dataesr/scientific-paragraphs-categorization huggingface dataset:
We use the FORTRAN code provided by  Reichler et al. (2003) , which we have adapted to the LMDZ model. With this the tropopause pressure is computed at each timestep. In the rare case that the algorithm does not find the tropopause in a grid column, it is set to a default value that only depends on the latitude ϕ (in radians) through the relationship p TP [hPa] = 500 -200 • cos(ϕ). (1) In this case the tropopause is assumed to vary between 300 hPa at the Equator and 500 hPa at the Poles, independently of the season.
Nucleation The formation rate of new particles via binary homogeneous nucleation of sulfuric acid and water vapour is parametrised as a function of the sulfuric acid gas-phase concentration, the relative humidity, and the absolute temperature as described by  Vehkamäki et al. (2002). This parametrisation provides the nucleation rate J nuc in units of particles cm -3 s -1 , the total number of molecules in each nucleated particle N tot , and the mole fraction of H 2 SO 4 in the new particle x. The equations are cumbersome and not repeated here, but it should be noted that we rely on the Fortran code provided by  Vehkamäki et al. (2002). The parametrisation is not valid any more under conditions where the number of molecules in the critical cluster is below 4 (which occurs mainly at large H 2 SO 4 vapour concentrations).
Given that there are only a few measurements to constrain the van der Waals coagulation term, and the mixed results obtained in our model (see Sect. 3. 3. 1), we do not include this process in our default model, but offer it as an option in the code of the model (using the enhancement factors from Eqs. 29 and 30 in  Chan and Mozurkewich, 2001). Coagulation is only activated in the stratosphere. For convenience, we repeat here the equations from Jacobson et al. (1994). New particles resulting from the coagulation of particles from size bins i and j have a combined particle volume V i,j = V i + V j. They are distributed among the size bins according to the following definition of the distribution factor f i,j,k : f i,j,k =            V k+1 -V i,j V k+1 -V k V k V i,j for V k ≤ V i,j < V k+1 ; k < N B 1 -f i,j,k-1 for V k-1 ≤ V i,j < V k ; k > 1 1 for V i,j ≥ V k ; k = N B 0 otherwise.
For colliding particles of identical size and a temperature of 298 K, E(0) and E(∞) have values of 1. 25 and 2. 27, respectively. The actual enhancement factor for stratospheric conditions can be expected to lie in between these two cases. As in previous studies, the van der Waals coagulation term improves the comparison to observation for particle number concentration (not shown) and particle average size (shown in Fig. 12 ), but it makes it a little worse for AOD, as shown in Fig. 13 , with the global mean stratospheric AOD peaking too low (and too early) compared to the Sato climatology. Given 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 10 that there are only a few measurements to constrain the van der Waals coagulation term, and the mixed results obtained in our model, we do not include this process in our default model, but offer it as an option in the code of the model.
Acknowledgements. This article is a contribution to DFG-funded priority programme SPP 1689. We thank  Luo Beiping  for providing a compilation of stratospheric aerosol observation data and  Josefine Ghattas  for preparing the LMDZORSTRATAER_v6 configuration. The authors acknowledge computing time from the  TGCC  under  GENCI  projects t2014012201, t2015012201, t2016012201, and t2017012201. The authors would like to thank two anonymous reviewers for their constructive comments, and the editor,  Graham Mann , for his editorial review of the first submitted version of this paper, which led to significant improvements to the introduction section. Edited by: Graham Mann Reviewed by: two anonymous referees.
Code availability. The code of S3A can be downloaded along with the LMDZ model from http://lmdz. lmd. jussieu. fr. S3A code is mostly contained within a separate directory (StratAer) of the model physics and is activated at compilation with a CPP key. A model configuration (LMDZORSTRATAER_v6) containing the S3A module is also available within the modipsl/libIGCM model environment of the IPSL Earth system model (http://forge. ipsl. jussieu. fr/igcmg_doc).
