also focused mainly on the stratospheric ozone) indicate, however, the presence of large GOMOS ozone bias in UTLS also in IPF v6 data.
For example,  Adams et al. (2014)  reported over 20 % positive GOMOS bias in UTLS in comparisons with OSIRIS/Odin v. 5. 0x profiles, which are of good quality in the UTLS (e. g. ,  Cooper et al. , 2011). Hubert et al. (2016)  performed validation of GOMOS v. 6 profiles with ozonesondes and reported a large positive ozone bias in the UTLS and the troposphere nearly everywhere except at polar high latitudes (only wintertime measurements are available for these locations). Large positive GOMOS ozone biases in UTLS are also observed in data agreement tables created for the HARMOZ dataset  (Sofieva et al. , 2013) , which consists of user-friendly vertically gridded ozone profiles from six limb-viewing instruments (GOMOS, MIPAS, SCIAMACHY, OSIRIS, SMR and ACE-FTS). The operational GOMOS retrieval algorithm is optimized mainly for the stratosphere and the mesosphere  (Kyrölä et al.
Data and methodology GOMOS IPF v6 nighttime ozone profiles (with solar zenith angles at tangent point larger than 105 • ) from the full mission are used in this work. The GO-MOS data have been screened for outliers according to the recommendations in the readme document (https://earth. esa. int/web/sppa/mission-performance/ esa-missions/envisat/gomos/products-and-algorithms/ products-information). The GOMOS IPF v6 GOMOS dataset in user-friendly format is available at the Ozone_cci web page (http://www. esa-ozone-cci. org, Sofieva et al. , 2013). Hereafter, we refer to IPF v6 as "V6" for short. For validation, we used ozonesonde data from the Network for the Detection of Atmospheric Composition Change (NDACC, http://www. ndacc. org), WMO's Global Atmospheric Watch (GAW, data distributed by the World Ozone and Ultraviolet Data Centre, http://www.
2. The presented comparison of GOMOS ozone profiles with ozonesonde data, together with other analyses overviewed in the introduction, shows a consistent picture: GOMOS V6 ozone is overestimated in a large part of the UTLS. The largest difference was observed in the tropics (median relative difference up to 100 %). The observed positive bias does not show any dependence on the star brightness, making it impossible to select a subset of GOMOS dataset with realistic ozone profiles in the UTLS region. These validation results prompt us to develop an improved retrieval algorithm, which is discussed below.
GOMOS ozone inversion optimized for the UTLS (ALGOM2s) In this section, we describe a new GOMOS retrieval algorithm optimized for the UTLS. The proposed ozone retrieval algorithm in the whole GOMOS altitude range from the upper troposphere to the lower thermosphere inversion consists of the following steps: 1. retrievals of horizontal column density ozone profile in the UTLS using visible triplets 2. forming the resulting horizontal column density ozone profile in the whole GOMOS range by weighted combination of the V6 ozone profiles in the middle atmosphere and the new triplet ozone profile in the UTLS 3. performing the vertical inversion in the same way as in V6. Below we describe each step in detail. Hereafter, we refer to the new processing method and the new GOMOS ozone dataset as "ALGOM2s v1.
Validation against ozonesondes For validation against ozonesondes, we used the same data as in validation of V6 dataset presented in Sect. 2. Figures 9 and 10 show the results of the validation of ALGOM2s ozone profiles against NDACC ozonesondes for tropical and high-latitude stations, respectively. For comparison, the results for V6 ozone profiles are also shown. For tropical stations, the dramatic reduction of biases is observed. At polar stations, the ALGOM2s results are not worse than those of V6 (at polar stations, also V6 data have a small bias in the UTLS in comparisons with ozonesondes). Figure  11  shows the global distribution of the median relative difference between GOMOS ALGOM2s and ozonesonde data as a function of absolute altitudes and relative to the tropopause altitudes, similar to that presented in Fig.
6 Some geophysical illustrations in the UTLS using the ALGOM2s data As an illustration of a large dataset, the GOMOS ozone profiles in the equatorial region (latitudes 20 • S-20 • N) in 2007-2008 are compared with ozone profiles from other satellite instruments -MIPAS  (von Clarmann et al. , 2003 (von Clarmann et al. , , 2009)) , OSIRIS  (Degenstein et al. , 2009)  and ACE-FTS  (Bernath et al. , 2005)  -which have been already used in several scientific studies in the UTLS  (Aschmann et al. , 2014; von Clarmann Figure 12. Spread in % of relative differences with respect to ozonesonde profiles for GOMOS V6 (left) and GOMOS ALGOM2s retrievals (right). et al. , 2007; Cooper et al. , 2013; Kunze et al. , 2010; Liu et al. , 2009; Manney et al. , 2011; Sioris et al. , 2014). As observed in Fig. 13  (left), the GOMOS V6 ozone profiles are strongly biased in the tropical UTLS, while new GOMOS data are close to the profiles from other satellite instruments.
