dataesr/scientific-paragraphs-categorization huggingface dataset:
In this case, the stress field is resolved but small stress variations would not be revealed. We produced a simple synthetic model m as the strike slip regime with σ 1 alternatively heading to N 000 • and N 045 • for each adjacent node ij. Then, we ran two inversion tests with (1) no damping (e = 0) and (  2 ) the optimal e-value of damping chosen at the lower left corner of the trade-off curve, which yields the best compromise between misfit and model variances (Figure  2A ). Stress tensors are calculated if at least 8 focal mechanisms are contained per cell for both tests. For inversion with no damping, the results show that the stress field m issued from the inversion of the synthetic data gave a good reproduction of the synthetic stress field m in terms of stress orientations in all cells (Figure  3 ).
Error propagation tests To evaluate the stability of stress inversion in each cell, we generated a data vector that includes random noise and inverted this to evaluate the propagation of the data errors. This experiment is similar to the "Monte Carlo" tests commonly used in seismic tomography. To carry out error propagation tests with the damped stress field models determined in section Stress Field Results, we first added random perturbations on the strike, dip and rake of our dataset in the range of the uncertainties of the fault plane solution (±15 • , section The Earthquake Catalog Used for Inversion), following a normal distribution (Figure  7D ). We then inverted the noisy data and finally evaluated the difference between the noisy models and the optimum damped model (e = 0. 6) determined in section Stress Field Results.
Stress analysis To detail the state of stress, we used another approach to the one that served for the regional stress inversion. A random stress search from the Fsa package  (Célérier, 2013 , see section Random Stress Search) allowed interactive detection of the mechanically homogenous data subset. A first stress inversion was applied to the 184 focal mechanisms. It yielded a reduced stress tensor which barely explains all the data. A second stress inversion was run but this time 30% of data were randomly selected and used for inversion to detect different datasets that are mechanically consistent. The resulting solution shows a reduced stress tensor with σ 3 orientation of N065 • associated with a plunge axis of 22 • and σ 1 orientation of N169 • associated with a plunge axis of 30 • ( = 0.
, 2002; Bergerat and Plateaux, 2012)  and for paleo-stress fields  (Bergerat et al. , 1990; Villemin et al. , 1994; Guðmundsson et al. , 1996; Bergerat and Angelier, 1998; Plateaux et al. , 2012). Local conditions such as the uniaxial extension (σ 1 = σ 2 > σ 3 ), fluid transport and dyking can effectively affect the stress field locally. However, with the database and the method used here the question arises as to whether this permutation reflects oblique mechanisms (i. e. , oblique slips) or rather two kinds of pure mechanisms. To answer this question, we selected the cell with the greatest amount of data. One hundred and four focal mechanisms are located around Bárðarbunga volcanoes as shown in Figure  4  at node 15-16 (stress tensor solution n • 48 in Table  1 ) as a representative cell.
ACKNOWLEDGMENTS We thank  Bergþóra S. Þorbjarnardóttir  for help in providing the earthquakes catalog from  IMO. We are also grateful to  Bernard Célérier  for fruitful discussions. Thanks to  Marie Keiding  for providing comments on the manuscript. We also would like to thank the editor  Valerio Acocella  and three anonymous reviewers for improving the manuscript. SATSI code ( Spatial And Temporal Stress Inversion ) was used for inverting data and was downloaded from  USGS  website (http://earthquake. usgs. gov/research/software/). Figures were made using GMT, MATLAB and ParaView.
The METAFOR controlled vocabulary Given that the metadata have to address all stages of the modelling process and given that they should serve data discovery and access tools, the prime objective of the METAFOR project was to design a conceptual metadata scheme and develop the associated hosting structure, the Common Information Model (CIM). The CIM defines objects, classes, and their relationships  (Lawrence et al. , 2012). Through specialized UML (Unified Modelling Language, www. uml. org) packages, the CIM addresses the description of the constituent elements of climate modelling: the "activity" package includes the experimental context and simulations; the "software" package covers the climate model itself; the final data objects produced by simulations and their inputs are described by the "data" package and the numerical grids of the models by the "grid" package; finally, a "shared" package of reusable elements supports some "orphan" classes, such as quality control records and platform descriptions.
