dataesr/scientific-paragraphs-categorization huggingface dataset:
DEM quality The quantitative assessment of the DEM quality is reported in Table  3. The error statistics demonstrated good agreement between the kite DEM and the independent DGPS-surveyed validation data set. In particular, the mean error and median error were smaller than the pixel size and the standard deviation of the error was of the order of the pixel size. These figures show that the DEM acquired by kite constitutes a reliable model of the catchment topography. Moreover, a qualitative assessment of the kite DEM was carried out with a manual inspection of full-resolution DEM shaded views with three close-ups (Fig. 8 ). The assessment showed that the kite DEM planimetric and altimetric resolutions allow for the visual detection of numerous landscape features, including most man-made structures (roads, tracks, buildings) and gully heads that were identified in the field (e.
Gully network map and 3-D gully morphology Although the overall accuracy of 74 % proved that our method was effective, the very process of validating the gully maps obtained from high-resolution DEM processing raises issues. To begin, validation methods are quite varied in the literature. Most authors  (Evans and Lindsay, 2010; Baruch and Filin, 2011; Höfle et al. , 2013; Castillo et al. , 2014)  have used manual digitisation of the gullies on the DEM as validation data and focused on different gully characteristics: width and depth  (Evans and Lindsay, 2010) , visual comparison  (Baruch and Filin, 2011; Höfle et al. , 2013) , and areal and volume difference  (Höfle et al. , 2013; Castillo et al. , 2014). Some authors (e. g. Noto et al. , 2017)  did not even validate the gully mapping results.
Computing time is indeed one issue for DEM processing: for instance  Castillo et al. (2014)  have been unable to process the full resolution of their largest DEM. Considering that upcoming topographic data sets will probably be more extensive and will have higher resolutions, this may still be an issue that will have to be mitigated by algorithmic improvements such as the one we have proposed. The interest in multi-scale approaches is as strong as the 3-D information of such DEMs is rich, which is the case for the data obtained in our study. The comparison of dense elevation profiles between the kite DEM and ground reference hence showed good agreement. These findings thus confirmed, at a very local scale, the results on DEM accuracy found at the scale of the whole DEM. However, this detailed analysis raised the issue of vegetation cover.
Acknowledgements. The OMERE observatory (http: //www. obs-omere. org,last access: 29 May 2018), funded by the  French institutes INRA  and  IRD  and coordinated by  INAT Tunis ,  INRGREF Tunis ,  UMR Hydrosciences Montpellier  and  UMR LISAH Montpellier , is acknowledged for providing a portion of the data used in this study. In particular, we gratefully acknowledge  Kilani Ben Hazzez M'Hamdi ,  Radhouane Hamdi  and  Michael Schibler  from  IRD Tunis  for the work that was carried out in the field to obtain topographic data. This research was also supported by the  TOSCA-CNES  project " A-MUSE; Analyse MUlti-temporelle de données SENTINEL 2 et 1 pour le monitoring de caractéristiques observables de la surface du sol, en lien avec l'infiltrabilité "( 2018-2019 ). Edited by:  Daniele Giordan Reviewed  by:  Mitchell Bryson  and four anonymous referees.
Their tortuosity and porosity were measured using classical methods  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]  (Table  1 ). We solved the inverse problem for these samples via waves reflected at the first interface and for different incident angles. Experiments were performed in air with two broadband Ultran NCT202 transducers with a 190 kHz central frequency in air and a 6 dB bandwidth extending from 150 to 230 kHz. Pulses of 400 V were provided by a 5052PR panametrics pulser/receiver. An optical goniometer was used to position the transducers. The received signals were amplified to 90 dB and filtered above 1 MHz to avoid high-frequency noise. Electronic interference was removed by 1000 acquisition averages. The experimental setup is shown in Figure  5. Figures  6  and 7  show a variation of the cost function, U, with tortuosity and porosity, for samples M1, M2, and M3, respectively.
