dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, both the finite-volume and spectral-transform formulations can share the same quasi-uniform horizontal grid with co-located arrangement of variables, geospherical longitudelatitude coordinates, and physics parameterizations, thereby facilitating their comparison, coexistence, and combination in the IFS. We highlight the advanced semi-implicit NFT finitevolume integration of the fully compressible equations of IFS-FVM considering comprehensive moist-precipitating dynamics with coupling to the IFS cloud parameterization by means of a generic interface. These developments -including a new horizontal-vertical split NFT MPDATA advective transport scheme, variable time stepping, effective precondi-tioning of the elliptic Helmholtz solver in the semi-implicit scheme, and a computationally efficient implementation of the median-dual finite-volume approach -provide a basis for the efficacy of IFS-FVM and its application in global numerical weather prediction.
(2017). Atlas is also designed to make use of specific programming paradigms to support accelerators, although these have not yet been explored with IFS-FVM. For the quasi-uniform octahedral reduced Gaussian grid (Sect. 2. 4), the parallelization of Atlas adopts the equalregion horizontal domain decomposition of IFS. The nearestneighbour communications enabling the FV stencil operations in the parallel horizontal domain are performed on overlap regions (halos) between partitions, as well as a "periodic overlap" for the east-west boundary on the globe. With the median-dual FV approach presented above, an overlap region of one element is typically used. In the present work, the programming of the discrete differential operators (  22 ) and (  23 ) in the IFS-FVM modern Fortran code has been comprehensively revised from earlier implementations in  Smolarkiewicz et al.
(2016). While Smolarkiewicz et al. (  2016 ) used a hybrid edge-and node-based programming already different from the edge-based codes described in  Szmelter and Smolarkiewicz (2010) , the current IFS-FVM programming represents a purely node-based implementation. When evaluating Eqs. (  22 ) and (  23 ), outer loops over nodes of the co-located grid (re)compute the required quantities on edges "on the fly". The code based on the resulting longer, fused node-based loops performs a larger overall number of computations as quantities on the edges are computed more than once, but it is overall more efficient as many intermediate memory stores are avoided and there is a greater chance for data in cache to be reused. Due to the underlying FD-FV discretization leading to stencil computations, IFS-FVM is naturally cache and memory-bandwidth bound.
The node-based programming improves the flop-perbyte ratio and relaxes the dependency on relatively (compared to pure computation) slow memory access. As a consequence, it provides a significant gain in efficiency compared to the hybrid edge-and node-based programming applied in  Smolarkiewicz et al. (2016). In addition, the fused loops and data locality in the node-based code effectively enabled the performance of the shared-memory parallelization with OpenMP, as the threads may operate on more local memory regions and avoid thread conflicts and cache trashing. An overall speed-up of IFS-FVM by a factor of ∼ 3-4 has been found on ECMWF's Cray XC40 by converting from the hybrid edge-and node-based to the purely node-based code. We note that the GCR solver for the Helmholtz problem (20) uses global communications in the computation of inner products (characteristic of Krylov-subspace methods) and residual error norms.
While the baroclinic instability benchmark aims at global atmospheric dynamics in the hydrostatic regime, referenced supplementary studies with IFS-FVM emphasize non-orographic and orographic flows in the nonhydrostatic regime. In addition to solution quality, we have demonstrated highly competitive computational efficiency of the presented semi-implicit NFT finite-volume integration of IFS-FVM in comparison to the semi-implicit semi-Lagrangian integration of IFS-ST. Common aspects of the finite-volume and spectraltransform model formulations are the octahedral reduced Gaussian grid, the co-location of variables, the geospherical framework, and the physics parameterizations. Sharing these properties facilitates the comparison of the different discretizations and physics-dynamics coupling. Moreover, it provides numerous benefits for the general IFS model infrastructure, data assimilation, and ensemble system.