Sources of uncertainty This study comes with a variety of uncertainties from various sources that must be considered. The first source of uncertainty comes from the EC data. NEE is measured at a half-hourly scale. However, it comes with data gaps that are filled to compare the observed data with the simulated data at a daily time scale. The gap-filled NEE is based on a modeling procedure  (Reichstein et al. 2005). At the Tharandt site, for example, the uncertainty of the gap filling methods is up to 10%  (Grünwald and Bernhofer 2007). In addition, GPP and respiration are not directly measured, but are partitioned from the NEE, which is based on another modeling procedure  (Reichstein et al. 2005 ). When we analyzed the drought event of 2003 at Tharandt, we found that GPP was 1. 87 tC•ha -1 less than in the previous year.
Acknowledgements Measurements were supported by the  CarboEurope-IP (European Commission ,  Directorate-General Research, Sixth Framework Programme , Priority 1. 1. 6. 3:Global Change and Ecosystem (Contract No. GOCECT-2003-505572 )) of the  Max Planck Institute for Biogeochemistry in Jena. We thank  Martina Mund  of the  Georg-August-University of Göttingen  for the inventory information at the Wetzstein site. EC data at the Tharandt site were kindly provided by the  Department of Meteorology at TU Dresden. We thank  Rico Fischer ,  Sebastian Paulick  and  Franziska Taubert  for their support and discussions. Funding ER, FB and AH were supported by the  Helmholtz-Alliance Remote Sensing and Earth System Dynamics. ER was supported by the  Helmholtz Impulse and Networking Fund  through the  Helmholtz Interdisciplinary Graduate School for Environmental Research (HIGRADE).
The differences in response time for the different sensors were corrected by finding the lag times from correlation analysis of the various time series. Ambient air composition was subtracted from the CO 2 , H 2 O and H 2 data, SO 2 interference on H 2 S data was calibrated and corrected. Multi-GAS measurements were taken on 1 December 2015 for 90 min. Post processing was performed using the Ratiocalc software  (Tamburello, 2015).
Satellite observations We used land surface temperature maps derived from ASTER thermal images (AST08 Land Surface Temperature, or LST, product at 90 m/pixel spatial resolution), short wavelength infrared Advanced Land Imager (ALI) images (nine bands from 0. 4 to 2. 4 μm at 30 m/pixel spatial resolution, with a panchromatic band (PAN) at 10 m/pixel spatial resolution-  Ungar et al. 2003) , Hyperion hyperspectral data (196 usable bands from 0. 4 to 2. 4 μm at 30 m/pixel spatial resolution)  (Pearlman et al. 2003)  and high resolution visible images (from Google Earth) to track potential changes in activity at the lava dome in the 15 years leading up to the fieldwork presented here. Hyperion and ALI are on-board NASA's Earth Observing 1 (EO-1) spacecraft. Observations by EO-1 were obtained via the NASA Volcano Sensor Web (VSW, e.
g. Davies et al. , 2015) , which utilizes advanced spacecraft operations software developed to streamline the process to re-task spacecraft as quickly and efficiently as possible to effect rapid data acquisition of dynamic targets  (Chien et al. , 2005). All ASTER granules covering El Misti were acquired from NASA Reverb at both level 1b (radiometrically and geometrically corrected at sensor radiance) and at level 3 AST08, extending the coverage of  Jay et al. , (2013)  from 2010 to late 2016. The AST08 LST dataset is produced by applying the temperature emisivity seperation algorithm (TES) to atmospherically corrected images  (Gillespie et al. , 1998). We used DA White's Aster Preprocessing Toolkit (APTK, White 2016) to extract the correct rational polynomial coefficients (RPCs) from the level 1b data, which were then used to orthorectify the LST product to the 30-m shuttle radar topography mission digital elevation model with bilinear resampling.
Discussion A magmatic gas signature The composition of the gases emitted from the El Misti lava dome is indicative of derivation directly from outgassing magma. This is apparent in the high SO 2 concentration and the relatively low H 2 O/SO 2 ratio, which together imply small to negligible contributions from a hydrothermal system. Given that we measured both H 2 O-H 2 and SO 2 -H 2 S redox couples, the gas-melt equilibrium temperature and oxygen fugacity can be calculated. Following  Giggenbach (1987 Giggenbach ( , 1996) )  and using the thermodynamic data of  Stull et al. (1969)  provides two equations with two unknown: log H 2 H 2 O ¼ - 12707 T þ 2:548- 1 2 logf O 2 ð2Þ and log SO 2 H 2 S ¼ 27377 T -3:986 þ 3 2 logf O 2 -logf H 2 O ð3Þ Solution yields an equilibrium temperature of 532 °C and a logfO 2 equivalent to ΔQFM = +2.
