dataesr/scientific-paragraphs-categorization huggingface dataset:
Hourly precipitation accumulations are based on gridded datasets merging radar and rain gauge observations over France (COMEPHORE), Germany (RADKLIM) and Switzerland (RdisaggH), and only on rain gauge observations over Italy (GRIPHO). Daily precipitation accumulations are based on the available hourly data from COMEPHORE, RADKLIM and GRIPHO over France, Germany and Italy. To extend the covered area, we also use the APGD-EURO4M daily dataset on the Pan-Alps domain. Over Switzerland, where RdisaggH is only available for a period of 8 years, we prefer the daily APGD-EURO4M with a longer period available. As COMEPHORE is the reference for our extreme precipitation evaluation study over the French Mediterranean in Sects. 3. 3 and 4, we propose a detailed description in appendix A.
This parameterisation implements additional prognostic atmospheric layers between the ground and the lowest level of the atmospheric model and makes it possible to explicitly calculate the 2 m temperature and 10 m wind. Physiographic data is based on the global ECOCLIMAP I database  (Masson et al. 2003)  at 1 km resolution. The CNRM-AROME41t1 version used in climate mode in this study is very close to the NWP version. The only model differences are: -the parameterisation of the turbulence fluxes above the sea, ECUME  (Belamari and Pirani 2007) , is replaced by the COARE3 scheme; -the relaxation zone where the atmosphere lateral boundary conditions are imposed is extended from 8 to 21 grid points to avoid losing information from the driving model when entering the CP-RCM domain. The main modifications between the cycle 38 used in Fumière et al.
(  2020 ) and the cycle 41 used in this study are the following: -a new database for orography with higher resolution: the GTOP030 with 1 km resolution (Survey 1993) is replaced by the GMTED2010 with 250 m resolution  (Carabajal et al. 2011 ); -a new version of SURFEX: from V7. 2 to V7. 3; -some minor modifications in the dynamics in order to improve the numerical cost; -two physical parameterisation modifications: the increase in the self-conversion threshold of primary ice crystals to snow and the addition of orographic shadowing and slope parameterisations  (Senkova et al. 2007 ); -the main modification is the implementation of the COMAD scheme  (Malardel and Ricard 2015). Indeed, in the vicinity of convective clouds, AROME had the general tendency to produce unrealistic divergent winds at the edges of the cold outflows generated by the precipitation evaporation.
Radiative forcing In addition to water vapour directly simulated by the model, the concentrations of five greenhouse gases are imposed: CO 2 , N 2 O, CH 4 , CFC11, CFC12 with one value per year and per species, homogeneous in the spatial dimension. The values come from observations before 2005 and from the RCP4. 5 scenario afterwards  (Moss et al. 2010 ). As the differences in greenhouse gases radiative impacts between observations  (Friedlingstein et al. 2019 ) and RCP4. 5 scenario are very small, the use of RPC4. 5 concentrations after 2005 is not expected to have significant impacts on the results. The  Nabat et al. (2013)  aerosols dataset, a mixture of remote sensing and model data, provides monthly evolutive two-dimension maps for dust, sea salt, organic carbon and black carbon. Due to an error in the preparation of the simulation, the sulfate forcing is missing in this simulation.
Common grids and interpolation method Model evaluation requires the choice of common grids where comparisons are performed. The first grid is the common regular 0. 0275° resolution ( ≈3km) ALP3 grid which is the evaluation target grid defined in the framework of the CORDEX FPS on Convection program (cf. Fig. 1 ). In addition, for a fair comparison between the 2. 5 km CP-RCM and the 12. 5 km RCM, and following  Hong and Kanamitsu (2014) , the comparisons must also be made on the coarsest resolution between compared datasets. The second grid is the pan-Alpine part of the common regular Euro-CORDEX Europe high-resolution grid, EUR-11i (0. 125°, i. e. around 12 km), called ALP11. All model and observational datasets are interpolated on these common grids. Among the different ways to interpolate horizontal fields, we select the cdo first order conservative remapping, a common method chosen in recent studies  (Ban et al.
