dataesr/scientific-paragraphs-categorization huggingface dataset:
Model interface The Yelmo model interface is designed to be clear and simple but also flexible. In its essence, there are three main model functions: yelmo_init to initialize the model variables, yelmo_update to perform the ice-sheet model calculations for a given timestep and yelmo_end to terminate the Yelmo object (free it from memory). The first subroutine, yelmo_init, is used to load parameters, initialize variables in memory (i. e. , allocate arrays) and, optionally, to initialize the topographic state variables (ice thickness, masks, etc. ). No other variables are initialized here in the sense of being populated with data values, which is left to the user. An additional, optional helper function can be used, yelmo_init_state, which populates the remaining model variables in the material, thermodynamics and dynamics components.
The last subroutine, yelmo_end, simply removes the Yelmo object from memory (i. e. , all domain variables are deallocated). After calling yelmo_end, it is possible to reinitialize the Yelmo object via yelmo_init, for example, in order to test a different grid resolution or other configuration. There are several input/output routines defined for Yelmo. yelmo_write_init can be used to initialize a NetCDF model output file with the axes of model dimensions defined from the Yelmo object and writing of static fields like domain masks. The writing of model output for individual timesteps is left to the user to maintain flexibility, as most programs require specific fields to be written (examples can be found in the test programs included with the code -see further below). In addition, yelmo_restart_write will create a NetCDF file and write all Yelmo fields as a snapshot, which can be used to restart the model (loading of a restart file can be activated with parameter choices).
, 2011). We set z 1 = 250 m everywhere and z 0 = -2000 m for West Antarctic Ice Sheet (WAIS) regions feeding the Ronne ice shelf and z 1 = -200 m elsewhere. Friction was scaled by the grounded fraction at the grounding line, but no additional scaling is applied. The enhancement factor parameters set for these simulations were E shr = 2. 5, E strm = 0. 7 and E shlf = 0. 5. The bedrock topography and initial ice thickness were prescribed from the RTOPO2. 1 dataset  (Schaffer et al. , 2016) , after which the model ran for 50 kyr, reaching a steady-state modeled ice distribution. For the simulation of the present-day state, surface mass balance (SMB) and surface temperature boundary fields were prescribed from a RACMO2. 3 simulation driven by ERA-Interim data and averaged over  1981 -2010 (van Wessem et al.
Acknowledgements. We would like to thank  Mahé Perrette ,  Christophe Dumas ,  Gunter Leguy  and  Bill Lipscomb  for valuable discussions about model design that improved Yelmo,  Akira Nishida  for help with Lis, and  Ilaria Tabone  and  Javier Blasco  for extensive model testing at intermediate development points. We are also grateful to the reviewers for helpful comments. Financial support. This research has been supported by the  Spanish Ministry of Science and Innovation project RIMA  (grant no. CGL2017-85975-R ). Alexander Robinson  was funded by the  Ramón y Cajal Programme of the Spanish Ministry for Science, Innovation and Universities  (grant no. RYC-2016-20587 ). Heiko Goelzer has received funding from the program of the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Dutch Ministry of Education, Culture and Science (OCW)  (grant no.
Code availability. Yelmo is maintained as a git repository hosted at https://github. com/palma-ice/yelmo (last access: 2 May 2020; palma-ice, 2020) under the license GPL-3. 0. Model documentation can be found at https://palma-ice. github. io/yelmo-docs/ (last access: 2 May 2020; Yelmo-docs, 2020). The exact version of the model, along with the necessary input data, used to produce the results used in this paper is archived on Zenodo (https://doi. org/10. 5281/zenodo. 3782650, Robinson et al. , 2020)  and has been tagged in the repository as v1. 02.
The PTPlot tool To allow the reader to explore the GW power spectra in light of the preceding discussion in specific scenarios, we have developed a web-based tool 'PTPlot' to accompany this paper. It may be accessed at ptplot. org. This tool is written in Python using the Django framework, performing server-side rendering of power spectra and SNR contour plots. The aim is to make high-quality, up-to-date and reliable predictions of the GW power spectra easily available to the model-building community. In time, it may be extended to include other cosmological sources of GWs, and other GW detectors. PTPlot allows the user to display GW power spectra for any thermal PT parameters (α, β/H * , g * , T * , v w ) of their choice, using the formulae presented here, with a comparison to the public sensitivity curve for LISA given in the Science Requirements Document  [65].
