dataesr/scientific-paragraphs-categorization huggingface dataset:
The ROSES Observatory Network The ROSES (observatory network for groundwater systems at the French national level) was initially set up to answer water management issues and was strengthened in the framework of the implementation of the European Water Directive. It gathers more than 77,000 stations, with 74,000 groundwater quality monitoring stations and 4400 monitoring wells. All types of aquifers are monitored in metropolitan territories as well as French overseas territories. All data are stored within the ADES database (http://www. ades. eaufrance. fr) managed by several governmental agencies.
For gauging flood discharge, non-contact methods have been developed and evaluated: surface radar, largescale particle image velocimetry (LS-PIV) based on images from fixed cameras or videos on YouTube  (Dramais et al. , 2014; Welber et al. , 2016; Le Boursicaud et al. , 2016). For large rivers, satellite data or acoustic Doppler current profiler surveys are used (e. g. ,  Mangiarotti et al. , 2013; Paris et al. , 2016). Groundwater levels are monitored using pressure transducers. Depending on the process of interest (hydrological cycle, tides, barometric effect, earthquakes), the frequency of measurements varies from one per day to 1 Hz or even greater. These conventional measurements are complemented using multiparameter probes and sampling to analyze major chemical elements and isotopic ratios using a wide range of natural and anthropogenic tracers for water residence time  (Leray et al.
, 2016a). Solexs is an optical instrument for the measurement of irradiance profiles in snow, which can be related to snow microstructure and ice absorption  (Picard et al. 2016b ). 6 Databases and Metadatabases in OZCAR In order to comply with the public data policy, a mandatory condition for recurrent funding, most of the OZCAR observatories developed data and/or metadata portals where data can be accessed and sometimes downloaded. All portals in OZCAR provide research data with the exception of the ADES portal (http://www. ades. eaufrance. fr/), which provides monitoring information about groundwater level and quality for the whole French territory and was primarily designed for operational use. A critical analysis of the portals reveals a large heterogeneity in practices in OZCAR: (i) free access vs.
access through login/password or no access; (ii) type of data that are provided: metadata only vs. possible downloading of the data; raw data vs. corrected data or more elaborate products including simulation results; (iii) access through information system and GIS interfaces, including sometimes visualization tools, vs. access to files or to ftp files; (iv) data formats and storage: relational databases vs. file repositories; (v) granularity of a dataset (e. g. , one rain gauge or all the data collected within one catchment); (vi) level of information provided in the metadata. More specific information on the diversity of current practices in OZCAR is given in Appendix 2 of the Supplemental Material (Supplemental Table  S2 ). In terms of metadata provision, the RBV metadata catalog (http://portailrbv.
sedoo. fr/#WelcomePlace; André et al. , 2015)  is a common initiative for providing visibility to the data collected within the RBV. It follows the INSPIRE (INfrastructure for SPatial InfoRmation in Europe, http://inspire. ec. europa. eu/) norms and can harvest existing sites when the latter are compliant. For the other portals, a manual system was proposed to feed the metadata. The usefulness of the data portal remains limited, however, because currently the definition of the granularity of datasets is heterogeneous; metadata that are not automatically harvested are quickly obsolete; and metadata documentation is incomplete, implying that access to the data portals is not granted. One particular ambition of OZCAR is to improve data accessibility and (i) high-frequency monitoring in the critical zone (at the interface with the atmosphere, in the subsurface, and at the outlet of catchments) and (ii) multidisciplinary monitoring of "hotspots" and during "hot moments" of the critical zone.
interoperability, building on the experience of the scientific teams involved in the network (see below). 6 Linking Data and Critical Zone Models within OZCAR Here we review the different modeling initiatives developed by the various scientific communities gathered in OZCAR. Surprisingly, despite the wide disciplinary spectrum found in OZCAR, common trends can be depicted and observed at the international scale. Classically, models in OZCAR can be classified into process understanding, system understanding, and management and/or prediction purposes  (Baatz et al. , 2018). All scientific communities in OZCAR have developed or used simple models for identifying and understanding CZ processes at different scales in their observatories. Models are built in order to interpret the collected data, but data can also question existing representations, in particular when new sensors or increased resolution are available.