The discussions were captured using mindmaps (Freemind software, http://freemind. sourceforge. net/wiki/index. php/Main_ Page), one for each realm, which proved to be very appropriate for capturing structured information and feedback on the fly. The interviewing and reviewing procedure is illustrated in Fig. 1 : following a first-round interview with one realm expert (step 1), revision processes were launched with other scientists from other research centres (step 5). We integrated the feedback in a structured way (steps 2, 3), capturing their precise meaning, getting confirmation when necessary, working out possible conflicting views (step 6), and taking care not to introduce inconsistencies with previously collected CV. Following this consultation process, several iterations led to a consensus among the modellers interviewed.
The CV forms a semantic database (the possible content) for building a metadata instance (an actual content recorded as a CIM document) for a given model and related simulation. A suite of tools were developed to exploit this semantic database in an automatic way so as to feed downstream tools such as the CMIP5 Questionnaire (see Sect. 5. 1). To that end, coding rules were added to the mindmaps. We defined a set of formal typographic rules (e. g. different font formats and icons) to distinguish the different types of CV containers and the different types of choice (exclusive or not) among possible values for parameters or to define the type of expected value (numeric or string). These rules are illustrated in Fig. 2  and detailed in the legend of this figure. A definition of parameters is provided (as attached note, not shown in Fig.
Possible choices are "via standard configuration", "via model modifications", "via inputs", "via combination", "not applicable" or "not conformant". Its main function is to enforce a conformance check by metadata providers. The Experiment CV-XML documents containing the specific CMIP5 experiment and simulation CV 3  were fixed once and for all and cannot be modified by the climate modellers; they are ready for ingestion into the CMIP5 Questionnaire (see next section) and conform to the CIM activity package class structure. In this CV, experiments are identified by a label, a title, a description and an associated list of requirements. Taking the pre-industrial control experiment as an example (see Fig. 4 ), 3. 1_pi-Control stands for the experiment label; Pre-Industrial Control: control experiment against which perturbations are compared for the experiment title and Pre-Industrial coupled atmosphere/ocean control run.
Creating instances of CMIP5 metadata To collect metadata for CMIP5 numerical models, simulations and experiments, METAFOR has constructed what was initially intended to be a "simple questionnaire". However, it rapidly became clear that a traditional questionnaire based on a linear collection of information would be completely inappropriate for the task, given the amount of information to be collected and given that much of this information would have to be shared and compared, for instance across two simulation descriptions. Moreover, a simple, linear text based questionnaire would have required a huge effort of "by hand" treatment in order to translate information harvested into CIM-instances that ultimately feed the CMIP5 metadata database (see Sect. 5. 2 for details on information workflow).
The information pipeline It is clear that the METAFOR CV has been built with the intent to go beyond simple vocabulary collection usage. Indeed, it is targeted at automatic ingestion by downstream tools (the CMIP5 Questionnaire -discussed in Sect. 5. 1) and for inclusion into OWL (Web Ontology Language) ontologies, e. g. as used in the ESG/CURATOR portal then in use. The Experiment and Simulation CV were fixed by the CMIP5 protocol and are not likely to evolve in the CMIP5 time frame (hence it was created and stored directly in XML without extra tooling). The CV built for model description is, on the other hand, intentionally managed in a different way (i. e. in mindmaps, see Sect. 4), independently from the software tools using them. The objective is to ensure separation of concerns between building and usage so that the semantic database (the Model CV) and the tools using them (the questionnaire) or hosting them (the CIM) can evolve on their own timeline.
However, the mindmap format cannot directly feed these downstream tools: format conversion into a machinereadable format was required. To that end, we developed the software to support the information pipeline illustrated in Fig. 9. This tool chain can be found on the METAFOR SVN repository at http://metaforclimate. eu/trac. To satisfy CMIP5 Questionnaire needs, a simple XML-CV structure was defined to encode the Model CV based on the mindmap rules and constraints described earlier. A mindmap validator (top-left grey box in Fig. 9 ), written in XSLT and invoked by Python, was implemented to check that a specific mindmap (top-right red box in Fig. 9 ) conforms to the defined encoding rules (see Sect. 4. 1). If a feature in the mindmap missed a rule (e. g. an element coded as leaf parameter having a child element) the person responsible for the CV mindmap is asked to make appropriate corrections.