The PTPlot tool uses (29) (shading the corresponding regions grey) and (32) that, as discussed before, only take into account the contribution from sound waves. It is anticipated that the sensitivity curve (and SNR contours) will be updated if, in the future, an improved public estimate of the eventual LISA sensitivity curve is made available. The site carries a database of benchmark points for all the theories considered in Sec. 6 below, and the power spectra can be viewed for these as well.
The LISA SNR curves are computed for T * = 50 GeV (corresponding approximately to the values of T * for the 2HDM benchmarks yielding the strongest transitions, see e. g. [100] ) and assuming a bubble wall velocity of 21  v w = 0. 7. The blue and orange points in the figure correspond to 2HDM benchmarks for which different LHC constraints apply, as discussed below. We stress that allowing for a slight departure from our assumptions about the 2HDM parameters (SM-like Higgs h, m H ± m A 0 and M 2 m 2 H 0 ) within the experimentally allowed region would result in a small decorrelation between α and β/H as compared to the very strong correlation shown in Fig. 5  (essentially, a minor "thickening" of the line traced out by the 2HDM benchmark data in Fig. 5 ). We also note that in the 2HDM one tends to find larger values of β/H for a given α as compared to other models (e.
Such limits are already very stringent. If they become stronger, these benchmark points would most likely be ruled out before LISA flies. A further step into non-minimality consists of introducing custodial triplets. As a result of the custodial symmetry, these fields can acquire large VEVs without spoiling EW precision tests  [149, 150]. As a consequence, the structure of the scalar potential can differ significantly from that of the MSSM, and a tree-level barrier between the different phases could exist. Very strong PTs with ultra-relativistic bubble velocities can then occur  [151]. Even if light, the triplets can evade current LHC bounds as well as relax some of the tension between light stops and LHC data  [152, 153]. The benchmark scenario SUSY 4 in Table  2  corresponds to this setup  [151].
Acknowledgements MC was funded by the  Royal Society  under the  Newton International Fellowship program. GD would like to thank  CNPq (Brazil)  for financial support. MH was supported by the  Science and Technology Facilities Council  (grant number  ST/P000819/1 ), and the  Academy of Finland  (grant number  286769 ). SJH was supported by the  Science and Technology Facilities Council  (grant number  ST/P000819/1 ). The work of JK was supported by  Department of Energy (DOE)  grant  DE-SC0019195  and  NSF  grant  PHY-1719642. TK and GS are funded by the  Deutsche Forschungsgemeinschaft  under  Germany's Excellence Strategy -EXC 2121 "Quantum Universe" -390833306. JMN is supported by  Ramón y Cajal  Fellowship contract  RYC-2017-22986 , and also acknowledges support from the  Spanish MINECO's "Centro de Excelencia Severo Ochoa" Programme  under grant  SEV-2016-0597 , from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie grant  agreements  690575  ( RISE InvisiblesPlus ) and 674896 (ITN ELUSIVES) and from the  Spanish Proyectos de I+D de Generación de Conocimiento  via grant  PGC2018-096646-A-I00.
KR is funded by the  Academy of Finland  grants  308791 ,  319066  and  320123. PS is supported by the  Cluster of Excellence "Precision Physics, Fundamental Interactions, and Structure of Matter"  ( PRISMA+ EXC 2118/1 ) funded by the  German Research Foundation(DFG). PS would like to thank  Moritz Breitbach  and  Eric Madge  for providing the data for Figures (14), ( 15 ) and (  16 ), and  Eric Madge  for checking the ptplot code. DJW ( ORCID ID 0000-0001-6986-0517 ) was supported by an  Science and Technology Facilities Council   Ernest Rutherford Fellowship , grant no. ST/R003904/1 , by the  Research Funds of the University of Helsinki , and by the  Academy of Finland , grant nos. 286769 ,  324882  and  328958.
At the same time more accurate and up-to-date input databases for vegetation, soil texture and orography were used and finally in mountainous areas a subgrid representation of the orography using elevation bands was adopted as well 25 as the possibility to add a reservoir to represent the effect of aquifers in mountains. SIM model was run for a 60y period from 1958 to 2018. The present paper describes the changes and demonstrates that the SIM new version performs better than the previous one by comparing a set of selected simulations to measurements of daily streamflow and observations of snow depths.