Several disciplines and/or compartments of the CZ are involved at larger spatial scales (e. g. , small to medium catchments) and are generally addressed. Process representations are often simplified (i. e. , process-based models with approaches such as reservoir models) compared with models deployed for process understanding, because they must cope with a larger degree of heterogeneity. A model calibrated with in situ data is thus a powerful tool to extend the knowledge acquired at local sites both in space and time (see examples in Supplemental Table  S3 ). Sensitivity analysis can also help to identify functioning hypotheses that are the most consistent with observations by varying model parameters or comparing different process representations. The AMMA-CATCH observatory, in collaboration with African researchers, gives a good example of this effort.
In the Ara catchment (10 km 2 ), observations of surface fluxes, soil moisture, and groundwater monitoring as well as geochemical and geophysical data and gravimetric measurements (Fig. 7 ) showed that water uptake by deep-rooted trees is the main driver of groundwater discharge in the dry season  (Richard et al. , 2013; Hector et al. , 2015). The mechanistic ParFlow-CLM model  (Maxwell and Miller, 2005) , incorporating the identified processes, was chosen to reproduce the observed functioning  (Hector et al. , 2018). Finally, a significant number of approaches developed in the OZCAR observatories are motivated by societal challenges such as a better estimation of sea level rise, the prediction of natural risks (floods, droughts, erosion, snow and ice avalanches, contamination, etc. ), water resources management, carbon storage, and other ecosystem functions.
The models used for management and prediction purposes are usually inspired by those developed for system understanding and are generally simplified to represent the main active processes and to be used operationally and/or in real time due to computational time constraints and to lower data availability. For instance, Crocus  (Brun et al. , 1992) , a numerical model used to simulate snow cover stratigraphy, and the blowing snow scheme SYTRON  (Vionnet et al. , 2018)   S3. Model integration and coupling between compartments of the CZ requires the development of dedicated tools. Modeling platforms allowing the building of models from available components and for managing exchanges of variables and fluxes between components have been successfully developed in OZCAR, mainly by the hydrological community.
Challenges in Data Management The amount of data produced in OZCAR is expected to increase in the near future due to the improvement in high-frequency acquisition systems and the development of new sensors. Simultaneously Open Data is pushed in Europe by the INSPIRE directive for spatial data and the Aarhus agreement (http:// ec. europa. eu/environment/aarhus/) for environmental data. This requires data to be permanently and freely accessible online, allowing data discovery, visualization, and downloading. Open data is expected to enhance new connections between datasets, data mining, and easier use in models. Scientists are aware of these possibilities but may remain reluctant to openly provide their datasets. The reasons put forward are: lack of technical skills or human resources, legal constraints, data quality and validation, priority for their personal use through embargo on their datasets, lack of traceability of open data, and lack of acknowledgement of their work.
g. ,  Morin et al. , 2012; Nord et al. , 2017; Guyomarc'h et al. , 2018) , and licensing of the datasets (e. g. , Creative Commons licenses; https://creativecommons. org/share-your-work/licensingtypes-examples/) will be encouraged within OZCAR by providing guidelines on the definition of the corresponding datasets, their granularity, and on filling the associated metadata. It is also planned to propose a minimum Information System kit for observatories that lack the required expertise.
Scientific and Technical Challenges in Linking Critical Zone Data and Models? A first challenge is related to the process representation at different scales. At small scale, the identification of elementary processes can benefit from the instrumental progress discussed above. One example is the development of geochemical reactive transport models (i. e. ,  Steefel et al. , 2015)  at the catchment scale exploiting in particular high-frequency datasets of stream chemistry, constraints from new isotopic systems  (Sullivan et al. 2016) , and the new representation of heterogeneities at the grain size  (Le Borgne et al. , 2013). Another challenge is the proper representation of vegetation and biological activity on chemical and physical reactions that determine hydrological and matter budgets. When moving to larger scales, unstructured heterogeneity, nonlinearity, and thresholds at all scales  (Blöschl and Zehe, 2005) , and the scarcity of integrated data at the scale of interest  (Cook, 2015) , preclude the use of the same approach.