5δt) , (A1) which provides the updated densities ρ [1]  d , ρ [2]  d , ρ n+1 d and accumulates normal mass fluxes (v z Gρ d ) [1] , (v ⊥ h Gρ d ) [2] , (v z Gρ d ) [3] for the three sub-steps. For compatibility with mass continuity, these quantities are then all employed in the subsequent advective transport of scalar variables (Eq. 8) as [1] i = A z i ( , (v z Gρ d ) [1] , (Gρ d ) n , (Gρ d ) [1] , 0. 5δt) , [2] 1] , (v ⊥ h Gρ d ) [2] , (Gρ d ) [1] , (Gρ d ) [2] , δt) , 2] ,(v z Gρ d ) [3] ,(Gρ d ) [2] ,(Gρ d ) n+1 , 0. 5δt) , (A2) where i ≡ [3] i. In Eqs. (A1) and (A2), the implementation of the horizontal advection transport A xy follows the horizontal part of the unstructured-mesh FV MPDATA of  Kühnlein and Smolarkiewicz (2017). The vertical scheme A z is a corresponding 1-D structured-grid MPDATA.
Results from numerical experimentation relevant to NWP show that the presented horizontally-vertically split NFT scheme based on MPDATA can be considerably more efficient than the standard fully multidimensional (unsplit) MPDATA of  Kühnlein and Smolarkiewicz (2017). This is particularly due to the integration of the vertical parts A z i with δt/2 each, which mitigates the vertical stability restriction while not adding any significant computational cost 17. Overall, the horizontal-vertical splitting of A i can enable a more than twice larger time step in the integration than the unsplit formulation. In addition, the split scheme facilitates the application of higher-order, e. g. Waruszewski et al. (2018) , and/or flux-form semi-Lagrangian advective transport in the vertical. While a detailed presentation and analysis will be provided in a future publication, results so far indicate a comparable solution quality of the split versus unsplit schemes for global atmospheric flow benchmarks.
Acknowledgements. We would like to thank two reviewers for their useful comments. Helpful discussions with the physics parameterization team at  ECMWF  are gratefully acknowledged. This work was supported in part by funding received from the  European Research Council  under the European Union's  Seventh Framework Programme   FP7/2012 / ERC  (grant agreement no. 320375 ), the  Horizon 2020 Research and Innovation Programme ESCAPE  (grant agreement no. 671627 ), and  ESIWACE  (grant agreement no. 675191 ). Rupert Klein  thanks  ECMWF  for support under their fellowship programme and acknowledges partial support of his contributions by the  Deutsche Forschungsgemeinschaft , grant  CRC 1114  " Scaling Cascades in Complex Systems ", project  A02. Zbigniew Piotrowski  acknowledges partial support from the " Numerical weather prediction for sustainable Europe " project, carried out within the  FIRST TEAM programme of the Foundation for Polish Science co -financed by the  European Union  under the  European Regional Development Fund.
Code availability. Model codes developed at ECMWF are the intellectual property of ECMWF and its member states, and therefore the IFS code is not publicly available. Access to a reduced version of the IFS code may be obtained from ECMWF under an OpenIFS licence (see http://www. ecmwf. int/en/research/projects/openifs for further information, last access: 6 February 2019). Data availability. The model output data can be downloaded from https://doi. org/10. 5281/zenodo. 1445597 (Kühnlein et al. , 2018).
Abstract. Water infiltration and recharge processes in karst systems are complex and difficult to measure with conventional hydrological methods. In particular, temporarily saturated groundwater reservoirs hosted in the vadose zone can play a buffering role in water infiltration. This results from the pronounced porosity and permeability contrasts created by local karstification processes of carbonate rocks. Analyses of time-lapse 2-D geoelectrical imaging over a period of 3 years at the Rochefort Cave Laboratory (RCL) site in south Belgium highlight variable hydrodynamics in a karst vadose zone. This represents the first long-term and permanently installed electrical resistivity tomography (ERT) monitoring in a karst landscape. The collected data were compared to conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) and a detailed structural analysis of the local geological structures providing a thorough understanding of the groundwater infiltration.