Although this method has proven in the last decades to be suitable to tackle scientific questions related to water resource, a revision towards a more physical method, based on the diffusion of heat and moisture in the soil  (Decharme et al. , 2011) , was undertaken to account for more sophisticated numerical schemes to enhance the system performances. 65 The goal of the current paper is to show how the development of new parameterizations has improved the system performances. The current study, based on a 60y long time series covering 1958-2018, shows how improvements in atmospheric forcing, land surface model physics and subgrid orography and hydrology impact and enhance these results. It also aims to describe how results are impacted by each set of modification and finally to demonstrate the new model configuration performs better than 70 the previous one in terms of discharge extremes, when comparing model results to independent observed data such as snow depth or river discharges.
Section 2 describes the original SIM configuration and its recent updates. Section 3 presents climate data and evaluation datasets, and the offline experiments used to demonstrate the advantages of the new SIM system. In section 4 the results of the new system are presented and finally they are discussed in 75 the last section. The SIM hydrometeorological model  (Habets et al. , 2008)  combines the three SAFRAN, ISBA and MODCOU models. SAFRAN (Durand et al. , 1993; Quintana Segui et al. , 2008)  performs a 6-hourly 80 analysis of near-surface meteorological variables such as 2-meter air temperature and relative humidity, 24 h precipitation, wind speed, cloudiness and a daily analysis of 24 h accumulated precipitation. The analysis is performed on geographic areas of a few hundreds of square-kilometres and the analysed fields are interpolated at an hourly timestep and direct and diffuse solar radiation as well as infrared radiation are computed from cloudiness, temperature and humidity profiles analysis using a radiation 85 model  (Ritter and Geleyn, 1992).
Use of more accurate land surface parameters In addition to the changes in the model physics described previously, the databases of soil texture, vegetation coverage and topography were upgraded to improve the realism of external parameters in the ISBA model. Only the hydrogeologic database representing aquifer and the routing network are 165 unchanged. Soil texture database in France was a soil map provided by the Institut National de Recherches Agronomiques (INRA -  King et al. , 1995)  at a 1 km resolution. The update here consists in using the Harmonized World Soil Database (HWSD -  Nachtergaele et al. , 2012)  which is a soil map at 1 km resolution that combines several datasets available worldwide. In particular over France the above-mentioned INRA soil map was integrated in the HWSD dataset (used in other applications using 170 SURFEX outside France), therefore, this change does not affect the SIM simulations.
The global ECOCLIMAP1  (Masson et al. , 2002)  at 1 km resolution was used originally in SIM application over France. Then a new classification algorithm was developed over Europe to build the ECOCLIMAP2 land cover map  (Faroux et al. , 2013) , in order to use more accurate and recent satellite information as inputs and for a longer time period. Among various differences, ECOCLIMAP1 185 was for instance using 1992-1993 AVHRR satellite data whereas ECOCLIMAP2 is using 1999-2005 SPOT/VEGETATION data. The impacts of changing the vegetation fraction as input to the ISBA model are manifold and won't be described here (see  Faroux et al. (2013)  for a detailed comparison), but using ECOCLIMAP2 makes the user more confident on the ability of such a database to capture the surface heterogeneities even if when performing numerical simulations with the ISBA model for long 190 time periods, the land cover varies in time and can obviously be different from the ones from 1999-2005 corresponding to the classification.
245 In this study the SIM system is an offline application where the ISBA land surface model is driven by climate data and no feedback from the surface to the atmosphere exists. Various configurations of SIM have been designed to highlight the improvements that have been achieved, each simulation being spun up for two years. The first configuration refers to the old SIM system (SIM_REF hereafter, as described in 2. 1), i. e. 250 before any of the changes previously described. An additional reference simulation (SIM_REF2 hereafter) is based on SIM_REF where subgrid drainage is removed. The first experiment (SIM_PHY hereafter) consists in changing the physics and the input databases. SIM_PHY uses the ISBA diffusion scheme with 14 layers in the soil, the updated snow scheme with 12 snow layers, a tile approach based on 12 vegetation types, and a runoff parameterization where the high constraint on the b coefficient 255 (b=0.