This requires the design of calibration and model evaluation criteria and data assimilation systems that are able to account for this uncertainty. Numerical uncertainty must also be quantified when models are used for predictive purposes. From a more technical point of view, important challenges are related to our ability to perform coupling between process modules running at different space and time scales and to link databases, GIS layers, and models  (Bhatt et al. , 2014). Facilitating data-model interactions to build integrated modeling requires novel technical developments allowing both data interoperability and model sharing (e. g. , OLES project  [Anquetin et al. , 2014] ; CSMDS project  [Peckham et al. , 2013] ; CUAHSI community model [https://www. cuahsi. org/data-models/community-models/] and web services based on the Basic Model Interface  [Jiang et al.
OZCAR sites and observatories have their own initial scientific questions, monitoring strategies, databases, and modeling activities, but all share the main overarching goal: to monitor, understand, and simulate CZ adaptation to a changing planet in the "new climatic regime"  (Latour, 2018). The challenge of OZCAR is thus to build upon the heterogeneity of sites, scientific cultures, and data management practices, to define a strategy at the network level enabling scientists to share models and data in order to significantly improve our integrated understanding of the CZ as a system and form a new generation of scientists. The OZCAR community aims to achieve this goal by defining cross-site activities, through the construction of a common database and metadatabase environment, by developing and sharing new instruments for exploring the CZ, by defining a set of parameters in some representative sites that should be measured at all sites, and through facilitating the interaction between data and Earth subsurface models, in particular through a better representation of the coupled water, energy, and biogeochemical cycles at all time scales.
Acknowledgments OZCAR-RI is supported by the  French Ministry of Education and Research, through the Allenvi Alliance. OZCAR observatories have benefited from numerous sources of funding coming from the different research institutions supporting the infrastructure ( ANDRA, BRGM, CEA, CNES, CNRS, Ifsttar, INRA, IPEV, IPGP, IRD, IRSN, Irstea, Météo-France, LNE), universities (Avignon Pays de Vaucluse, Bourgogne Franche-Comté, Bretagne Occidentale, Grenoble-Alpes, La Réunion, Lyon, Montpellier, Orléans, Paris Diderot, Pierre et Marie Curie, Rennes, Rouen-Normandie, Savoie-Mont Blanc, Strasbourg, Toulouse, Clermont-Auvergne ), and institutes ( INP-Toulouse, Mines Telecom, VetAgroSup, IPGP ). In the southern countries, the following universities- UCAM, TREMA International Joint Laboratory, Morocco; lNRGREF, INAT, Tunisia ;  Univ.
Abdou Moumouni, Niger ;  Univ. Abomey-Calavi, Bénin ;  Univ. des Sciences des Techniques et des Technologies de Bamako, Mali ;  UFAM in Manaus, Brazil ;  UFF in Rio de Janeiro, Brazil ;  UNALM in Lima, Peru; UMSA in La Paz, Bolivia ;  UCV in Caracas, Venezuela ;  UMNG in Brazaville, Republic of the Congo-as  well as the following national hydrological services-ANA, CPRM in Brazil, SENAMHI in Peru and Bolivia, INAMHI in Ecuador, and DREAL in France-are thanked for making international collaboration possible. The  French Ministry of Ecological and Inclusive Transition (AFB/ONEMA)  is supporting the piezometer network (ROSES). A number of sites were supported by the  ANR  and  PIA  ( Programme Investissement d' Avenir ):  Equipex CRITEX  ( ANR-11-EQPX-0011 ),  LabexOSUG@2020 ,  Labex DRIIHM-OHM du Haut Vicdessos.