The growing water drop creates a decrease in the FM signal, followed by a sharp increase triggered by the drop's fall. Specific electrical conductivity (SpC) measurements are also performed in-cave at the PWD1 monitoring station, as well as at the surface for monitoring rainwater conductivity, next to the ERT profile. Both measurements are performed using a Campbell CS547A probe (accuracy of ±5 %). Rainfall was monitored for the whole period of ERT monitoring using a Lufft tipping bucket type rain gauge with a 1 min sample rate, located on the RCL site itself. The locations of the WCR profile and rain gauge are shown in Fig. 1c. Additional potential evapotranspiration (ET 0 ) data are also available. ET 0 is derived from the Penman-Monteith relationship  (Allen et al. , 1998)  based on data from a meteorological station (Pameseb) located 5 km from the Rochefort monitoring site.
Every significant precipitation event progressively infiltrates the soil layer, producing a sharp increase in VWC followed by an exponential recession curve. The delay between the beginning of the rainfall event and the first arrival of infiltrating water depends on the intensity of the rainfall event, evapotranspiration conditions, the depth of the moisture probe and hydraulic conductivity parameters defining the soil retention curve. In winter, a delay of ∼ 14 h is observed between the 10 and 105 cm deep probe. In summer, this delay can be significantly longer, up to several days. The influence of evapotranspiration is clearly noticeable as fewer peaks are present in the VWC data set. After long droughts, such as that of August and September 2016, a delay of 85 days is noticed between the first moisture content peak observed at 10 cm depth and that observed at 105 cm.
In parallel, in-cave percolating water discharge data bring crucial information on the infiltration processes occurring in the vadose zone at the RCL site. The three stations show different discharge dynamics given their location and the type of inlet flow that they sample. Smart and Friederich (1987)  developed a drip discharge classification based on the relationships between maximum discharges and coefficients of variation of the discharge; they can be described as vadose flows for PWD1 and PWD2 and seepage flow for PWD3 respectively. Vadose flows refer to high discharges, albeit lower than for shaft flows, with a high variability, especially regarding their rainfall events responses. Seepage flows exhibit significantly lower discharges with low coefficients of variation but noticeable seasonal changes.
Despite being classified as vadose flow, the PWD2 data set exhibits a strong seasonal pattern. It actually samples more of a dripping zone rather than one single inlet flow associated with one stalagmite or fracture. This could lead to overestimating the maximum discharge regarding the approach of  Smart and Friederich (1987). PWD2 could therefore be described as seasonal drip, which differs from seepage flows by its higher coefficient of variation, following the modification of the classification after  Baker (1997). The spatial proximity of PWD1 and PWD2 exhibiting different discharge regimes testifies to the high heterogeneity of the Rochefort karst. Seasonal cycles affecting percolating water are strongly related to effective rainfall and soil moisture data, as shown in Fig. 2. Similar observations have been described and analysed in multiple studies, highlighting the buffering role of the epikarst in water infiltration (e.
Unlike PWD2 and PWD3, PWD1 does not exhibit a clear seasonal trend, even though the baseflow threshold and post-storm drip decrease during the driest periods, while longer recession curves are observed. This is especially the case for the August and September 2016 drought. Poulain et al. (2018)  provides a specific analysis of the diffuse flow and quick-flow components of PWD1, supported by a vadose dye tracing test. It confirms the two-flow regime as a mixing of matrix and conduit infiltration. PWD2 and PWD3 seem to depend more on diffuse flow through the matrix but a part of quick flow is still present in the signal. In conclusion, drip discharge data reflect well their station's location: PWD1 samples inlet flows from an open fracture crosscutting a clayey limestone layer, which explains the great quick-flow component from post-storm drip type percolation through the fracture.
(2009)  to monitor shallow recharge processes. Two acquisition systems were installed at the RCL site. A first testing period lasted from March 2014 until June 2014. An automated time-lapse electrical resistivity tomography (ALERT) acquisition system developed by the British Geological Survey  (Kuras et al. , 2009)  collected daily dipole-dipole (DD) measurements. After this testing period, we installed a four-channel Iris Syscal Pro resistivity meter in March 2015, which is still presently measuring. Daily multiple gradient (GD) and DD data were collected, except for summer 2015 and winter 2016 where DD arrays, which require higher injection power, suffered from battery malfunction issues. Both acquisition systems are remotely controlled from the office. Data are automatically sent to a server and checked for measurement errors.