, 2017). The effects of scavenging/desorption on the isotopic composition of dFe are however not yet clear. While one study found that scavenging resulted in the preferential uptake of heavy Fe onto particles (D 56 Fe dFe-scavFe = À0. 67‰; Ellwood et al. , 2015) , results from another study indicated that differences in d 56 Fe values of scavenged Fe relative to dFe were only small (D 56 Fe dFe-scavFe = +0. 3 ± 0. 3‰;  Radic et al. , 2011). To constrain the processes that regulate the behaviour of Fe within OMZs, we have determined the isotopic signature of dFe from four water column profiles in the West African shelf and slope region of the tropical North Atlantic Ocean. We use our data to identify the sources of dFe in the OMZ and assess the effects of internal processes, such as Feligand formation, biological uptake, remineralisation and scavenging, as well as water mass transport and mixing, on the distribution of Fe.
Auxiliary data A Sea-Bird 911 plus CTD sensor, fin-mounted secondary temperature and conductivity sensors, a Digiquartz pressure sensor, a fluorometer sensor and a transmissiometer sensor were mounted on the titanium rosette frame. Sensors were cross-calibrated with discrete seawater analyses using the Winkler method for oxygen  (Carpenter, 1965)  and conductivity measurements of a certified reference material for salinity. Chlorophyll-a content was monitored using a fluorometer fitted on the rosette frame and the manufacturer's calibration was applied. More details on sensors and calibration methods employed during D361 can be found at the British Oceanographic Data Centre (www. bodc. ac. uk). Concentrations of seawater nitrate, nitrite, silicate, ammonium and phosphate were determined on board the RRS Discovery, using a 5-channel segmented flow autoanalyser (Bran and Luebbe AAIII)  (Woodward and Rees, 2001).
ACKNOWLEDGEMENTS We gratefully acknowledge the support of the captain and crew of RRS Discovery throughout cruise D361. We also thank  NMF  staff for their technical assistance onboard. We thank the rest of the trace metal sampling team  Maeve C Lohan ,  Angela Milne ,  Rosie Chance  and  Felix Morales  for sharing the hard work on-board. We thank  Eithne Tynan  for pH data and  Alexander Forryan  for help with CTD data. We are grateful for the constructive comments from  Olivier Rouxel  and three anonymous reviewers, which have significantly improved this manuscript.
Airborne measurements of CO 2 , CO, and CH 4 proposed in the context of IAGOS (In-service Aircraft for a Global Observing System) will provide profiles from take-off and landing of airliners in the vicinity of major metropolitan areas useful for constraining sources and sinks. A proposed improvement of the top-down method to constrain sources and sinks is the use of a multispecies inversion. Different species such as CO 2 and CO have partially overlapping emission patterns for given fuel-combustion related sectors, and thus share part of the uncertainties, both related to the a priori knowledge of 5 emissions, and to model-data mismatch error. We use a regional modeling framework consisting of the Lagrangian particle dispersion model STILT (Stochastic Time-Inverted Lagrangian Transport), combined with high resolution (10 km x 10 km) EDGARv4.
3 (Emission Database for Global Atmospheric Research) emission inventory, differentiated by emission sector and fuel type for CO 2 , CO, and CH 4 , and combined with the VPRM (Vegetation Photosynthesis and Respiration Model) for biospheric fluxes of CO 2. Applying the modeling framework to synthetic IAGOS profile observations, we evaluate the 10 benefits of using correlations between different species' uncertainties on the performance of the atmospheric inversion. The available IAGOS CO observations are used to validate the modeling framework. Prior uncertainty values are conservatively assumed to be 20%, for CO 2 and 50% for CO and CH 4 , while those for, GEE (Gross Ecosystem Exchange) and respiration are derived from existing literature. Uncertainty reduction for different species is evaluated on a domain encircling 50% of the profile observations' surface influence over Europe.
Discussion started: 1 March 2017 c Author(s) 2017. CC-BY 3. 0 License. produced extensive datasets that have proven to be important in the fields of atmospheric modeling and satellite calibration and validation. Regarding carbonaceous species, CONTRAIL has so far been collecting CO 2 mixing ratio measurement, while IAGOS was focused on CO. In the next years the IAGOS fleet will simultaneously provide CO, CO 2 and CH 4 atmospheric concentration measurements  (Filges, 2015) , enabling the use of multi-species synergy in modeling applications. This synergy follows from the fact that the collocated measurements share the same atmospheric transport and have partially 5 correlated emission uncertainties. This paper is focused on investigating the benefits on uncertainty reduction of such a multi-species inversion in comparison with a single-species inversion.
