dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2002) , among many others. Other initiatives were focused on the intercomparison of LSMs: e. g. the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS;  Henderson-Sellers et al. , 1996)  and the Global Land-Atmosphere Coupling Experiment (GLACE;  Koster et al. , 2006). Recently, the Cloud-Roots field experiment (Vilà-Guerau de  Arellano et al. , 2020)  offered an integrated multi-scale approach from leaf to landscape measurements complemented with models. Some specific works have also focused on the effect of LC through the investigation of the impacts of improving the accuracy and resolution of LC databases used in the models (e. g. Pineda et al. , 2004; Cheng et al. , 2013; Santos-Alamillos et al. , 2015; Schicker et al. , 2016; Jiménez-Esteve et al. , 2018). Others have focused on modelling the changes that might occur under the assumption of possible future changes to the surface (e.
g. Li et al. , 2018; De Meij et al. , 2019). These studies stated the importance of having an accurate surface representation in the models to obtain improved simulations of different variables. In this sense, the present work was firstly motivated by the inaccurate representation of the LC provided by the default LC dataset (International Geosphere-Biosphere Programme from the Moderate Resolution Imaging Spectroradiometer, IGBP-MODIS) in the Weather Research and Forecasting (WRF) model over the area of analysis (southern France), which differed significantly from the LC observed in the area. We hypothesized that this would lead to errors in the simulated surface energy fluxes (specifically, sensible and latent heat fluxes). Also, the default configuration in WRF only uses the information from the tabulated surface parameters of the LC category with a higher percentage of coverage within each grid cell (dominant approach).
In order to strengthen the analysis, three widely used LSMs available in WRF were used: (1) Noah  (Chen and Dudhia, 2001) , (2) Noah-MP (Multi-Physics)  (Niu et al. , 2011) , and (3) RUC (Rapid Update Cycle)  (Smirnova et al. , 2016). The different experiments were designed as follows. First, we improved the LC in the area evaluated using the more realistic and higher-resolution LC dataset from the Centre d'Etudes Spatiales de la Biosphère research laboratory (CESBIO;  Inglada et al. , 2017). The results showed a high dependence of the flux on the specific LC categories, which motivated a second experiment including the sub-grid information on the surface, the so-called mosaic approach (e. g. Li et al. , 2013). Finally, an additional experiment was carried out due to the extreme biases found in the first two experiments over pixels mostly covered by conifer trees in Noah-MP, with the aim of diminishing the biases by modifying some parameters associated with the transpiration processes of this LC category.
Area-averaged flux (AAF) calculation The EC measurements from towers were used for the calculation of AAF with a resolution of 1 km in a region of 19 × 19 km around the central site of the field campaign: 43 • 07 27. 15 N, 0 • 21 45. 33 E; 600 m above sea level (a. s. l. ). This approach can be considered a mosaic observational method for flux computation. It was based on the multiplication of the values of the fluxes measured over each vegetation type by their respective vegetation cover fraction within each 1 × 1 km pixel (see Fig. 2a  and b ). Finally, the contribution from each vegetation type was summed to provide the final values in each grid cell (area-averaged fluxes). High-resolution and realistic LC data were needed to construct the AAF, which were obtained from the 30 m LC database prepared by CESBIO based on Landsat-5 data  (Inglada et al.
, 2017)  (Fig. 2b ). Hence, the main assumption of the AAF calculation was to consider the same SH and Le for sites with the same LC type at different locations, also implying the following simplifications (a further discussion is included after the list). 1. There were no flux differences depending on the soil type. 2. There was no difference in activity for the same vegetation type in the area. 3. There was no horizontal variability of the soil moisture (SM). 4. The radiation and the wind forcing was the same for all the pixels, without altitude influences. 5. All the forests were considered coniferous (evergreen needleleaf forest -ENF). 6. Fluxes over urban and bare soil surfaces were estimated using a simple model. (1) Two soil types were the dominant ones in the evaluation area based on the 1 × 1 km pixels from the soil database of WRF (United States Department of Agriculture, USDA): clay loam (81 % of the area) and loam (19 % of the area).
These simplifications could have led to some overestimation of the urban effect on the total fluxes, since the urban surfaces in the area were surrounded by gardens, prairies, and trees (so-called urban diffuse in the CESBIO LC dataset). However, the 30 m resolution LC dataset should be appropriate to deal with the urban and vegetation patches. Despite the high-quality data and the efforts made to reduce uncertainties in the AAF, the evaluation of models with surface observations is a necessary task which is not straightforward. The observational measurements are almost always linked to uncertainties that can add limitations to the evaluation process  (Bou-Zeid et al. , 2020). In our case, the mea-C. Román-Cascón et al. : Surface impacts on fluxes in WRF surements were taken at heights that implied homogeneous footprints, but even in this case, other uncertainties are always present  (Mauder et al.
