dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors thank the captain and the crew of the  R/V Pourquoi Pas ? for their professionalism and their work at sea. We thank  Julia Uitz ,  Céline Dimier  and the SAPIGH HPLC analytical service at  Institut de la Mer de Villefranche (IMEV)  for sampling and analysis of phytoplankton pigments;  John Dolan  for microscopic counting; and  Lynne Macarez  and the  PIQv-platform of EMBRC-France, a national Research Infrastructure  supported by  ANR , under the reference  ANR-10-INSB-02 , for meso-zooplankton analyses. Financial support. This study is a contribution to the  PEACETIME  project, a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA , and  Météo-France  as part of the programme MISTRALS coordinated by  INSU. PEACETIME is a contribution to SOLAS and IMBER international programmes.
The project was endorsed as a process study by  GEOTRACES. The project leading to this publication has also received funding from the  European FEDER Fund  under project  1166-39417. Julie Dinasquet  was funded by a  Marie Curie Actions-International Outgoing Fellowship  ( PIOF-GA-2013-629378 ). Review statement. This paper was edited by  Christine Klaas  and reviewed by two anonymous referees.
, 2006; Ryder et al. , 2018; Di Biagio et al. , 2019). Measurements of iron oxides on soils from around the world have been reported for two particle size classes, i. e. , the clays with diameters of less than 2 µm and the silts with diameters between 2 and 64 µm. The iron oxide varies drastically, depending on the soil types, and most measurements indicate a weight content of 1 % to 7 % equivalent to 0. 5 % to 3. 5 %  (Lafon et al. , 2006; Di Biagio et al. , 2019; Moosmüller et al. , 2012; Engelbrecht et al. , 2016; Journet et al. , 2014)  by volume. To determine the amount of iron oxide over the Sahel, we used the high-resolution database published by  Nickovic et al. (2012)  and determined the amount of hematite over the Sahel region (16 • W-36 • E; 10-20 • N), as shown in Fig. 1. With the 30 s arc grid high-resolution of the database (GMINER30; http://www.
Acknowledgements. We would like to thank  Jim Haywood  and the two anonymous reviewers, for their suggestions on restructuring the initial draft. Yves Balkanski  and  Ramiro Checa-Garcia  acknowledge the hospitality of the  Institut Pascal, Université Paris-Saclay , during the  INDICES program  of 2019 ( Investissements d'avenir   ANR-11-IDEX-0003-01  Paris-Saclay). The simulations were performed using the supercomputing resources from the  GENCI  ( Grand Equipement National de Calcul Intensif ; grant no. 2020-t2014012201 ). Financial support. This research has been supported by the  European Union 's  Horizon 2020 Research and Innovation programme CRESCENDO  (grant no. 641816 ). Review statement. This paper was edited by  Ulrich Pöschl  and  Rolf Müller  and reviewed by  J. M. Haywood  and two anonymous referees.
method since it does not rely on an explicit mathematical relationship between the predictors and the target distribution of the predictand. Furthermore, many predictors can be incorporated without decreasing the forecast skill  (Taillardat et al. , 2019) , which can be particularly interesting in our case when, for example, the raw ensemble only contains zero HN while rainfall forecasts are large. Indeed, in some cases, the PEARP-S2M ensemble snowpack completely misses important snow events (e. g. due to an erroneous rain/snow limit). For some problematic meteorological situations, QRF can possibly provide a specific correction. Section 2 summarizes the forecasts and observations dataset used in this study. Section 3 provides the details of the ensemble model output statistics (EMOS) method tested in this study, a particular nonhomogeneous regression method already employed in  Nousu et al.
Implementation The QRFs are obtained using the function quantregForest of the package quantregForest in R  (R, 2017). The splitting procedure described above can be constrained by different choices, e. g. a minimum number of observations in leaves. In this paper, we grow 1000 trees which represent a sufficiently large number of trees to span a great variety of meteorological situations without demanding an unbearable computational effort. Different values for the parameters mtry and nodesize have been tried, the best performances being found for mtry=2 and nodesize=10 (see Section 6).
Cross-validation For all the experiments of this study, we use a leave-one season out cross-validation scheme. For each of the 22 seasons, the season is used as a validation dataset while the other 21 seasons are used for training. It first ensures that a robust calibration of the post-processing methods is obtained. It also avoids the evaluation of the performances with a unique validation period that could be atypical (e. g. a very snowy/dry winter season).