Local weather scenarios can then be produced for any prediction day, conditional on the largescale atmospheric configuration observed or simulated for this day, where the "prediction day" refers here to some future, past, or present simulation day, depending on the application context at hand (e. g. , forecasting, simulation, reconstruction). Perfect prognosis SDMs include transfer functions, weather-type-based models, and methods based on atmospheric analogs. In the latter case, atmospheric analog days of the current prediction day are searched for on the basis of some atmospheric similarity criterion in the historical database. The weather variables observed for the most similar day, for one similar day chosen randomly, or for a selection of the k-most similar days are then used as a weather scenario for the prediction day of interest (e.
This makes the statistical downscaling link potentially adaptive; i. e. , the predictors and the regressions parameters are likely to vary from one day to the other. As mentioned above, SDMs are used for the simulation of local weather scenarios in different contexts, e. g. , local weather forecasts, reconstructions, or climate impact studies. No specific context is considered here and the two-stage model could be further considered for either forecasting, reconstruction, or future projections. Depending on its intended use, some specific issues would obviously apply, calling for specific focused analyses and developments. For in-stance, the large-scale atmospheric parameters to be considered as predictors would depend on the dataset considered (e. g. , atmospheric reanalyses, climate models, or numerical weather prediction models) as a result of their intrinsic quality (e.
Data The predictand is the daily small-scale precipitation estimated for the 1982-2001 period over 8981 grid cells of 8 × 8 km 2 covering the continental French territory. The predictand is "local" precipitation, i. e. , precipitation at a given grid cell. Each of the 8981 grid cells is thus considered in turn in the following independently of the other cells. In other words, the predictions do not target precipitation fields. Small-scale precipitation data are obtained from the SAFRAN analysis produced for several surface variables at an hourly time step by MeteoFrance  (Quintana-Segui et al. , 2008; Vidal et al. , 2010). SAFRAN precipitation estimates are obtained each day from the closest measurement stations. They are considered as pseudo-observations. Atmospheric predictors are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40,  Uppala et al.
β q K q ). The shape parameter ν of the gamma distribution is computed from the variance σ 2 of non-zero precipitation amounts estimated from Pearson's residuals  (McCullagh and Nelder, 1989)  as σ 2 = 1 N q -K q + 1 N q i=1 (q i -µ) 2 µ 2 , ( 4 ) where N q is the number of non-zero precipitation data q i considered in the analysis. As the shape parameter ν equals the inverse of the variance 1/σ 2 , the gamma distribution F Q modeling the precipitation amount thus follows a gamma distribution of this type: (ν, α = µ/ν). For any given prediction day, the estimation of both GLM models practically proceeds as follows. -The precipitation state (wet or dry), the precipitation amount, and the values of the different potential predictors are extracted for the N d nearest analogs of the day. The precipitation state of a given day is considered to be wet if the precipitation amount for this day is higher than or equal to 0.
They for instance likely depend on the database used for the large-scale atmospheric predictors. The day-today behavior of such an analog/regression approach (and the skill of the prediction) likely depends on the database and especially on the quality of the predictors. An atmospheric reanalysis with a higher spatial resolution would for instance likely allow for a better description of the shapes of geopotential fields and for a more relevant simulation of regional/local thermodynamic processes. It would likely lead in turn to higher-quality variables for some atmospheric parameters such as air instability. This may allow for a better identification of the daily specificity in the downscaling relationship and for the most informative predictors to be used each day. The reverse may occur when using lower-quality predictors, for instance lower-quality data from reanalyses available for the 20th century or lower-quality data from climate or numerical weather forecasting models.
Acknowledgements. The authors especially thank  Charles Obled  and  Isabella Zin  for fruitful discussions on the analog method. The authors also thank the  Grenoble University High Performance Computing centre, CIMENT  (https://ciment. ujf-grenoble. fr/wiki-pub/index. php/Welcome_to_the_CIMENT_site!), for their help and the large computing resource they provide. We would especially like to thank the associate editor and two anonymous reviewers for their relevant comments and suggestions that allowed us to improve this study and broaden its scope. Edited by: Luis Samaniego Reviewed by: two anonymous referees.