DEFAULT experiment In this experiment we used the default configuration for the surface representation in WRF, i. e. the LC dataset from IGBP-MODIS (21 categories) and a dominant approach for the sub-grid heterogeneity, which means that the fluxes were calculated taking into account only the surface parameters of the dominant LC category, i. e. assuming that no sub-grid variability exists even when this information is available. The dominant LC category is the one with the highest percentage of coverage within each pixel. This method has an intrinsic dependency on the number of LC categories in the dataset. For example, a pixel with 40 % water, 30 % conifer forest, and 30 % deciduous forest will be treated as water, since it is the dominant category despite both types of forest covering 60 % of the total surface of the grid.
NEW-LC experiment In this experiment, we used a more realistic LC dataset than IGBP-MODIS, obtained from 30 m resolution data prepared by CESBIO  (Inglada et al. , 2017). Figure  2a  shows a satellite image of the area evaluated, which serves to visually validate the CESBIO LC at 30 m resolution (Fig. 2b ). The inaccurate representation of the pixel-dominant LC by IGBP-MODIS is revealed in Fig. 2c , and the more appropriate surface representation of the dominant LC categories for each pixel used in the NEW-LC experiment is shown in Fig. 2d. In order to incorporate the more realistic LC from CES-BIO in the WRF model, first, the different categories of the new LC dataset (CESBIO, 17 categories) were transformed into the most appropriate ones of the WRF default LC dataset (IGBP-MODIS). That is, the same 21 LC categories of IGBP-MODIS were conserved, following a similar approach as done in  Pineda et al.
EXPERIMENTS -The two possible types of crop in CESBIO (winter and summer crop type, i. e. wheat and corn, respectively) were transformed to the single cropland category available in IGBP-MODIS. Hence, we did not take advantage of the differentiation of crop type by CESBIO, but it is important to note that wheat surfaces only covered 2. 8 % of the analysed area (Fig. 2b ). -The four different urban categories in CESBIO were transformed to the single urban definition in IGBP-MODIS. Most of the urban surfaces of the area evaluated were defined as diffuse urban (8. 2 %) and industrial zones (0. 8 %). This new LC information was incorporated into the modelling system following the technical details included in Appendix B. Note how even using a more realistic LC dataset over the area, the sub-grid information was not used because the dominant approach was maintained in this experiment.
Noah The bias of the simulated SH and Le by the DEFAULT-Noah simulation relative to the observations (the AAF) is shown in Fig. 3a  and d , respectively. The results are presented for each LC category, taking into account the dominant LC of each grid cell (1 km) in the model. Note that the DEFAULT experiment includes 10 LC categories in the area based on the IGBP-MODIS database. The total bias values for SH and Le (represented as blue dashed horizontal lines) are close to 0 W m -2 due to the compensation effect when merging cells with positive and negative biases, but the SD has significant values due to these differences among LC categories, with some surfaces that systematically present noticeable biases. Pixels represented as WiF (wild forest) in the model (27 % of the total area) normally present an overestimation of the SH, while the mixed forest (MixF) pixels (14 %) underestimate it (Fig.
3a ). For the Le, the pixels covered by SaW (woody savanna, which represent 33 % of the area) are normally characterized by a remarkable Le underestimation, with bias values around -100 W m -2 (Fig. 3d ). The definition of some of the LC categories existing in the DEFAULT experiment did not appropriately represent the real LC of the area (as seen in Fig. 2c ), but they notably influenced the simulated model errors. This is the first indicator of the high dependency of the model results on the LC categories. Then, based on the main hypothesis, the results should improve using a more realistic LC representation (NEW-LC experiment). The all-area-averaged results for SH and Le from NEW-LC-Noah are shown in Table  3. In this experiment, the LC was modified towards a more realistic representation using the high-resolution data from CESBIO.
Hence, the SD and the RMSE have also been included. The SD was also diminished in the NEW-LC experiment, with less variability in the error in the new experiment. In the case of the RMSE (Fig. 3c  and f ), the results are shown by real LC categories (not model categories), whose fractions of coverage are very similar to those used in the NEW-LC experiment (slightly different due to small differences in the categories; e. g. corn and wheat in the real LC were merged into crop in the model). The SH RMSE (Fig. 3c ) improved for the total area when using the improved LC dataset from 56 W m -2 (DEFAULT-Noah) to 44 W m -2 (NEW-LC-Noah). The vertical bars in Fig. 3c  indicate the RMSE for each real LC category (the LC categories used in the AAF). They provide information about the types of vegetation associated with larger errors, i.
The results for Le RMSE (Fig. 3f ) also show significant improvements when using the improved LC dataset, except for pixels covered by urban area (with a small impact on the total values due to their scarce presence -2. 4 % -as dominant pixels and for which the results are out of the scope of this work). The Le improvement was more substantial in pixels covered by grass than by forest, since Le is slightly overestimated in the forest pixels (see ENF in Fig. 3e ). Notice how, besides the better LC pixel distribution in the NEW-LC experiment, we also avoided using some LC categories associated with large errors in the DEFAULT-Noah experiment, such as the unrealistic SaW pixels related to large Le underestimation (Fig. 3d ).