The acquisition system is installed in a brick shelter, furnished with a wired internet connection and a 230 VAC power access, providing ideal infrastructure for an ERT monitoring site. The measurement protocols involve dipole-dipole and multiple gradient types. They were chosen because of their effectiveness for multichannel data acquisition purposes as well as their good image resolution capabilities  (Dahlin and Zhou, 2004 ). On the one hand, DD arrays are well suited to image lateral features and allow efficient collection of reciprocal measurements. Exchanging current and potential electrodes should ideally deliver the same results, as stated by the reciprocity theorem  (Parasnis, 1988). Comparing forward and reciprocal measurements provides a robust method for estimating the data error and quality  (LaBrecque et al.
Data processing, quality control and error estimation We developed a semi-automated workflow involving routines for data acquisition, storage, filtering, inversion and visualisation. A first data filtering is applied on the repeatability error of each measurement. During the acquisition, the potential difference on the measurement dipole of each quadrupole is measured two to four times by the resistivity meter. Distributions of the repeatability error are shown in Fig. 3a. For DD arrays, data having repeatability with a SD (repeatability or stacking error) over 5 %, as well as measured potentials lower than 1 mV, are automatically filtered. Following this step, reciprocal errors are computed for the DD type data set. Reciprocal error is the resistance difference between normal and reciprocal measurement, i.
e. when current injection and potential dipoles are swapped. Figure  3b  shows the distribution of the reciprocal errors for the whole DD type data set, after filtering for repeatability errors and low potentials. Data with relative reciprocal errors over 20 % were also removed. Reciprocal errors are used as a noise estimate for the inversion procedure, where the resistance of each measurement needs to be weighted. Overall, filtering on repeatability error and reciprocal error leads to 15 % of all the DD measurements being rejected, mainly due to too-low mea-sured potentials. GD type surveys have no reciprocal measurement available for each daily data set. A punctual reciprocity test was performed on GD arrays and showed relative reciprocal errors slightly higher than those of DD arrays. This is attributed to the fact that GD surveys have a measured resistances range significantly broader than that of the DD surveys.
In dry periods, this leads to a higher number of rejected data after filtering. In August 2016, electrode #12, placed in the middle of the slope of the sinkhole, started to show significantly bad contact resistances (> 50 k ) which induced poor measurement quality. For time-lapse processing, all the quadrupoles comprising electrode #12 were therefore continuously rejected, which reduced the maximum number of measurements per survey and the resolution in that part of the ERT profile. Full surveys are composed of 990 DD reciprocal measurements (901 without electrode #12) and 1420 GD measurements (1296 without electrode #12). Figure  4b  and c  summarise the percentage of rejected measurements per survey for the monitoring period. Days with more than 10 % of rejected data were removed from the time-lapse data set and are therefore not shown in Fig.
4b  and c. Given the greater amount of rejected DD measurements, this results in 467 DD and 588 GD data sets. In December 2015, despite the fact that the monitoring site was equipped with AC power supply, the injection batteries started to fail because of the increased power demanded  by the resistivity meter to deliver higher voltages, especially for DD surveys. The battery malfunction led to the rejection of almost all the DD surveys acquired during that period. GD surveys were less sensitive to the problem because the power and voltage requirements were lower on average. The greater percentage of rejected data in 2015 is also attributed to the batteries being slightly less efficient for repeated highvoltage injections. The original starting type batteries were replaced by deep-cycle gel batteries in July 2016, especially designed for deep discharge, similar to those frequently used with solar panels.
The long drying period from August to October 2016 is responsible for a progressive increase in the amount of rejected data for both DD and GD surveys. Temperature variations of the subsurface can have significant impacts on resistivity data  (Brunet et al. , 2010). Because a marked temperature gradient is expected in the sinkhole, we modelled the 2-D temperature field using the framework of pyGIMLi  (Rücker et al. , 2017; www. pygimli. org) using data from a network of eight temperature probes installed along the ERT profile and a 105 cm vertical profile of five temperature probes. Correcting for the effect of temperature in time-lapse ERT can be implemented in several ways. We chose to apply the method described in  Hayley (2007)  in which both the data and the modelled resistivities are corrected for temperature effects.