9 ). This ontology was  also used to guide the mapping tool which allowed the conversion of CIM documents into gateway RDF (Resource Description Framework, http://www. w3. org/TR/rdf-mt/) triple stores  (Lawrence et al. , 2012). The conversion of Model CV into OWL was then the decisive step for the final adoption of METAFOR CV as CMIP5 metadata for models and simulations (bottom-right yellow box in Fig. 9 ). Finally, CIM-compliant documents, conforming to the CMIP5 DRS, were broadcast as "atom feeds", and the corresponding metadata were ready to be included in the CMIP5 metadata catalogue deployed on the ESG portal. Since the original tool chain was developed, a new tool chain has been deployed. The CIM-compliant XML documents are now stored in a database, and extracted and displayed in client portals via JavaScript code which loads the documents across the net, and then displays them.
Summary and further work CMIP5 was conducted by 20 modelling groups that produced about 90 000 years of simulation for a total volume of several petabytes. A CMIP5 climate data user is faced with a large amount and large diversity of data sets archived in CMIP5 data-node centres. In this context, the METAFOR mission was to provide a metadata system to support data preservation, data reuse (both in time and by different research communities), data readability and discovery, and to guarantee the data quality (or conformity). Until now, such an integrated metadata system for climate modelling was missing. The controlled vocabulary for model and simulation should be considered as necessary raw material for such a system. This paper introduced the controlled vocabulary developed both for generic description of earth system models and as input for the tool developed to collect this description for CMIP5 models and simulations (the CMIP5 Questionnaire).
The mindmap technology used facilitated the CV development, ensuring a wide engagement of the scientific community in this process, hiding away the complexity of the underlying ontological concepts (the CIM). The metadata pipeline, which starts from the mindmaps, serves both the metadata entry tool (the CMIP5 Questionnaire) and metadata catalogues such as the ESGF gateways. The cornerstone of the METAFOR CV has indisputably been the engagement of a large number of modellers from the climate community since the early stage of the CV elaboration process. The CV collection produced at the end of the METAFOR project gathers thousands of terms for which hierarchical arrangement is equally important as the terms themselves. Even if it can be improved further, METAFOR CV is the first one to address the whole climate modelling chain.
Available in CMIP5 metadata catalogues and supporting data discovery tools, the hope is to provide essential services to climate data users. There are two significant pieces of work yet to be done before the CV can be easily governed and maintained. Firstly, a conversion tool taking the CV XML back to the mindmap format would support the ability to convert between all CV formats. This tool would allow use of the CV XML as the primary preservation and governance artefact, generating mindmaps from those XML instances for websites and human-mediated discussions for example. Secondly, we need to formalize an http interface for the CV following appropriate standards (see  Leadbetter et al. , 2011). Secondly, the maintenance and governance of the controlled vocabulary and of the associated metadata pipeline needs addressing.
Gathering feedback from the questionnaire users and finding ways to benefit from this feedback to make the CV evolve should also be strongly considered. The intended focus is the set-up of a real standard, which requires a governance committee to emerge (as planned in the framework of EU-FP7 ENES2 project). For now, the METAFOR work is extended within the UK JISC-funded PIMMS project (Portable Infrastructure for the METAFOR Metadata System). Although populating the CMIP5 Questionnaire was not mandatory, but highly recommended by the CMIP5 panel (i. e. not blocking for CMIP5 model outputs publication), about 70 % of the modelling groups contributing to CMIP5 provided metadata through the questionnaire and more than a thousand CIM documents are stored in the CMIP5 documentation repository by this way.