Composite of datasets As we can see in Fig. 3 , the lengths of the available periods of the different observational datasets are not homogeneous; some are constrained by the availability of radar archives, and others are limited by databases not extended after the end of production projects. But extreme precipitation events in the northwestern Mediterranean present high temporal variability, and the chosen study period might strongly impact the calculation of extremes. Therefore, when calculating differences between models and observations, we choose only common periods. Further, in order to take full advantage of the databases and simulations, we use common time periods that can be spatially different. The figures will be composites with various time period depending on the datasets used (cf.
Percentiles In Sect. 3, percentiles are calculated for each model and observational datasets over each common period. The calculation takes into account all-day precipitation (both dry and wet-days) following  Schär et al. (2016). Interpolation on common ALP3 and ALP11 grids is performed before percentile computation in order to compare models and observation percentiles. For each grid point, absolute differences are then calculated and preferred to relative differences in order to highlight the areas with high percentiles values. CNRM-AROME and observations are compared on both 3 and 12 km grids. When considering CNRM-ALADIN, data are interpolated only on the ALP11 grid for a fair comparison. A bootstrapping method (described in appendix B1) is used to validate the statistical significance of model bias differences at the 90% confidence level.
On the figures representing percentiles differences on the 12 km grid (cf. Fig. 6de  and 8de ), only data with significant differences between model and observations are plotted. Added value A spatial added value index of the CNRM-AROME CP-RCM compared to CNRM-ALADIN RCM (cf. Fig. 6f  and 8f ) is computed. As for the calculation of differences, the absolute added value is preferred in order to highlight the areas with high percentile values. The added value index is defined on each grid point as the difference of the absolute values of the differences between the RCM and observations on the one hand and the CP-RCM and observations on the other hand, which means that for the Q studied statistics: Fig. 3  The available periods of the different precipitation observational datasets used for hourly and daily precipitation evaluation, compared to the period of the model evaluation simulation A positive added value index means that the CP-RCM bias is smaller than that of the RCM: we can therefore conclude that the CP-RCM behaves better than the RCM for the studied statistics.
Definition of the French MED area To compute statistics on French Mediterranean HPEs for both standard climate statistics and precipitating system detection and tracking, a specific area is defined, called French MED (cf. Fig. 1 ). Following  Ribes et al. (2019) , we computed the annual maximum 1-day rainfall RX1d for each grid point for the 20 years of the COMEPHORE observational dataset. Then we selected the rectangular limits of the area where the all-period average of RX1d is higher than 60 mm, the threshold used in  Ribes et al. (2019)  to select stations affected by Mediterranean events. As the COME-PHORE data does not cover the sea, the grid points over the sea are also removed for the model dataset. We excluded Corsica from this study area because of its specific orography, the difficulty of capturing trajectories on this small mountainous area of land, and the fact that COMEPHORE is not able to represent precipitation correctly on a large part of the island (cf.
Implementation The precipitating system detection and tracking algorithm described in Sect. 2. 3. 5 and appendix C is applied on the 1-h accumulated precipitation field both to the COME-PHORE observational dataset and to CNRM-AROME on the overlapping domain, called "tracking domain" and plotted in black on the Fig. 1. Focusing on fall Mediterranean HPEs, we apply it to twenty extended fall seasons (from September to December) from 1997 to 2016. On a given area, for each time step, the algorithm firstly detects the precipitating systems, defined by contiguous grid points above given thresholds from 2 to 30 mm/h with a minimum area of 20 km 2. In a second time, trajectories are built by identifying links between cells at different time steps. In Sect. 4. 2, we first focus on the precipitating system characteristics, mainly in function of the different thresholds.
-a criterion to select tracks occurring during days with heavy precipitation : we select all tracks occurring during days with 24-h accumulated precipitation exceeding the 100 mm threshold at least once over the French MED area (the 24-h accumulations are calculated on 24-h sliding windows in order to take into account events occurring between two calendar days). We are aware that other options would have been possible, but we tried to choose a reproducible one, consistent with the rest of the study and based on the Mediterranean HPEs' characteristics. We are also aware that the trajectories starting or ending over the Mediterranean sea, out of the tracking domain, will not be complete trajectories for these events but will be present in the tracking database. As the same information is missing in both the observations and the model, this does not bias the evaluation study, but as there is a missing element in the life cycle of some systems, the tracking database cannot be considered as a climatology of precipitating system trajectories.