Comparison of performances between QRF and EMOS approaches In this paper, we compare the scores of post-processed forecasts of the 24h height of new snow between two commonly used statistical methods: EMOS and QRF. With this dataset, the added value of QRF is unambiguous with a general improvement of CRPS, an improvement of rank diagrams for severe snowfall events, and a slight improvement of ROC curves for more common events. The predictors selected by the QRF training clearly suggest that the simulated HN from the Crocus snow cover model is useful but not sufficient to optimize the post-processed forecasts as the meteorological variables forcing the snow cover model are also selected by the algorithm. The added value coming from these meteorological predictors is the most likely explanation of the improvement obtained between QRF and EMOS.
Limitations for operational perspectives In order to investigate the potentials of the statistical methods themselves regardless of the constraints on the available dataset, we choose in this paper to calibrate and evaluate the post-processing methods on the same 22-year long dataset with a crossvalidation scheme. However,  Nousu et al. (2019)  illustrate the strong impact of the discrepancies between reforecasts and operational forecasts in the post-processing efficiency. In complementary investigations (not shown), we noted that QRF is even more sensitive to the homogeneity between calibration and application datasets. For instance, the added value of QRF compared to EMOS was completely lost when using the evaluation dataset of  Nousu et al. (2019)  (operational PEARP-S2M forecasts). Therefore, despite the significant added value of QRF compared to EMOS with consistent and homogeneous datasets for calibration and evaluation, its practical implementation in real-time operational forecasting products is still a challenge because reforecasts strictly identical to operational configurations are often not available.
Time adaptative training based on operational systems is an alternative to favour the homogeneity of the dataset. Although new theories are emerging to face the challenge of model evolutions  (Demaeyer and Vannitsem, 2020) , several consistent recent studies show that the length of the calibration period is more critical than the strict homogeneity of datasets to forecast rare events  (Lang et al. , 2020; Hess, 2020). In the case of HN forecasts from EMOS  (Nousu et al. , 2019) , even a 4-year calibration period was detrimental for the reliability of severe snowfall events compared to a longer heterogeneous reforecast. However,  Taillardat and Mestre (2020)  manage to successfully implement QRF in real-time forecasting products of hourly precipitation using a calibration limited to 2-year operational forecasts, because they adapt the distribution tail with a parametric method.
Producing reforecasts more homogeneous with operational forecasts is still one of the most promising solution to improve the forecast probabilities of severe events but the evolutive skill of NWP systems is strongly linked to the available data to assimilate and will never be completely removed. Therefore, the robustness of post-processing algorithms for their transfer to operational dataset or their efficiency when calibrated with shorter datasets will always remain the most important criteria compared to their theoretical added values with perfect and long datasets. This is therefore a major point to consider to transpose the advances of this paper towards operational automatic HN forecasts.
If the predictive distributions obtained with the different post-processing methods are adequate, then the CDF values of the predictive distributions for the observations should be uniformly distributed (so-called Probability Integral Transform, PIT). The flatness of the histogram of these CDF values is a necessary but not sufficient condition of the system reliability. Systematic biases are detected by strongly asymmetric rank histograms. It is also an indicator of the spread skill as underdispersion will result in a U-shaped rank histogram and overdispersion in a bell-shaped rank histogram. Rank histograms can be computed for the whole forecast dataset, or stratified according to different classes of average ensemble forecasts (stratifying according to the observations leading erroneous conclusions, see Bellier et al.
, 2017). In this study, as proposed in the recent study of Bröcker and Bouallègue (2020) , a stratification based on the average of the combination of raw forecasts and the verification observations is used. Three https://doi. org/10. 5194/npg-2021-18 Preprint. Discussion started: 28 April 2021 c Author(s) 2021. CC BY 4. 0 License. HN intervals are considered: [0 cm ; 10 cm[,[10 cm ; 30 cm[ and [30 cm  ; ∞[. To guarantee a sufficient sample size for rank histograms, they are computed for the whole evaluation dataset by considering all dates and stations as independent.