An overall 78 % of the published documents are attached to the description of experiments and simulations and 12 % describe the models and their grids. Only 2 groups provided a description for their model but not for the simulations they performed, and 8 groups did not provide any metadata at all. Further diagnostics to measure quality and completion rate of the metadata documents would be advisable. A key point for a wider acceptance of the metadata harvesting procedure (the questionnaire and the underlying CIM) is certainly to limit the effort asked to metadata providers. With the CMIP5 Questionnaire, the effort required from the provider was indisputably too strong. The logic of the information flow and connections between formal concepts was viewed as somewhat complex. Lessons are to be learned from the METAFOR experience in the context of CMIP5 that should be reinvested in future projects.
While the application of CMIP5 has dominated most of the development thus far, next generations of the questionnaire are currently being developed by the ES-DOC community (Earth System Documentation, http:// earthsystemcog. org/projects/es-doc-models/). Initiated during the METAFOR project, specific CV is being developed to describe the models and simulations used in the EN-SEMBLES EU project (http://www. ensembles-eu. org). The US NCPP project (National Climate Predictions and Projections, http://earthsystemcog. org/projects/ncpp/) and EU EURO-CORDEX (Coordinated Downscaling Experiment -European Domain, http://www. euro-cordex. net) are also agreeing on statistical and dynamical downscaling CV for regional climate studies. Finally, one can expect that the METAFOR CV for global climate models will be reused in upcoming or recent EU FP7 initiatives dedicated to climate services as the SPECS project (Seasonal-to-decadal climate Prediction for the improvement of European Climate).
Acknowledgements. METAFOR was funded by the EU 7th Framework Programme  as an e-infrastructure (project # 211753 ). The support of the  EU   FP7 IS-ENES  (project # 228203 ) is also acknowledged. This work benefited significantly from the engagement of other METAFOR members and colleagues from the  US Earth System Curator  project. We also appreciated guidance from the METAFOR advisory committee, in particular  Wilco Hazeleger  and  Karl Taylor. Edited by:  M. Kawamiya.
Climate models predict Antarctic precipitation to increase during the 21st century, but their present day Antarctic precipitation differs. A fully model-independent climatology of the Antarctic precipitation characteristics, such as snowfall rates and frequency, is needed to assess the models, but was not available so far. Satellite observation of precipitation by active spaceborne sensors has been possible in the polar regions since the launch of CloudSat in 2006. Here we use CloudSat products to build the first multiyear model-independent climatology of Antarctic precipitation. The mean snowfall rate from August 2006 to April 2011 is 171 mm yr -1 over the Antarctic ice sheet north of 82 • S. The ECMWF ERA Interim dataset agrees well with the new satellite climatology.
Data and methods The CPR, onboard CloudSat, is a nadir-looking radar at 94 GHz which measures the power backscattered by hydrometeors according to the distance from the sensor. It provides radar reflectivity profiles divided into 150 bins at a vertical resolution of 240 m, with a 1. 7 km × 1. 3 km footprint, and up to 82 • of latitude. Its minimum detectable radar reflectivity is around -28 dBZ. In this study, two CloudSat products are used to determine characteristics of Antarctic precipitation. The first product, 2C-PRECIP-COLUMN  (Haynes et al. , 2009) , is used to assess the phase and occurence frequency of Antarctic precipitation. 2C-PRECIP-COLUMN provides a precipitation flag based on the near-surface reflectivity (dBZ) at the fourth bin over the ocean (between 600 and 840 m above the surface), and at the sixth bin over land (about 1300 m) to remove surface contamination (ground clutter).
To the extent they can be characterized, systematic errors are removed. Within the context of the retrieval algorithm the remaining uncertainties are considered to be unbiased and random; however, these likely consist of some combination of systematic and random uncertainties since, for example, the algorithm's a priori assumptions are not tuned to the particular characteristics of Antarctic snowfall. Thus, while climatological averaging of retrieved snowfall rates reduces the truly random component of the uncertainties, some indeterminate bias is likely also present, and its evaluation is an ongoing area of research. In this study, both CloudSat datasets are processed over a grid of 1 • of latitude by 2 and precipitation, but only one precipitation phase, this phase is retained. If the flags in the same grid cell indicate rain and mixed precipitation, mixed precipitation and snow, or rain and snow, mixed precipitation is retained.