Time-lapse inversion Data are inverted with Boundless Electrical Resistivity Tomography software (BERT), which is based on a finite element modelling  (Günther et al. , 2006; Rücker et al. , 2006). Each of the DD and GD data sets after filtering and correction for temperature effects constitutes one data set for the inversion, whereas the inverted results of the first data set of the DD and GD series constitute the reference model for the whole time series. The inversion is carried out using a L 1 (robust) data constraint to mitigate bias produced by outlying data on the convergence of the inversion and a L 2 (smoothness) model constraint. The time-lapse inversion procedure comprises an additional time regularisation constraint (λ t = 50) for the inversion of each subsequent data set, linking each of the inverted models to the reference model.
This is used to smooth over time the inverted results between each other, while facilitating the convergence of each inversion. Note that the structural assumptions presented in Sect. 5 are not used as model constraints in the inversion. This choice was made because of the uncertainty concerning the spatial continuity of structural observations in such a heterogeneous karst context. Time-lapse inversion achieved acceptable convergence between the observed data and the reconstructed model data as average RMS misfits of 11. 7 % (SD 1. 2 %) and 2. 4 % (SD 0. 2 %) for DD and GD arrays were respectively retrieved. Higher RMS errors of DD arrays are not really surprising. They are attributed to the lower signal-to-noise ratios of dipole-dipole arrays and the greater number of large dipoles configurations in the chosen DD protocol.
The chi-squared test is another way for evaluating the convergence of the inversion. A chi-squared value about 1 generally indicates that the error model is appropriately calibrated and does not lead to data underfitting or overfitting. Time-lapse inversion resulted in chi-squared values of 0. 87 (SD 0. 1) and 0. 84 (SD 0. 1) for DD and GD time series respectively. BERT provides a coverage parameter calculated from the sum of the absolute sensitivities of the measurements. Areas with high coverage are better constrained by the measurements and the modelling choices than low coverage areas. The coverage is usually incorporated in the data visualisation as a transparency mask that is used to weight the data depending on their associated coverage values.
The greater number of bedding planes could therefore provide more preferential pathways favouring rapid percolation of rainwater, compared to the porous limestone region. Both regions are correlated without any lag, with the deepest WCR probe and PWD2 data at lag 0 day, similarly with D1-type regions, with the matrix plateau region being even the most correlated with these variables. This confirms on a short timescale the greater contribution of these D2-type regions to the PWD2 seasonal drip discharge. The porous limestone and rainfall cross-correlation coefficient decreases progressively, being still negatively correlated at positive lag 2 and 3 days. The resistivity of the porous limestone still decreasing 2 to 3 days after a rainfall event is a possible explanation, highlighting a likely diffusion of the rainwater infiltration within the porous limestone layer.
Acknowledgements. This work is part of the  Karst Aquifer Research by Geophysics (KARAG)  project (karag. be)funded by the  Fonds de la Recherche Scientifique (FNRS-FRS). Arnaud Watlet  is also supported by the  Ernest du Bois Fund , managed by the  King Baudouin Foundation. We would like to thank all the colleagues who were involved in this project and particularly  Claudio Barcella  for field and laboratory works,  Christophe Bastin ,  Bérénice Deletter ,  Fadel Cissé ,  Stefaan Castelein ,  Marc Hendrickx ,  Giovanni Rapagnani ,  Sebastian Uhlemann ,  Fabian Wagner ,  Thomas Lecocq ,  Koen Van Noten  and  Thierry Camelbeeck , as well as the  ASBL Grottes de Lorette ,  Guy Evrard  and the team members, and the municipality of Rochefort for their hospitality and support. This paper benefited from Matplotlib (Hunter, 2007), Pandas (McKinney, 2011), Glue (available at http://glueviz.
Data availability. Data used in this research paper (comprising ERT, cave drip discharge, soil moisture, rainfall and water conductivity) are available at https://doi. org/10. 5281/zenodo. 1158631 (Watlet et al. , 2018a). ET 0 data are available via the Pameseb organisation (http://pameseb. be). The Supplement related to this article is available online at https://doi. org/10. 5194/hess-22-1563-2018supplement.