Acknowledgements. This work has been undertaken inside the project  PROSNOW  which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  730203. The authors would like to thank  Bruno Joly  who developed and ran the PEARP reforecast,  Matthieu Vernay  who developed and ran the SAFRAN downscaling of the PEARP reforecast and realtime forecasts and  Michael Scheuerer  for providing the initial code of the  EMOS-CSGD. CNRM/CEN  and  INRAE are part of LabEX OSUG@2020  ( ANR10 LABX56 ).
Typical ground-motion models predict the response spectral ordinates (GMM-SA), which are the damped responses of a suite of single-degree-of-freedom oscillators. Response spectra represent the response of an idealized structure to input ground-motion, but not the physics of the actual ground-motion. To complement the regionally adaptable GMM-SA of  Kotha et al. (2020) , we introduce here a model capable of predicting Fourier amplitudes (GMM-FA); developed from the same Engineering Strong Motion (ESM) dataset for pan-Europe. This GMM-FA reveals the very high variability of high frequency ground-motions, which are completely masked in a GMM-SA. By maintaining the development strategies of GMM-FA identical to that of the GMM-SA, we are able to evaluate the physical meaning of the spatial variability of anelastic attenuation and source characteristics.
We find that a fully data-driven geospatial index, Activity Index (AIx), correlates well with the spatial variability of these physical effects. AIx is a fuzzy combination of seismicity and crustal parameters, and can be used to adapt the attenuation and source non-ergodicity of the GMM-FA to regions and tectonic localities sparsely sampled in ESM. While AIx, and a few other parameters we touch upon, may help understand the spatial variability of high frequency attenuation and source effects, the high frequency site-response variability -dominating the overall aleatory variance -is yet unresolvable. With the rapid increase in quantity and quality of ground-motion datasets, we call for an upgrade of regionalization techniques, site-characterisation, and a paradigm shift to Fourier groundmotion models to complement the traditional response spectra prediction models.
INTRODUCTION Typical ground-motion models (GMMs) used in seismic hazard and risk assessments predict the random distribution of ground-motion in terms of spectral amplitude (), i. e. damped response of an elastic single-degree-of-freedom (SDOF) oscillator with fundamental resonance period. In  Kotha et al. (2020)  we introduced the regionally adaptable GMM derived from the Engineering Strong-Motion (ESM) dataset  (Bindi et al. 2019a; Lanzano et al. 2018) , developed specifically to predict the 5% damped response spectra () of shallow crustal earthquakes in Europe and Mediterranean regions (GMM-SA from hereon). GMM-SAs are quite useful in engineering applications because they predict the response of idealized SDOF structures to input ground-motion. However, ( ) integrate characteristics of Fourier amplitudes over a range of frequencies (), and hence do not exactly represent the actual ground-motion itself.
In addition, using the random vibration theory (RVT) framework of  Bora et al. (2016)  one can conveniently generate () from a GMM-FA. Alongside the GMM-SA of  Kotha et al. (2020) , the ESM dataset provides also an opportunity to develop a GMM-FA for shallow crustal earthquakes originating in the pan-European region; extending from Pyrenness in the west to North-Eastern Anatolian fault system of Turkey. As it is, there are very few GMM-FA developed for this regionthe most recent one being that of  Bora et al. (2014)  derived from an older pan-European strong ground-motion dataset RESORCE  (Akkar et al. 2014). Bindi et al. (2019a)  developed a GMM-FA from the ESM dataset, but it was not intended for engineering application but to perform a sanity check of data through residual analysis. Essentially, the last update of a compendium of GMMs published since 1964 by Douglas (2011) features only 18 models that predict Fourier Amplitude Spectra (FAS).
For instance, the short-period  integrate features of moderate-high frequency  , making it difficult to interpret physically the quantified random-effects at  < 0. 5. Towards this end, in this study we introduce a regionally adaptable GMM-FA, correlate the regional adjustments to an independently derived geospatial index, and suggest using these correlation to adapt the model to regions with little to no data in the ESM dataset. Our best candidate geospatial index is the Activity Index developed by  Chen et al. (2018). Although we have tried a suite of geological and geophysical parameters (e. g. Moho depth, 1Hz coda Q maps, etc) to serve as proxy for our regional adjustments (the random-effects), the spatial coverage and resolution, and the epistemic uncertainty of these parameters made them less viable than the globally available Activity Index.