9th percentile in Sect. 3. 2). In the southeast of France, the main observed maxima (Cevennes, French Riviera and Corsica) are also simulated by the model, but generally with a larger extension. In the eastern Pyrenees, parts of the Alps and Corsica, the lower density of convective cells with COMEPHORE could be related to the known underestimation of rainfall by rain gauge and radar in the observational dataset over mountainous regions. Plotting spatial distributions on several time periods (cf. Appendix A Fig. 17 ) shows a relative improvement over time in COMEPHORE's distribution over mountains. If so, CNRM-AROME could present a more realistic behaviour in these areas. The main statistics above threshold (annual mean number of cells, mean intensity and mean area) are presented in figure  12 , both for COMEPHORE and CNRM-AROME, on the French MED area, where we find the major part of the convective cells in the fall season (around 70% of the total number of cells for the 20 mm threshold).
Track characteristics The comparison of tracks between observations and the model allows us to evaluate the model's ability to produce realistic trajectory characteristics, such as number, duration, Variations of the number of tracks in function of these different characteristics are presented in Fig. 14a-f. The logarithmic scale for the number of tracks used in all the bar plots highlights the tail of the distribution but should be analysed with caution for the extreme part of the tail with a very low number of tracks. Similarly, the differences between model and observation systematically not significant at the confidence level of 95% for the tail of the distributions should not be over interpreted because of the low number of tracks. We count 5054 tracks in the French MED area within the COMEPHORE observational dataset (around 250 tracks/year).
The majority of the most severe tracks (7/9 for COMEPHORE and 7/7 for CNRM-AROME) are related to known HPEs after comparing the dates with the established list of HPEs up to 2010 by  Ponzano et al. (2020). Similarly, most severe tracks of COMEPHORE correspond Fig. 14  Variation of the number of tracks in function of a duration, b maximum intensity, c mean intensity, d maximum area, e velocity and f severity for CNRM-AROME in red and COMEPHORE in blue. A blue star is added when the differences between model and observation means are not significant at a confidence level of 95%. g presents IDF plot and h IAF plot for CNRM-AROME and COME-PHORE. In all the figures, we consider all tracks of cells exceeding 10 mm/h occurring during days exceeding 100 mm/day in the French MED area during the extended fall (SOND) between 1997 and 2016 ◂ to events present in the Meteo-France database of extreme precipitation (http://pluiesextremes.
The need for reliable precipitation datasets High temporal and spatial resolution observational datasets are necessary for carrying out climate model evaluation studies dealing with kilometric-scale CP-RCMs. The study of hourly precipitation extremes at the kilometric scale imposes strong constraints on the choice of observational reference databases because of the high variability in space and time of surface rainfall. For example, with daily and 25 km resolution data, the European EOBS database  (Haylock et al. 2008; Cornes et al. 2018 ) is not relevant  (Hofstra et al. 2010; Lenderink 2010; Flaounas et al. 2012; Prein and Gobiet 2017). Similarly, the satellite precipitation analyses of CMORPH  (Joyce et al. 2004 ), TRMM  (Huffman et al. 2010 ) and GPM  (Hou et al. 2011 ) present a too-coarse resolution of 25km.
A problematic bias can also be added for datasets using a convection parameterised model to disaggregate hourly fields from the daily amount  (Bosilovich et al. 2008; Parker 2016) , such as the SAFRAN precipitation analysis  (Durand et al. 1993; Quintana-Segui et al. 2008; Vidal et al. 2011). A solution for improving precipitation datasets and compensating for the insufficient density of rain gauges is the use of datasets merging rain gauges and high-resolution (around 1 km), temporally and spatially continuous weather radar data. Relevant hourly and kilometric gridded datasets of this kind already exist in France, Switzerland and Germany, but not yet in Italy, Spain or Croatia. The limitations of observations should also be kept in mind when analysing comparisons between models and observations before jumping to conclusions.