To attain this goal, we set up a synthetic experiment utilizing the measurement times and locations collected from the IAGOS projects in the year 2011. The present paper is intended to pave the way for future 10 studies making use of multi-species IAGOS datasets when they become available. A receptor-oriented framework was set up to derive flux interactions between the atmosphere and the biosphere using IAGOS data. The modeling framework is composed of a Lagrangian Particle Dispersion Model (LPDM, specifically the STILT model), a diagnostic biosphereatmosphere exchange model (the VPRM model), gridded emission inventories, global tracer transport model output that provides the tracer boundary conditions for the regional domain, and a Bayesian inversion scheme. The present work is 15 based on the modeling framework used in  Boschetti (2015)  and builds upon that by adding other species, and using a formal Bayesian inversion.
Vertical profile input data In this study the modeled profiles have the identical structure to those collected from the IAGOS fleet of commercial airliners. More precisely, the spatial and temporal coordinates of different observations will be used as input for the 15 modeling framework whereas the observed values of atmospheric mixing ratios of CO and meteorological parameters themselves will play a role in calibrating the modeling framework. Central for this work is the concept of the Mixed Layer (ML), the lower part of the troposphere in which trace gases are well mixed due to turbulent convection in the time scale of an hour or less, and in which the effect of regional surface-atmosphere 20 fluxes is the strongest. As input to the inversion we use the enhancement of the species' mixing ratio within the mixed layer relative to that in the free troposphere (FT), similar to the approach described in  Boschetti (2015).
Transport-flux coupling The modeling framework is composed of a regional transport model (STILT), the EDGAR (Emission Database for Global Atmospheric Research) emission inventory to model anthropogenic emissions, VPRM (Vegetation Photosynthesis and Respiration Model) to model emissions from the biosphere and output from global transport models for lateral boundary 5 conditions for the different modeled species. The expressions 'anthropogenic emissions' and 'fossil fuel emissions' are considered synonymous in this paper and are used to indicate the sum of fossil fuel and biofuel emissions, without including contributions from LULUCF (Land Use, Land use Change and Forestry). For regional transport we make use of the LPDM STILT (Stochastic Time-Inverted Lagrangian Transport)  (Lin, 2003)  to 10 derive the sensitivity of the atmospheric mixing ratio measurement to upstream surface-atmosphere fluxes, so-called "footprints".
Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017-69, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 1 March 2017 c Author(s) 2017. CC-BY 3. 0 License. perform the maintenance since 1994. In its last 10 years of operations MOZAIC has been funded by INSU-CNRS (France), Météo-France, Université Paul Sabatier (Toulouse, France) and Research Center Jülich (FZJ, Jülich, Germany). The IAGOS database is supported by AERIS (CNES and INSU-CNRS). Data are also available via AERIS web site http://www. aeris-. Phys. Discuss. , doi:10. 5194/acp-2017-69, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 1 March 2017 c Author(s) 2017. CC-BY 3. 0 License.
Acknowledgements The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( [FP7/2007([FP7/ -2013]])  under grant agreement n°  312311  (IGAS). The authors acknowledge the strong support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Air-France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) who carry the MOZAIC or IAGOS equipment and.
25 30 25 Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017-69, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 1 March 2017 c Author(s) 2017. CC-BY 3. 0 License. perform the maintenance since 1994. In its last 10 years of operations MOZAIC has been funded by INSU-CNRS (France), Météo-France, Université Paul Sabatier (Toulouse, France) and Research Center Jülich (FZJ, Jülich, Germany). The IAGOS database is supported by AERIS (CNES and INSU-CNRS). Data are also available via AERIS web site http://www. aeris-. Phys. Discuss. , doi:10. 5194/acp-2017-69, 2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 1 March 2017 c Author(s) 2017. CC-BY 3. 0 License.
Basilisk and the Grid Adaptation Algorithm The AMR runs are performed with the partial-differential-equation solver called Basilisk, a code that contains a second-order accurate finite-volume solver for the Navier-Stokes equations. For a detailed description of the numerical formulations see  Popinet (2003 Popinet ( ,2009)) ,  Lagrée et al. (2011) , and references therein. In order to facilitate local adaptive refinement and coarsening whilst maintaining a Cartesian grid-structure, a so-called tree-based grid is used. To illustrate this mesh structure, Fig. 2  shows the two-dimensional (2D) variant of a tree-based grid (i. e. a quadtree), whose structure introduces a hierarchy between cells at integer levels of refinement. The resolution between the levels of refinement differs by a factor of two and the Basilisk solver allows neighbouring cells to vary up to one level.