Acknowledgements This research was conducted as part of the ' Trail By Fire ' expedition (PI:  Y. Moussallam ). The project was supported by the  Royal Geographical Society  (with the  Institute of British Geographers ) with the  Land Rover Bursary ; the  Deep Carbon Observatory DECADE Initiative ; Santander,  Ocean Optics ; Crowcon; Air Liquide; Thermo Fisher Scientific; Cactus Outdoor;  Turbo Ace  and  Team Black Sheep. We thank  Jean-loup Guyot ,  Sebastien Carretier ,  Rose-Marie Ojeda ,  Pablo Samaniego  and  Jean-Luc Lepennec  together with IRD South-America personnel for all their logistical help. We are extremely grateful to  Marco Rivera  and all OVI personnel for their help and support. YM acknowledges support from the  Scripps Institution of Oceanography Postdoctoral Fellowship program.
The in situ tests of first ever autonomous aerosol and cloud backscatter LIDAR (light detection and ranging) systems implemented on buoys for Arctic observations has been achieved in 2015 within the French EQUIPEX IAOOS project. The environmental and operational constraints were met by adopting a concept of a fibered microjoule lidar system using a laser diode. Two systems have been developed with and without polarization analysis capability. A specific optical design was used for polarization discrimination. These systems were integrated in buoys and tested in the Arctic in 2014 and 2015 at latitudes higher than 80°N. Data were transmitted through an Iridium space link. Measurements have been obtained 90% of the time from the non-polarized system in 2014 over 8 months as the first fully equipped buoy drifted from the Barneo Russian camp close to the North Pole toward Svalbard.
In order to limit satellite communication costs each profile is only transmitted after averaging and summation, in progressively increasing altitude bins (Table  2 ). Background noise is determined from the upper channels. Accelerometers are implemented in the buoy, to provide with information concerning the tilt angles of the buoy as linked to potential ridge formation. All data are transferred to IPEV (http://iaoos. ipev. fr) using Iridium satellite link. The main characteristics of the system are listed in Table  2. Since L A and L B , and OF 1 and OF 2 (or OF 3 ) (see Fig. 1 ), have the same properties, we may expect to have a similar divergence on both channels. A first calibration of the transmission of the system was performed in lab, using the emission diode. This transmission measurement involves the optics between the input/output fibers OF 2 (or OF 3 ) and OFD (see Fig.
Data calibration in clear air An important difficulty in processing raw data came from spurious icing of the emission window, despite the built-in heating system. The icing problem is impacting the calibration of the IAOOS microlidar for a quantitative analysis of aerosol and cloud optical properties. However, as icing also increases the scattered signal by the emission window, measured just before the backscattered atmospheric signal, this information was used to determine a nonicing index (N-IcI), used to put aside too iced profiles, and possibly correct others. N-IcI is defined here from the ratio of the scattered signal on the iced window (IWS) to the clean window scattered signal (CWS) as N-IcI = CWS/IWS. It varies from 1 for a perfectly clear to 0 for a totally iced window. Before being deployed, the optical transmission of IAOOS 2 has been measured in lab.
Non-polarized system The first fully equipped IAOOS buoy (IAOOS 2) was placed in the iced arctic ocean at the North Pole (Barneo camp) on April 15, 2014. The measurements then taken during the drift showed a very good behavior of the whole system, with no unexpected power consumption in arctic cold conditions, and very few profiles lost because of satellite communications. The 2014 drift provided about 750 profiles of total backscattering profiles in the area 80-88°N and 10-25°E. Attenuated scattering ratios SR att obtained after data correction, calibration and normalization to molecular scattering are reported in Fig. 5. The inclination of the buoy is also reported on the same figure. It shows that the buoy was close to zenith in spring (tilt of about 1°), and experienced a strong tilt due to a compression ridge at day 183 (early July), and then recovered at the end of August.