A very good agreement of GOMOS and OSIRIS profiles in the middle stratosphere can be noticed in Fig. 13  (left and right panels). For the ALGOM2s retrievals, the mean profile is close to that of OSIRIS also in the UTLS. The ALGOM2s gives slightly smaller ozone in the vicinity of the tropical tropopause (∼ 20-30 %) compared to MIPAS and OSIRIS. This feature is also observed in comparisons with ozonesondes. The mean uncertainty of ALGOM2s ozone profiles is smaller in the UTLS than that of V6 data in absolute values (Fig. 13 , center). Seasonal variations and temporal evolution of ozone profiles in the UTLS are the subject of intensive research (e. g. ,  Gettelman et al. , 2010; Hegglin et al. , 2010; Randel and Jensen, 2013; Sioris et al. , 2014). Figure  14  shows the temporal evolution of ozone profiles in the equatorial region (20 • S-20 • N) from OSIRIS and two versions of GOMOS data.
The pronounced seasonal cycle associated with the variations in the tropopause height are clearly observed in all datasets. The ozone values in the troposphere for the AL-GOM2s retrievals are closer to those of OSIRIS than V6 profiles. It should be noted that the coverage of the UTLS region by GOMOS data is limited due to applied screening by signal-to-noise ratio; thus the seasonal cycle is reproduced in GOMOS data with a significant sampling uncertainty. The Asian summer monsoon (ASM) contains a strong anti-cyclonic vortex in the UTLS, spanning from Asia to the Middle East. The ASM has been recognized as a significant transport pathway for water vapor and pollutants to the stratosphere (e. g. ,  Kunze et al. , 2010; Park et al. , 2007    Figure  15  shows ozone distributions at 100 hPa (∼ 16 km) in June-August from OSIRIS, ACE-FTS, MIPAS, SCIA-MACHY, and GOMOS measurements.
To obtain these maps, all available data have been used. For GOMOS, the maps are shown for both V6 and ALGOM2s ("GOMOS new") retrievals. The low ozone values in Asia associated with the strong upward motion of tropospheric air are clearly seen in these distributions, and peculiar features of ozone associated with the ASM are very similar in all datasets displayed. For V6, the ozone UTLS data have a significant positive offset, but lower values associated with ASM are observed in V6 distribution as well. For new GOMOS retrievals, the distribution is very similar to those by other satellite instruments.
Summary and discussion Satellite data with a good quality in the UTLS are very important for the studies of the complex processes and longterm changes in the UTLS. The validation of GOMOS IPF v6 ozone profiles against ozonesonde data has shown a strong overestimation (median relative difference up to 100 %) of the ozone values by GOMOS IPF v6 in the UTLS. The largest difference was observed in the tropics. The observed positive bias does not show any dependence on the star brightness, thus making it impossible to select a subset of GOMOS ozone V6 data with realistic ozone profiles in the UTLS region. The extensive sensitivity analyses have shown that the current GOMOS retrievals of ozone, aerosols, and NO 2 are very sensitive to parameterization of aerosol extinction used in the inversion.
In this paper, we have presented a new algorithm for GOMOS ozone retrievals in the UTLS by using the visible triplets. Using relatively narrow wavelength bands in the triplet inversion allows avoiding the scintillation and dilution correction (allows using Level 1b transmittances) and reducing uncertainty related to aerosol extinction spectral dependence and removal of Rayleigh scattering. The proposed inversion using visible triplets in the UTLS is stable with respect to small variations of reference and absorbing wavelengths. For the proposed inversion (ALGOM2s), the ozone profiles follow V6 data in the middle atmosphere and follow the triplet ozone profiles in the UTLS and the troposphere. Such an approach seems to be advantageous for the ongoing Ozone_cci project (http://www. esa-ozone-cci.
org/). GO-MOS V6 ozone data have been extensively validated; they exhibit good quality and small biases with respect to groundbased measurements in the stratosphere  (Adams et al. , 2014; van Gijsel et al. , 2010; Hubert et al. , 2016; Laeng et al. , 2014; Rahpoe et al. , 2015). The ALGOM2s ozone data preserve all positive features of V6 in the stratosphere and have a significantly improved quality in the UTLS. ALGOM2s ozone concentrations are on average lower and less noisy in the UTLS than the IPF v6 data. The ALGOM2s ozone profiles are in good agreement with ozone sondes and other satellite data having a good quality in the UTLS. The geophysical phenomena are seen by the GOMOS dataset, but the coverage of the UTLS data by GOMOS is limited. In the current dataset, all occultations of stars with insufficient UV flux (cool or/and dim stars) are excluded due to their problems in the upper atmosphere and also in the UTLS due to the influence of dark current noise, which increases during the GOMOS mission  (Tamminen et al.