An Example of the Adaptation Algorithm This section aims to exemplify how the adaption algorithm assesses a discretized signal and adapts the grid according to a refinement criterion ζ. For this purpose, we apply the algorithm to a subset of the data from the simulation of forced isotropic turbulence in  Li et al. (2008). The simulation is run using a fixed equidistant grid with 1024 3 nodes; in terms of the Kolmogorov length scale (η), the grid spacing (∆ i ) is ∆ i = 2. 2η. For the analysis we assume the data to be resolved well enough, and the results are kindly made available via the Johns Hopkins turbulence databases (http://turbulence. pha. jhu. edu/). We analyze a 2D slice of the data (i. e. 1024 2 cells) and for simplicity, we only consider the velocity component perpendicular to the sliced plane (u ⊥ ).
As such, we only allow the algorithm to coarsen the field with a maximum error threshold ζ (as defined in Eq. 4). The number of grid cells resulting from the application of the adaptation algorithm for a range of ζ values is shown in Fig. 5c ; as expected, the number of grid cells decreases with an increasing ζ value. Note that the plot also shows that even for the high ζ values, the grid still contains cells at the maximum resolution. The main concept of employing the described grid-adaption algorithm is visualized in Fig. 5d. Here histograms of the number of grid cells within 512 equallyspaced χ bins are presented for the original data and the data obtained from applying the grid adaptation technique with three different refinement criteria. It appears that for the original dataset, the histogram is monotonically decreasing with increasing χ.
MicroHH: https://github. com/microhh/microhh/tree/master/cases/vanheerwaarden2016 1 From a users' perspective, the case definition for the adaptive-grid runs is (subjectively) not more involved than the fixed-grid counterpart. The more complex adaptation-specific formulations are addressed by a low-level part of the Basilisk toolbox that does not require explicit attention from the users' side.
Therefore, the results presented here are intended as an illustration rather than as absolute values. Nevertheless, it is clear that the MicroHH run is notably cheaper compared to the runs performed with the Basilisk solver. This can be explained by the different numerical schemes that are employed. Most notably, for obtaining the pressure field, the Basilisk code uses a multigrid strategy for solving the corresponding Poisson equation whereas the isotropic-fixed grid in MicroHH facilitates the usage of a spectral Poisson solver. Although the spectral method requires more MPI communication for parallelization when using a large number of processors, it is known to be more efficient  (Fornberg 1998 ). If we compare the adaptive and non-adaptive simulation runs performed with the Basilisk solver, we do see a considerable decrease in costs for the adaptive method runs.
In Fig. 10d  the memory used for the different simulation runs is presented. Compared to the fixed-grid runs, the adaptive-grid runs require less memory. This is due to the fact that the maximum number of grid cells is considerably lower than the number of grid cells in the fixed-grid runs (see Fig. 10a ). From this perspective, the adaptive-grid approach can also be attractive for applications where the available memory is limited. However, even though the run with MicroHH employs many more grid cells, the required memory is comparable to that of run BA-0. 0025, meaning that per grid cell, the MicroHH code is more efficient in terms of memory. 4 Outlook: Towards Adaptive Mesh Refinement in Atmospheric LES We have based our test and performance benchmark on an idealized flow configuration of a CBL using DNS, providing a ground truth for our intercomparison.
Note that this concept also applies to three-dimensional turbulence (see  Chap. 8 in Frisch, 1995). However, the corresponding scaling behaviour for ABL cases is not obvious. Van Heerwaarden CC, van Stratum BJ, Heus T, Gibbs JA, Fedorovich E, Mellado JP (2017) Microhh 1. 0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows. Geosci Model Dev 10(8):3145-3165 Table  1  Overview of the different simulation run details. In the top section a reference name, the used solver, grid type, the (maximal) numerical grid resolution, lateral boundary conditions and refinement criterion (ξ b , if applicable) are listed for each run. In the bottom section the used number of cores, the total amount of integration steps taken at t/T = {10, 45} and the total wall clock time of each run are presented.