Severe icing periods were observed in winter. Although solutions could be found as explained in our analyzis to face moderate icing conditions, severe icing is an important limitation to get a proper and stable calibration of the system. Solutions for avoiding icing of the window, as experienced mostly during the end of autumn, and winter period have to be improved for forthcoming deployed systems. Outside severe icing periods, valuable statistical analyzes could be started on the data obtained in Central Arctic. Results show the high occurrence of very low level arctic clouds, with an average altitude lower than 800 m in the period going from end of spring to end of autumn at latitudes higher than 82°N. Polarization information is also of importance to better describe cloud properties and identify the occurrence of spherical aerosol particles.
Acknowledgments Sample collection was funded by  Antarctic Funding Initiative   AFI4/02  and analysis were supported by the  Laboratory of Environmental Marin sciences (LEMAR ,  UMR 6539 ). The authors are grateful to  E. Ponzevera  and  Y. Germain  for their assistance during isotopic measurement and  Samia Mantoura ,  Damien Carson ,  Andrew Miller ,  Paul Mann , the  Base Commander  and all staff at  Rothera Research Station (BAS). We also thank  P. Tréguer ,  S. Lalonde  and  N. Coffineau  for their interesting comments and suggestions.
A growing number of applications needs GIS mapping information and commercial 3-D roadmaps especially. This paper presents a solution of accessing freely to 3-D map information and updating in the context of transport applications. The method relies on the OSM road networks that is 2-D modeled intrinsically. The objective is to estimate the road elevation and inclination parameters by fusing GPS, OSM and DEM data through a nonlinear filter. An experimental framework, using ASTER GDEM2 data, shows some results of the improvement of the roads modeling that includes their slopes also. The map database can be enriched with the estimated inclinations. The accuracy depends on the GPS and DEM elevation errors (typically a few meters with the GNSS sensors used and the DEM under consideration).
Introduction Nowadays, an increasing number of transport applications would need to have access to 3-D map information whether for location or route planning problems  [1]. Some solutions of 3-D maps exist but they are commercial and expensive. In this context, the purpose of our work is to offer a free solution based on the use of OpenStreetMap (OSM, http://www. openstreetmap. org). OSM is a free web-service that provides map data from uploaded GPS traces and user-registered information as names, types, traffic directions and inclinations of roads  [2]. More generally, OSM is built by volunteer users who contribute and maintain these GIS data all over the world. The level of accuracy depends primarily on existing data. It should be noticed that the level of accuracy is optimal when one is in a dense area for that many users have refined the data over time.
Providing none 3-D support, the OSM database is increasingly used in the development of transport applications  [3, 4]. In our earlier work  [5] , a method has been proposed to update the OSM roads slopes by using a car positioning system composed of 2 GPS units. To make the method more robust, we are interested in the fusion of the dual-GPS positioning system and data from a Digital Elevation Model (DEM) that accounts for the terrain relief. This can lead to the improvement of the estimated elevations  [6]. One of the objectives of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mission was to produce a global DEM of a very large part of the Earth, based on a horizontal resolution of 1 arcsecond (30 m)  [7]. In 2009, this American-Japanese collaboration allows to produce a model whose horizontal resolution was three times sharper than the DEM of the Shuttle Radar Topography Mission (SRTM)  [8]  that was built with at 3 arcseconds (about 90 m).
The aim is to complete the mapping system with 3-D information as nodes elevations and inclinations of roads. We equipped a standard vehicle with a Global Navigation Satellite System (GNSS) based on a dual-GPS system whose receivers antennas are aligned on the extremities of the same side of the roof. A such consideration induces a more accurate positioning of the vehicle by bounding the possible errors due to GPS signal degradation  [19]. Knowing that OSM provides a digital modeling of roads that relies of the 2-D geolocation of their nodes depending on land surveys, the method requires a map-matching step with GPS data. The goal is to deliver an estimation of the road inclinations. To this end, we develop an original method based on a geometric approach to manage the DEM data and compute a more accurate MSL-elevation.