5 in the runoff parameterization in SIM_REF) was lowered to 0. 25. In SIM_PHY also, the updated databases are used for a better representation of soil texture, orography, and vegetation. The SAFRAN infrared radiation correction with cloudiness is then introduced in experiment SIM_FRC (based on SIM_PHY). Then, SIM_TOP (based on SIM_FRC) uses the subgrid orography representation in mountains, and finally SIM_NEW (based on SIM_TOP) accounts for a drainage reservoir in mountains. 260 Table  1  summarizes the main characteristics of the experiments. In the SIM system, climate data is provided by the SAFRAN analysis. In the present study, SAFRAN covers a period of 60 years from 1 st August 1958 to 31 st July 2018. In SAFRAN, the guess comes from ERA-40 or ERA-Interim  (Dee et al. , 2011). The analysed variables are then interpolated hourly into the SIM grid at 8 km resolution and this full set of near-surface variables is then used to drive offline 265 simulations.
The Nash Sutcliff Efficiency (NSE, Nash and Sutcliff, 1970) was used to evaluate the model performance, and in addition the discharge ratio between the SIM simulations and the observations was calculated to assess the bias of the system. The complementary cumulative distribution function (CCDF hereafter) of 290 NSE that calculates the probability of the NSE to be greater than a threshold, averaged over the number of gauging stations in France, is also used as a metric to assess NSE. Snow depth is another independent dataset (i. e. not assimilated in the reanalysis process) used to evaluate the system. Measurements are provided by 185 stations in the Alps, the Pyrenees, and Corsica at elevations ranging between 600 and 3000 m a. s. l. They include 26 ultrasonic sensors mainly in high 295 elevation areas (Nivose network) and 161 stations operated by Meteo-France partners mainly in ski resorts and providing manual measurements on snow stakes.
However, in ERA-Interim, the data 320 assimilation system evolved substantially and changed to a 4D-variational method as compared to the 3D-variational method previously used in ERA-40. This new system appeared to be more accurate and the assimilation of a lot more satellite observations has significantly improved the analysis and the forecast. In particular the vertical profiles of temperature and relative humidity were improved, and they are used as input of the cloudiness analysis for the computation of the solar ad infrared radiation. Table  325  2 summarizes the comparison in terms of bias and root mean square error (RMSE) at the four weather stations measuring IR radiation. Except for Carpentras station where LSAF IR radiation is almost unbiased and the error the lowest as compared to SAFRAN, scores are better for stations in altitude with SAFRAN when the correction is applied.
56, corresponding to half of the total number of stations, and highlighting the added value of the physics associated with better description of vegetation types and other more accurate databases. SIM_PHY exhibits slightly worse results for Nash ranging from 0. 5 to 0. (about 40 % of the stations) but in this case the two models do not perform very well. 345 https://doi. org/10. 5194/gmd-2020-31 Preprint. Discussion started: 2 March 2020 c Author(s) 2020. CC BY 4. 0 License. Figure  8  shows how scores are improved for the experiments with corrected infrared radiation (SIM_FRC), subgrid orography (SIM_TOP) and hydrology (SIM_NEW), both in terms of CCDF of NSE and discharge ratio. The bias in river discharge is significantly reduced when increasing infrared radiation due to a higher total evaporation leading to less water available in the rivers.
(2008)  for another time period, except that in SIM_NEW the Landes forest (Southwestern France at the Atlantic coast) exhibits a higher Bowen ratio. The first reason comes from the difference in the photosynthesis parameterization and more precisely the parameterization of the leaf conductance used in SIM_REF based on  Jarvis (1976)  and SIM_NEW based on Calvet (1998) ISBA-A-gs module that accounts for plant stress and reduces substantially the 435 evaporation over vegetated areas. Thus, the surface energy balance tends to increase the sensible heat flux. The second reason is linked to the increase in infrared incoming radiation that strengthens sensible heat flux and diminishes the latent heat flux, which usually occurs on dry soils with a low evaporative capacity. Interannual variability of the ratio of the evaporation to precipitation (E/P hereafter) and the Bowen ratio are shown in Fig.