Back Close Full A comparison between the snowfall rate observed by CloudSat and simulated by ERA Interim is shown in Fig. 6  and in Table  2. Over the Antarctic continent (latitude < 82 • S), the mean snowfall rate is 171 mm w. e. yr -1 for CloudSat and 163 mm w. e. yr -1 for ERA Interim. The ratio of the snowfall rate observed by CloudSat and predicted by ERA Interim is relatively homogeneous over the ice sheet, except over parts of the Peninsula, the Vinson massif, and the Prince Charles Mountains, where it is very high. Orographic precipitation could be seen by CloudSat, but not predicted by ERA Interim due to the difference in spatial resolution between both datasets. However, ground clutter should be stronger over mountainous areas than over flat terrain, and may induce a spuriously high snowfall rate.
Comparison of the CloudSat products to the accumulation rate assessed by Arthern et al. (2006) Table  2  and Fig. 7  show a comparison between the snowfall rate obtained by CloudSat for the period August 2006-April 2011, and the accumuation rate assessed by  Arthern et al. (2006)  for the period 1950-2000. Arthern et al. (2006)  used in-situ glaciological measurements to assess the accumulation, and passive radiometer data (AMSR-E) sensitive to snowpack characteristics for interpolating their results. Assuming that accumulation has not significantly changed during the last 50 yr  (Monaghan et al. , 2006; Frezzotti et al. , 2013) , the snowfall rate observed by CloudSat is higher than the accumulation over the periphery of the ice sheet, which is expected due to the negative contribution to accumulation of evaporation, melt, run-off, and blowing snow.
In the 2C-PRECIP-COLUMN dataset, a large number of flags are sorted as "precipitation possible". This leads to a considerable range of precipitation frequency, even if the frequency estimated is probably more reliable than the snowfall rate. Moreover, on the Antarctic plateau, 2C-PRECIP-COLUMN algorithm may have difficulties in distinguishing precipitating from non precipitating hydrometeors due to their small particle size. The reflectivity thresholds applied in this algorithm could be too high for this kind of precipitation. Figure  8   ors, and on the plateau, particles are probably too small to increase the near surface reflectivity above the threshold "precipitation certain". CloudSat is likely more accurate in peripheral areas than in the interior. Shallow precipitation (< 1300 m), missed by CloudSat, could be an important contribution to precipitation on the Antarctic plateau.
, 2009a). The lack of ground-based measurements prevents direct validation of CloudSat data. However, agreement between CloudSat data and ERA Interim reanalysis is encouraging for reliability of both datasets. This is consistent with the study of  Boening et al. (2012)  who have already found good agreement between CloudSat and ERA Interim for the snowfall rate in Antarctica (in the region 30 • W-60 • E, 65 • S-80 • S). Even if the spatiotemporal sampling of CloudSat is not really good (between 350 and 500 orbits per grid cell over the Antarctic periphery for the period August 2006-April 2011), the snowfall rate obtained with CloudSat is similar to the snowfall rate predicted by ERA Interim during the same period (Fig. 6 ). Therefore, the spatiotemporal sampling of Cloud-Sat seems to be sufficient to reproduce statistics of Antarctic precipitation for the period August 2006-April 2011.
Field observations and radiosoundings are performed in Dumont d'Urville and assimilated in ERA Interim. Particularly, humidity profiles obtained by radiosounding are used to predict precipitation in ERA Interim. Therefore, precipitation predicted by ERA Interim should be relatively reliable in Dumont d'Urville. Comparisons of the ERA Interim reanalysis data at Dumont D'Urville station against the precipitation flags from CloudSat were used to establish a precipitation rate thresh-Discussion Paper | Discussion Paper | Discussion Paper |old for comparing the datasets. A threshold of 0. 07 mm/6 h for the ERA Interim precipitation rates was found empirically to give good agreement with the CloudSat precipitation flags. From 2006 to 2011, 67 % of the time, the ERA Interim precipitation rates at Dumont D'Urville were below this rate.