GROUND-MOTION DATASET The ground-motion dataset we used is the Fourier amplitude version of the ESM dataset. The Fourier version of ESM does not contain data from Iran, and is therefore a few dozen records smaller than that used to derive the  Kotha et al. (2020)  GMM-SA. The ground-motion data selection criteria, regionalisation models, functional form, and regression methods are identical to that of the Kotha et al. (2020) GMM-SA -except for a few minor changes noted subsequently: 1) We select only those events classified as non-subduction events by  Weatherill et al. (2020d). The selection removes in-slab, interface, outer-rise, and upper-mantle events from the regression dataset. The resulting 923 events with hypocentral depth 0 <  ≤ 39 are located in regions with 14 ≤ ℎ ℎ ≤ 49, as per the Moho map of  Grad et al.
(2009). Note that the same selection criteria resulted in 927 events in case of GMM-SA 2) Only those events with ≥ 3 records in the dataset are used in regression. Also, wherever available, the harmonised   estimates according to the European Mediterranean Earthquake Catalogue  [EMEC]   (Grünthal and Wahlström 2012)  are preferred over the ESM default values. As with GMM-SA, preference of EMEC   is to maintain consistency with the seismic source models developed for the new European Seismic Hazard Map (ESHM20) 3) We keep data from all 1622 sites in the dataset irrespective of whether their  30 measured from geotechnical investigations is provided or not in ESM. This is to estimate the site-specific terms (2  ) at as many sites as possible, and then explore various site-response proxies to characterize them  (Kotha et al.
REGIONALISATION DATASETS The GMM-SA of  Kotha et al. (2020)  is a regionally adaptable model, wherein the event and path effects are regionalised. It is capable of predicting () accounting the regional differences in distance decay through  3, () ≈ Ɲ(0,  3 ()) and average of localised source effects through 2  () ≈ Ɲ(0,  2 ()) , and site-specific effects through 2  () ≈ Ɲ(0,  2 ()) random-effect adjustments to the generic GMM-SA median ( ()). We use the same regionalisation models for GMM-FA, and therefore, regionalise the data identically. Regional differences in locality-specific event and region-specific anelastic attenuation characteristics are estimated as random-effects in a mixedeffects regression. The GMM-FA random-effect values can be used in three ways: 1) in region-and locality-specific predictions accounting their epistemic uncertainties, 2) ignored while reintroducing and locality-specific adaption, 3) investigated for correlation with geophysical parameters or proxies in order to extend their usage to regions with no data in ESM.
EVENT LOCALISATION Alongside the traditional earthquake-to-earthquake variability captured by the between-event randomeffect group   ≈ Ɲ(0,   ),  Kotha et al. (2020)  introduced an additional random-effect to quantify locality-to-locality variability of earthquake characteristics; analogues to the location-to-location variability 2  ≈ Ɲ(0,  2 ), of Al  Atik et al. (2010). We assign the 923 shallow crustal events in the ESM dataset to 55 seismotectonic zones defined in the European Seismic Hazard Model 2020 (ESHM20). The seismotectonic source zonation (called "TECTO") was designed to distinguish regional tectonic features influencing the generation of crustal earthquakes, but not the very localised features. 'TECTO' was thus a good model for earthquake source localisation in  Kotha et al. (2020) , and distinct from the TSUMAPS-NEAM of  Basili et al.
(2019)  used for regionalisation of anelastic attenuation. As in  Kotha et al. (2020) , this random-effects group is the between-locality 2  ≈ Ɲ(0,  2 ), wherein the levels are tectonic localities (locality ) containing their assigned ESM crustal events and records. Fig. 3  shows the distribution of records from active shallow crustal events within the event localisation polygons. As with the attenuating region group, this group is introduced to quantify the earthquake locality-to-locality variability of ground-motion in the dataset through the betweenlocality variance ( 2 2 ), which if zero indicates that the regionalisation model is not able to identify any signficant spatial variability of earthquake characteristics.
ACTIVITY INDEX Chen et al. (  2018 ) introduced a fully data-driven global tectonic regionalisation model for selection of ground-motion models in seismic hazard applications. Based on a fuzzy logic workflow, they have rendered a regular grid with a spacing of 0. 5 o , wherein each cell is assigned a probability of being an active tectonic regionthe Activity Index (AIx), as shown in Fig. 4. AIx in a grid cell is calculated from the following fuzzy rules: AIx is derived as a of combination of seismic moment rate density  (Weatherill et al. 2016 ), 1Hz Lg coda Q  (Mitchell et al. 2008) , and shear-wave velocity variation at 175km  (Ritsema et al. 2011 ). We use this dataset to evaluate the various regional variabilities quantified as the GMM random-effects.