Many Archaean and Paleoproterozoic cratons show deformation patterns that differ from those observed in modern orogens. On the other hand, they constitute an important part of present-day emerged continents and contain a large part of continental mineral resources known to date. On the basis of a summary of structural data from some typical field examples and of results of analogue modelling, we emphasize that pop-down tectonics marked by vertical burial of supracrustals within an underlying hot and weak crust may be the most suitable model to account for deformation patterns of many ancient deformation zones. An overview of relationships between structural patterns and mineralisation in several ancient deformation zones further emphasizes that pop-down tectonics provides a very promising structural framework for mining exploration in Precambrian cratons.
Observational data The observational data used in this paper include images by the Meteosat Second Generation (MSG) (Geo-Stationary) Satellite (NERC Satellite Receiving Station, Dundee University, Scotland, http://www. sat. dundee. ac. uk/, last access: 17 March 2017). MSG carries the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, which has the capacity to observe the Earth in 12 spectral channels. Here, we present images from the mid-infrared/water vapour channel (5. 35-7. 15 µm) for 12:00 Z on 6 January 2017 and 12:00 Z on 7 January 2017. Radiosonde data in the form of skew-T log-P diagrams (taken from the Wyoming University, Department of Atmospheric Science, http://weather. uwyo. edu/ upperair/sounding. html last access: 27 April 2018) are used from four European stations: 1.
Overall, this process-oriented analysis and evaluation study indicates that the CAMS global and regional forecast modelling systems are able to capture the specific regional meteorological and air quality characteristics of a specific deep STT event over Europe in January 2017. It also highlights the importance of data assimilation in the CAMS global model as well as of the meteorological and chemical forcing in the CAMS regional forecast systems. Data availability. The CAMS global and regional forecast data were processed within the framework of the service element CAMS_84. Both CAMS global and regional forecast products can be obtained from https://atmosphere. copernicus. eu/ (last access: 19 October 2018). The satellite images were obtained from the NERC Satellite Receiving Station, Dundee University, Scotland, at http://www.
sat. dundee. ac. uk/ (last access: 17 March 2017). The radiosonde data were obtained from Wyoming University, Department of Atmospheric Science, at http://weather. uwyo. edu/upperair/ sounding. html (last access: 27 April 2018). The ozonesonde data were obtained from the World Ozone and Ultraviolet Radiation Data Center (WOUDC) at https://woudc. org/ (last access: 9 June 2017). The IAGOS aircraft ozone measurements were provided by Hannah Clark (Hannah. Clark@aero. obs-mip. fr) and will shortly be available in the database at http://www. iagos. fr (last access: 19 October 2018) or directly via the AERIS website at http://www. aeris-data. fr (last access: 19 October 2018). The Supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-15515-2018supplement Author contributions.
Acknowledgements. This work is performed within the framework of the service element "CAMS_84: Global and regional a posteriori validation, including focus on the  Arctic and Mediterranean areas" of the Copernicus Atmospheric Monitoring Services (CAMS). ECMWF is the operator of CAMS on behalf of the  European Union  (Delegation Agreement signed on 11 November 2014). The CAMS_84 work is financially supported by  ECMWF  via its main contractor,  Royal Netherlands Meteorological Institute KNMI. The authors acknowledge the strong support of the  European Commission, Airbus  and the  Airlines (Lufthansa, Air-France, Austrian, Air Namibia, Cathay Pacific, Iberia ,  China Airlines ,  Hawaiian Airlines  so far) who have carried the MOZAIC or IAGOS equipment and performed the maintenance since 1994.
In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS database is supported by  AERIS  ( CNES  and  INSU-CNRS ). Data are also available via the AERIS website at https://www. aeris-data. fr(last access:  19 October 2018 ). The authors acknowledge the use of Copernicus Atmosphere Monitoring Service Information (2017). We also acknowledge the  WOUDC , the  Department of Atmospheric Science of Wyoming University  and the  NERC Satellite Receiving Station of Dundee University  for the free use of ozonesonde data, radiosonde data and satellite images respectively.