For example, rain gauge measurement errors such as possible underestimation of rainfall due to undercatchment in case of strong wind and/or low rainfall intensity or snowfall conditions  (Neff 1977; Yang et al. 1999 ) is particularly true in mountainous areas. Radar measurement also presents errors such as a masking effect leading to precipitation underestimation in complex topography areas or overestimation in case of hail. In the study, temporal and spatial discontinuities inside and between datasets must be taken into account as much as possible. For example, it is worth mentioning that in the COMEPHORE product, the number of radars increases over time and the processing method has evolved since 2007 (cf. appendix A). Its quality and therefore its ability to detect precipitating events are improving over time, which can affect the temporal homogeneity of the results.
It is also important to note the difference between grid resolution (i. e. grid spacing) and effective resolution of observational datasets. For gridded datasets based on stations, the effective resolution depends mainly on local stations densities. Isotta et al. (2014)  indicate an effective resolution of around 10-20 km for the 5 km daily precipitation dataset APGD-EURO4M. To our knowledge, no studies about effective resolution were carried out for the other datasets. This difference between grid resolution and effective resolution should prevent us from over-interpreting the results because some details and phenomena cannot be represented in the datasets. In the near future, improvements are expected in precipitation measurement and datasets. For example, the recent use of X-band dual-polarization radar  (Anagnostou et al.
Therefore, the CNRM-AROME CP-RCM simulation is compared to selected high spatial and temporal resolution observational datasets, such as the French kilometric and hourly COMEPHORE dataset now available for a 20-year period, and to its forcing simulation of the CNRM-ALADIN convection-parameterised Regional Climate Model (RCM). The comparison between the two models and observations leads to the following results: -CNRM-AROME is able to represent northwestern Mediterranean fall extreme precipitation at a daily scale and even better at an hourly scale: CNRM-AROME demonstrates its ability to represent HPEs in terms of location and intensity of extreme rainfall with daily 99th and hourly 99. 9th percentiles values close to observational values, especially in areas with high percentile values. Daily and hourly event-based studies also show CNRM-AROME's ability to represent extreme precipitation frequency and interannual variability; -outside the Mediterranean region, CNRM-AROME tends to overestimate hourly precipitation extremes.
A precipitating system detection and tracking algorithm is applied to 1-h accumulated precipitation fields on a period of twenty fall seasons, both for the CNRM-AROME CP-RCM and the reference COME-PHORE precipitation dataset. This approach turns out to be an efficient tool for evaluating high-resolution climate models. Considering French Mediterranean HPEs as objects, CNRM-AROME's ability to represent the main characteristics of fall convective events is highlighted in terms of location, number, mean intensity and area for precipitating systems and in terms of number, duration, intensity, area, velocity and severity for tracks. Its main limitation is the underestimation of high precipitation with an important bias in the number of cells for high thresholds and the lack of tracks with maximum intensity values exceeding 40 mm/h.
A COMEPHORE COMEPHORE (COmbinaison en vue de la Meilleure Estimation de la Précipitation HOraiRE) is a high-resolution (1 km×1 km) and hourly gridded dataset of precipitation merging radar and rain gauge observations provided by Meteo-France over metropolitan France  (Tabary et al. 2012; Fumière et al. 2020). It is available from 1997 to 2016. The first ten years of  COMEPHORE (1997 COMEPHORE ( -2006) )  were already used as an observational reference in the first evaluation study of the CNRM-AROME CP-RCM  (Fumière et al. 2020). COMEPHORE is also used in  Ban et al. (2020)  and  Pichelli et al. (2020). As this dataset is the reference for our extreme precipitation evaluation study over the southeast of France, we include a detailed description below. The COMEPHORE observational dataset is based on local radar 5 min reflectivities and rain gauge data (approximately 4000 rain gauges with a daily timestep including approximatively 1200 rain gauges with an hourly timestep, cf.