485 This study illustrates how the developments performed these last ten years improve the hydrometeorological system SIM. Several important changes have been done, in particular in the ISBA soil physics where the Force-restore method has been abandoned and replaced by the soil multi-layer diffusive method. In the same time the snow model was revised to get closer to Crocus model as described in Sect. 2. The model was run based on the tiling approach for vegetation, each of the 12 490 vegetation types being characterized by their own set of parameters, opposite to the one vegetation type approach where parameters are aggregated. Then more accurate databases of soil, orography, and land use were used. A more accurate infrared forcing has allowed to significantly improve the results as well as the use of a groundwater reservoir in mountains associated to a specific vertical discretization of massifs.
Then, accounting for anthropisation, especially irrigation and the presence of dams could benefit the SIM system to improve its realism and allow fairer comparisons to gauging stations in anthropized basins. Irrigation is currently being developed in the ISBA model and the integration of dams is a longer-term project. Finally, better representing groundwater and their characteristics in France is 515 another challenge that needs also to be addressed. Code availability. The SURFEX v8. 0 source code, including the ISBA code, used in this study is available in the supplement, as well as the SAFRAN code. The post-processing codes, including the scores package of the snowtools opensource project, are also available in the supplement. 520 Data availability. The outputs from all models discussed here and the R and Python programs to plot the results are available in the supplement.
https://doi. org/10. 5194/gmd-2020-31 Preprint. Discussion started: 2 March 2020 c Author(s) 2020. CC BY 4. 0 License. 3. 2 Validation data sets and tools Various datasets were used to assess the SIM model performances along the validation process, to 270 ensure that an improvement in climate input data or physics was improving at the same time surface or soil variables and rivers discharges. The Land Surface Analysis Satellite Applications Facility (LSAF) project disseminates Meteosat Second Generation geostationary satellite products, and in particular infrared downwelling radiation (LSA SAF; Trigo et al. , 2011; http://lsa-saf. eumetsat. int ). LSAF Data covering the period 1 st August 275 2010 and 31 st July 2015 are considered to evaluate the quality of the SAFRAN infrared radiation. In addition to Infrared radiation data of Carpentras and Col de Porte already mentioned in Sect.
The supplement related to this article is available online at http://doi. org/10. 5281/zenodo. 3689560. Author contribution. Model developments were performed by PLM and EM for the SAFRAN analysis 525 over France, SF for SURFEX and BD for the diffusive version of SURFEX/ISBA, and FH for MODCOU. PLM and FB designed the experiments and carried them out. ML performed the comparison of snow results. DL provided valuable Python scripts for figures. JB and ML were first testing the model for their own research. PE, FB and FR are responsible for the operational SIM suite at Meteo-France. PLM prepared the manuscript with help of all co-authors. 530 https://doi. org/10. 5194/gmd-2020-31 Preprint. Discussion started: 2 March 2020 c Author(s) 2020. CC BY 4. 0 License.
Supplement. The supplement related to this article is available online at http://doi. org/10. 5281/zenodo. 3689560. Author contribution. Model developments were performed by PLM and EM for the SAFRAN analysis 525 over France, SF for SURFEX and BD for the diffusive version of SURFEX/ISBA, and FH for MODCOU. PLM and FB designed the experiments and carried them out. ML performed the comparison of snow results. DL provided valuable Python scripts for figures. JB and ML were first testing the model for their own research. PE, FB and FR are responsible for the operational SIM suite at Meteo-France. PLM prepared the manuscript with help of all co-authors. 530 https://doi. org/10. 5194/gmd-2020-31 Preprint. Discussion started: 2 March 2020 c Author(s) 2020. CC BY 4. 0 License.
ARIMA Recently, Time Series Analysis (TSA) becomes very useful in the prediction field as it is applied in several domains such as weather forecasting, economics, engineering, environment, medicine, etc. [8]. In weather forecasting, TSA helps in predicting future weather situations, by collecting historical data within regular time intervals  [14]   [15]. Known as series, this data is collected at specific intervals (hourly, daily, weekly, monthly, yearly, etc. ). TSA offers certain advantages in terms of trusted results in weather forecasting and efficiency in detection both trends and seasonality within the series  [8]. The ARIMA model follows a linear expression given by the following equation: 0 1 1 1 1 t t p t p t t q t q y y y φ φ ε ε ε - - - - = Θ + + ⋅⋅⋅ + + -Θ -⋅⋅⋅ -Θ where: y t : is the observed value.