0 is the generic event-to-event variability corrected for locality-to-locality variability, and does not vary with locality  4) Site-to-site response variability is captured by the site-specific random-effects 2 = Ɲ(0,  2 ). The potential of 2  in site-specific GMM-SAs is well-known, and are useful in studying regional differences in site-response scaling with  30 (time-averaged shear-wave velocity in 30m top-soil) as in  Kotha et al. (2016) , topographic slope and geology as in  Weatherill et al. (2020a)  or a variety of site parameters  (Kotha et al. 2018; Pilz and Cotton 2019; Zhu et al. 2020) , and even in site-specific seismic risk assessment  (Kohrangi et al. 2020). Site-specific GMM-FA adjustments 2  , estimated for the 1622 sites  in the dataset Fig. 3 , can be used to make site-specific groundmotion predictions by replacing the offset  1 in equation (  1 ) with  1, =  1 + 2  5) The left-over residuals  = (0, ) contain the unexplained natural variability of ground-motion observations, and thus represent the apparent aleatory variability of the model.
RANDOM-EFFECTS AND RESIDUALS Prior to discussing the random-effects and residuals in following sections, there are few inferences derived from the random-effect and residual variances in Fig. 6. In this plot,  2 ,  2 ,  0 ,  3 are the random-effect standard deviations of between-site, between-locality (tectonic zones), between-event (after between-locality correction), and between-region (anelastic attenuation) random-variables, respectively. is the residual standard deviation. The total-sigma of the ergodic version of GMM-FA, All the random-variances are comparable in size, which means the random-effect groups are meaningful for this dataset. The largest variability, however, is the site-to-site response variabilitycaptured by the between-site standard deviation  2. Increasing monotonically at  ≥ 3,  2 suggests that site-response (in the dataset) is highly variable at moderate-high frequencies.
Such rapid increase of  2 towards higher frequencies has been reported for a various datasets  (Bayless and Abrahamson 2019b; Bindi et al. 2017b; Bindi et al. 2021). For instance, ground-motion amplification at a site with 2  ( = 10) = 1. 5 *  2 ( = 10) is 20 times larger than that at a site with 2  ( = 10) = -1. 5 *  2 ( = 10). The large variability in site-response and the consequently large  2 (the largest contributor to ) suggests that partially non-ergodic site-specific ground-motion predictions may soon become indispensable  (Faccioli et al. 2015; Kotha et al. 2017; Rodriguez-Marek et al. 2013). The next largest random variance is that of between-event variability quantified into  0. Note that a part of the overall between-event (spatiotemporal) variability   is quantified in to the betweenlocality variability  2 ; as in   = √ 0 2 +  2 2.
Apparently, variability of event-specific effects is the highest at  ≤ 0. 5. Seismic moment and moment-magnitude are the event-specific parameters estimated at these frequencies. For a GMM with   (from EMEC catalog) as an explanatory variable, such large between-event variability at  ≤ 0. 5 suggests large differences in observed groundmotions between events of identical. The most likely cause, to our understanding, is errors in   in the dataset. A few studies  (Holmgren and Atkinson 2018; Kuehn and Abrahamson 2017)  demonstrated that   uncertainty is a contributor to the between-event variability at long period spectral accelerations, which are analogues to low frequency Fourier amplitudes. Beyond  ≥ 1 the  0 values are lower and remain almost constant. The counter-part of  0 is the between-locality variability  2 , which captures the average event-to-event variability when events are localized into seismotectonic zones ('TECTO').
Since the random-effects are estimated only for the regions, localities, and sites (levelsas explained in Regionalisation Datasets) whose ground-motion data is used in GMM regression, new levels with no ground-motion data cannot benefit from the non-ergodic levelspecific GMM predictions. However, correlating random-effects to a physical parameter or a geospatial index may allow, in a limited way, predicting region, locality, and site-specific adjustments for new levels outside the regressed dataset through the correlated parameter.