Then, twenty-four intermediate rainfall accumulations calculated with ANTILOPE are used for the temporal interpolation to an hourly time step. advantages COMEPHORE allows a good quality climatology of daily surface precipitation accumulation over long time steps (seasonal) to be reproduced, with values similar to those obtained with the Lassegues (2018) dataset, a high-resolution dataset resulting from a kriging of rain gauge daily rainfall data over France  (Roger 2018). Thanks to the use of high spatial resolution and coverage radar, COMEPHORE provides information in regions where the density of stations is low and allows a better representation of extreme values with a refined representation of temporal and spatial precipitation fields and therefore a better localisation of heavy rainfall peaks and higher maximum intensities values  (Roger 2018).
With a focus on the southeast of France,  Fumière et al. (2020)  show that COMEPHORE is a more suitable product for the study of fall extreme precipitation than the SAFRAN database  (Durand et al. 1993; Quintana-Segui et al. 2008; Vidal et al. 2011). limitations If corrections of errors related to radar measurement are made in the COMEPHORE processing, a mask effect problem in areas with high altitude leads to the underestimation of precipitation amounts in mountainous areas above 1500 m (Roger 2018). COMEPHORE does not yet have correction taking altitude into account, and these regions also encounter a low density of rain gauges and undercatchment problems. The regions with the lowest-quality data are limited to parts of the Alps, limited parts of the Pyrenees and the western part of Corsica (cf.
B1 Bootstrapping method The following bootstrapping method is used to validate the statistical significance of model bias differences at the 90% confidence level. For each observation/model common period defined in Sect. 2. 3. 2, observations and model datasets are simultaneously resampled with replacement for the same time period so that resampled datasets contain the same meteorological conditions for all datasets. The resampling is done for time blocks of one-season in order to avoid over-confident estimates due to temporal correlation. We then calculate percentiles for each resampled datasets on each point of the domain and compute the 5th-95th percentile ranges of the differences between the percentiles of the two datasets. For each grid point, if the zero value is included in the range, we conclude that the difference between the studied percentiles of the two datasets is not significant at the 90% confidence level.
This projection is obtained using the procedure described in Algorithm 1 (see  [8]  for details about this transformation). In order to estimate the impact of noise addition, we conducted experiments on the φ angles of the first 25 residues of a protein (1M22) extracted from Dataset A (presented in Sect. 2. 7). Results obtained for a thousand Monte-Carlo simulations are depicted in Figure  2A  for noise levels η 1 ∈ {0. 05, 0. 1} Å. We conclude that noisy distances will impact significantly dihedral angles when the imprecision is greater than 0. 05 Å. Eq. (  2 ), formulated as cos ω = h(α, β, γ), shows that the dihedral angle ω can be computed only based on angles α, β, γ. If these angles were to be computed with another method than distances, the impact on the dihedral angles might be reduced.
Datasets of protein structures Dataset A: Dataset A is composed of 3621 protein X-ray crystallographic structures downloaded from the server PISCES  [39]. These structures correspond to a set of Protein Data Bank (PDB)  [3]  entries for which structures have been determined at a resolution better than 1. 6 Å, and with R factors better than 0. 25. The set of PDB entries and protein chains present in dataset A has been chosen  [39]  in order that the percentage of sequence identity between any pair of chains is smaller than 20%, to avoid statistical bias on the protein sequences. Dataset B: Dataset B is composed of 226 protein structures obtained by processing the database of NMR chemical shifts used for the training of the neural network TALOS-N  [35]. For 226 proteins of this database, a structure was determined by NMR.
In addition, as described in Section 2. 7, the Dataset B is only used for testing purpose and not for training. In the case when only C α atoms were included in the calculation, the prediction of secondary structures (Figure  3 , lower panels) displays features similar to those observed when all atoms were included in the graph. In case where no noise was added to the angle/distance information, the F1-scores were the most decreased, but the decrease was bigger for FOS than for Sequoia. Overall, the prediction of α helix alone (green curves) keeps quite similar scores than in the case of all backbone heavy atoms were considered. There is a marked decrease of the success as soon as more than one type of secondary structure is considered. The C α networks seems thus to have less discriminating features between different secondary structures than the all network of heavy backbone atoms.