, 2010). For climatologic studies, the application of a similar ozone inversion to averaged GOMOS transmittances, the approach used in  Fussen et al. (2004 Fussen et al. ( , 2005 Fussen et al. ( , 2010) )  and  Tétard et al. (2009) , might provide a richer GOMOS climatologic dataset in the UTLS due to inclusion of more occultations and a less strict data filtering according to signal-to-noise ratio.
Data availability The ALGOM2s ozone dataset is available at the AL-GOM website (https://earth. esa. int/web/sppa/activities/ instrument-characterization-studies/algom/data-resources). It will be also provided in the user-friendly Ozone_cci netcdf format  (Sofieva et al. , 2013) , on both pressure and altitude grid; it is expected to replace the current GOMOS dataset used in the Ozone_cci project. It will be available at the Ozone_cci website (http://www. esa-ozone-cci. org/?q=node/161). Interested users can acquire all intermediate data products from the corresponding author.
Acknowledgement. The development of the GOMOS algorithms has been performed in the framework of the  ESA  project  ALGOM. The validation against ozone sondes is supported by the  ESA   DRAGON-3 program  and  Ozone_cci  project. The comparison with satellite measurements has been performed in the framework of the  ESA Ozone_cci  project. The FMI team acknowledges the support by the  Academy of Finland  (project  INQUIRE ). The correlative data from balloon-based ozonesonde used in this publication were obtained from the  NDACC network  (www. ndsc. ncep. noaa. gov), World Ozone  and  Ultraviolet Data Centre  (http://www. woudc. org),and the  Southern Hemisphere Additional Ozonesondes network  (SHADOZ, http://croc. gsfc. nasa. gov/shadoz). We acknowledge the meticulous and sustained work of the PIs and staff at ozonesonde stations to acquire and maintain ozone profile data records of high quality.
For group analyses, the algorithm can generate population atlases and establish homology across the database, facilitating comparison of morphometric measurements in localized regions. Moreover, fshape mean computation or matching can be performed with flexible constructions of fshapes that can include multiple geometric shapes and function parameters; this allows the building of specific sets of geometry and function features to compare across multiple groups. In this paper, we demonstrate the use of this algorithm for investigating the effect of age and glaucoma on retinal nerve fiber layer (RNFL) and choroid. Loss of RNFL in the optic nerve head (ONH) region is a well-known hallmark of glaucoma that leads to irreversible vision loss  (Medeiros et al. , 2005). Currently, the RNFL thickness profile along a circular scan centered at the ONH and the sectoral average thickness maps are used in clinics to assess the disease progression.
Fshape Registration The framework of functional shapes (or fshapes) provides a quantitative measure of inter-subject variability in the RNFL and choroid. In this section, we briefly summarize the algorithm which is detailed in  Lee et al. (2017). Let the ith subject's RNFL or choroid thickness be represented by (X i , f i ), where X is the layer surface (geometry) and f is the surface-indexed function (thickness here) mapped on X. Let a template exemplar for this database be denoted by X * , f * , consisting of a template surface geometry X * and an associated surface-indexed signal (thickness here) mapping f *. Given X * , f * as the template fshape, and (X i , f i ) as the ith target fshape to be registered to the template, the fshape framework will estimate a smooth deformation φ i of the template geometry X * and a residual ζ i to be added to the template function f * , such that after transformation of the template fshape with the mapping φ i to the ith target fshape, the geometry of the transformed template matches the geometry of the ith target X i ≈ φ i (X * ) and the transformed template function plus residual matches the function of the ith target i.
e. , f i ≈ f * + ζ i •φ i -1. Hence, for each of the target fshapes (X i , f i ) for all i = 1. N a pair φ i , ζ i consisting of a smooth deformation and a function residual are estimated such that: X i , f i ≈ φ i , ζ i • X * , f * = φ i (X * ) , f * + ζ i • (φ i ) -1 = Xi , f i (1) The function residual added to the template function estimated by fshape matching effectively becomes the representative of the target function in the geometry of the template. By fixing a template geometry and function, a group of target fshapes can be brought into the coordinates of the template such that the residuals representing different target function values are now indexed on the same template geometry. The choice of the template is an important consideration and the mean of the observations in the database is a standard choice.