Calibrations including δ 18 O seawater The WW72 dataset does not take into account δ 18 Oseawater. Juillet-Leclerc and Schmidt (2001) included annual δ 18 O SeaWater values assessed in the calibration established for Porites following the formula: δ 18 O carbonate -δ 18 Oseawater = α × SST(°C) + β Eq. ( 3 ) where α and β are constants. They obtained: δ 18 O carbonateδ 18 Oseawater = -0. 20 × SST(°C) + 0. 45 Eq. (  4 ) with R 2 = 0. 83, N = 22, p < 0. 001, only significant over the SST range from 24 to 30 °C  (Juillet-Leclerc and Schmidt, 2001) , by introducing δ 18 Oseawater following two estimates (Fig. 2 ). The correlation linking δ 18 O directly to temperature showed a higher coefficient: δ 18 O carbonate = -0. 27 × SST(°C) + 2. 24 Eq. (  5 ) with R 2 = 0. 91, N = 24, p < 0. 001 (Fig. 1c ) including the lowest temperatures neglected in Eq.
Relationship between constants a and b WW72 data reveal a strong relationship between annual δ 18 O-annual temperature calibrations and taxonomy because each genus shows a unique relationship. Calibration bundles defining groups (Table  1 ) enhance this feature. Coral genera classification or taxonomy is based on coral morphology. Land et al. (1975)  stressed the high δ 18 O variability following the longitudinal section on the calices of Eusmilia fastigiata or the septa dentations of Scolymia cubensis inducing coral skeleton isotopic variations. The authors observed that according to coral location some skeleton portions might be more or less developed, implying a large isotopic variability. Considering the relationship δ 18 O carbonate = a x SST (°C) + b Eq. (  2 ) derived from the WW72 dataset, (a), the slope value, varies from -0.
temperature ranges (Fig. 1d ). Data Center site) (https://www. ncdc. noaa. gov/paleo/study/1877). On Fig. 4a  are reported seasonal isotopic profiles from 1967 to 1993 time period for 92PAC coral core (blue curve), 92PAD coral core (pink curve), 99PAA coral core (green curve) and 92PAA coral core (violin curve). All the cores have been harvested at Fort Amédée lighthouse proximity. Seasonal isotopic profiles are strongly impacted by seasonality with different light influence. Fig. 4b  displays seasonal δ 18 O-seasonal temperature (GISS SST) calibrations for the coral cores studied.
Figure4-Graphs derived from Stephans et al. (2004)  data, available on NOAA (National Climatic Data Center site) (https://www. ncdc. noaa. gov/paleo/study/1877). On Fig. 4aare reported seasonal isotopic profiles from 1967 to 1993 period for 92PAC coral core (blue curve), 92PAD coral core (pink curve), 99PAA coral core (green curve) and 92PAA coral core (violin curve). All the cores have been harvested at Fort Amédée lighthouse proximity. Seasonal isotopic profiles are strongly impacted by seasonality with different light influence. Fig. 4bdisplays seasonal δ 18 O-seasonal temperature (GISS SST) calibrations for the coral cores studied. 92PACδ 18 Ocarbonate = -0. 17 x SST (°C) -0. 08, R 2 = 0. 77, n = 296, p<0. 001, blue curve 99PAA δ 18 Ocarbonate = -0. 16 x SST (°C) -0. 39, R 2 = 0. 67, n = 296, p<0.
and annual δ 18 O ranges corresponding to the intersection of the calibrations. This feature is made 1170 possible by the homogenous light influence on calibrations. , https://doi. org/10. 5194/bg-2018-433 Manuscript under review for journal Biogeosciences Discussion started: 19 October 2018 c Author(s) 2018. CC BY 4. 0 License. , https://doi. org/10. 5194/bg-2018-433 Manuscript under review for journal Biogeosciences Discussion started: 19 October 2018 c Author(s) 2018. CC BY 4. 0 License. calibrations, δ 18 Ocarbonate = a x SST (°C) + b. Weber and Woohead (1972)  data series provided coral data. Fig. 3adisplays constants values from the 44 coral genera of Table1. (a) is considered as the disequilibrium indicator compared to -0. 19, the slope value derived from the theoretical δ 18 Otemperature relationship at equilibrium (Kim et al.