In the proposed solution, all measurements are sequentially processed by an unscented Kalman filter (UKF) that is more suited to nonlinear equations than the standard Kalman filter. An original aspect of our approach lies in the modeling of the OSM and DEM databases. They are considered as sensors with their own measurement errors which makes possible to develop a global filtering approach. Finally, by injecting the estimated vehicle elevations and their orthogonal projections on the matched road into a least squares method, we get an estimate of the slope of this road. We have chosen to validate this approach in a context of road experimentation mainly composed of highways that are built in an open environment with a varied but limited terrain elevation. We present experimental results of 3-D time-location of the vehicle that allow to get an accurate estimation of the inclination of the roads traveled during the test.
OSM Road Network The next step is to fuse the previous GPS-estimated parameters with the OSM database in order to match the traveled road. As a consequence, the accuracy of the car ground-positioning can be increased since the OSM road network is modeled by road segments whose 2-D location of transitional nodes from one segment to another is available. The accuracy of the GPS-based positioning is straightforwardly related to the intrinsic performance of the GPS receiver (e. g. , about a few meters for our sensors). Fusing GPS-based estimation with the OSM data allows to consider the road geometry as a constraint for the car displacement. In this case, the estimator can take benefit of the accuracy of the map database. The ground-location (x OSM t+1 , y OSM t+1 ) and the ground-direction of its road segment θ OSM t+1 are the main features of each node: Z OSM t+1 =    x OSM t+1 y OSM t+1 θ OSM t+1    = h OSM (X t+1 ) + V OSM t+1 (8) V OSM t+1 is an additive white Gaussian noise with zero mean and R OSM t+1 as the noise covariance matrix so that the statistical aspects related to the inhomogeneities and errors of land surveys can be integrated.
Estimation of the Road Inclination The goal of this paper is to propose a theoretical framework to OSM data enrichment with road inclination. The proposed nonlinear modeling is solved by an unscented Kalman filter (UKF). It is known that the UKF approach improves the state estimation compared with the EKF-methods in the nonlinear case  [21]. The benefit of the UKF-approach also lies in its lower computational complexity compared with the particle filtering methods  [22]  which are not essential here due to moderate non-linearity of the modeling. Figure  1  shows that the filter is based on a sequential processing structure of the GPS, OSM and DEM data. Finally, the filter estimates the inclination which is used to enrich the road network database.
Correction: Proposed Solution The proposed solution is based on a sequential processing of sensor data. An original feature of our method consists in a statistical modeling of map data from OSM and DEM. The popular approaches match the estimate of the position with the road network  [24]. Our statistical method allows to take into account the mapping inaccuracies in the estimation process. Indeed, in many solutions, the map matching procedure uses a projection onto the road network and thus does not consider the map accuracy as a parameter of the problem. Our approach uses this accuracy to build a map-matching procedure that gives in the estimation process a relative weight to the OSM-based correction. This is done via the measurement covariance matrix R OSM t. The accuracy of the OSM measurements is relative to the accuracy of the GPS traces uploaded by the users in the database.
Moreover, the DEM database, also modeled as an additional sensor, is used to correct the estimation of the elevation of the vehicle. Similarly to OSM measurements, the DEM data are used in the estimation process, considering their accuracy defined by the covariance matrix R DEM t. Again with this "sensor", we do not use a all-or-nothing correction but a weighted correction using the DEM measurement covariance matrix. This leads to an improvement of the estimation accuracy, especially of the elevation which remains a parameter with high errors in GPS (typically several meters in some cases or more in multipath propagation problems of GNSS signals). As a consequence, the integration of OSM and DEM data leads to a better estimation of the road network parameters and in particular of the road sections elevation (including slope).
This estimation is performed in real time and improves the OSM database by extending it to the 3-D case. The updated database will then be used for future positioning thus leading to an overall improvement in estimation. Figure  1  details the different steps of the filtering solution. The measurements are sequentially processed in the following order: GPS readings #1 and #2, OSM measurements and DEM measurements. The relative weight of each sensor is related to their measurement error covariance matrix which initially appears in the computation of the sigma-points χ Z α t+1|t related to measurement equation Equation (  16 ).