ERA-INTERIM experiment Not only can the SM have an important effect on the model results, but the rest of the atmospheric variables used to initialize the model can also impact the whole simulation. This possibility was studied using a different database to initialize the model (ERA-Interim versus NCEP-FNL). The comparison of the scores obtained with the ERA-INTERIM experiment slightly affects the evaluation: in Noah the bias changed from -17 to -16 W m -2 for SH and from 34 to 45 W m -2 for Le, leading to similar RMSE values. In the case of Noah-MP, the bias varied from 66 to 75 W m -2 for SH and from -42 to -39 W m -2 for Le, also with similar RMSE values. Therefore, although there is an impact of the initial conditions on the evaluation of the model, in this case the effects were not very large.
In order to add robustness to the study and to detect differences, the experiments were carried out using three LSMs available in WRF: Noah, Noah-MP, and RUC. First, a control experiment was performed with the default options in WRF: LC from the IGBP-MODIS database and a dominant sub-grid approach; i. e. the model used the tabulated surface parameters of the LC category with the highest percentage of coverage in each pixel. We hypothesized that these simulations were limited because of the large differences between the LC representation and the actual surface and because of the loss of LC information at the sub-grid scale. Thus, a new experiment was designed (named NEW-LC) that improved the surface representation by adding the LC information from the 30 m resolution CESBIO maps, which were much more accurate and realistic in the area than the default IGBP-MODIS dataset.
B3 FOREST experiment The three parameters modified in the FOREST experiment were changed in the MPTABLE. TBL file of WRF. This file contains the vegetation parameters tabulated from the different LC categories for the two LC datasets available in WRF. Since our simulations used the IGBP-MODIS LC dataset, we changed these parameters in the corresponding section within the file. Specifically, we modified MP from its original value (6) to 9, BP from 0. 002 to 0. 01 mol H 2 O m -2 s -1 , and V cmax25 from 50 to 62. 5 µmol m -2 s -1 for the LC type 1 (ENF). This was done before running the WRF simulation with the Noah-MP LSM.
Code and data availability. The source code of the Weather Research and Forecasting model (WRF v4. 1. 3) is available at https://github. com/wrf-model/WRF/releases (last access: January 2021). The initial and boundary data used for the specific analysed period can be downloaded at https://rda. ucar. edu/datasets/ds083. 2/ (last access: January 2021: NCEP (2000); last access: 10 January 2021) (https://doi. org/10. 5065/D6M043C6). Additional initial and boundary data (ERA-Interim) were also used, which can be downloaded at https://rda. ucar. edu/datasets/ds627. 0/ (last access: May 2021: ERA-Interim (2009); https://doi. org/10. 5065/D6CR5RD9). The CESBIO land use dataset (Inglada et al. , 2017)  for 2011 can be downloaded from the OSO CESBIO web page: http://osrcesbio. ups-tlse. fr/oso/posts/2016-10-06-cartes-2009-2011/ (last access: January 2021).
The BLLAST data are accessible at https://www7. obs-mip. fr/bllast/. All the data and scripts used in this work are available on Zenodo (Román-Cascón et al. , 2021) (https://doi. org/10. 5281/zenodo. 4449761), including (1) the area-averaged flux (AAF) data used to evaluate the model, with the data and scripts; (2) the scripts and data used to prepare the WRF experiments, including the modified geo_em*. nc files used to change the land cover of the domains; (3) the scripts and data used to process and analyse the WRF output; (4) the scripts used to prepare the figures; and (5) the WRF output for the domain used for each simulation in this work.
Aims. The Rosetta mission provided us with detailed data of the surface of the nucleus of comet 67P/Churyumov-Gerasimenko. In order to better understand the physical processes associated with the comet activity and the surface evolution of its nucleus, we performed a detailed comparative morphometrical analysis of two depressions located in the Ash region. Methods. To detect morphological temporal changes, we compared pre-and post-perihelion high-resolution (pixel scale of 0. 07-1. 75 m) OSIRIS images of the two depressions. We quantified the changes using the dynamic heights and the gravitational slopes calculated from the Digital Terrain Model (DTM) of the studied area. In particular, we measured seven geometric parameters associated with the two depressions (length, three width values, height, area, and volume) using the ArcGIS software before and after perihelion.
It is therefore an ideal region to study its present and past surface dynamics. The aim of this paper is to use a morphometrical approach to investigate the Ash region. We focus on two areas within the Ash region, studying them before and after perihelion to track surface changes. We perform this analysis in order to constrain, quantify, and better understand the landscape dynamic and evolution of the Ash region, aiming to find a generic eroding process that can apply to other nucleus regions. First, we describe the OSIRIS data set and the morphometrical approach we developed. Next, we present the results of our morphometrical analysis, followed by a pre-and post-perihelion comparison to discuss the observed changes. Finally, we propose a qualitative scenario for the dynamical evolution of the studied area.
Digital terrain model The NAC data set offers stereo coverage of the Ash region, used to derived a high-resolution digital terrain model (DTM) of this region via the method known as stereophotoclinometry (SPC;  Fig. 2c, 2d; Gaskell et al. 2008; Capanna et al. 2013; Jorda et al. 2016). However, since the SPC method mixes images from before and after perihelion, the resulting DTM represents a mean topography over the course of the Rosetta mission and does not track the surface changes that could have occurred at perihelion. Depending on the date and number of images used to generate the DTM, it either better represents the surface before perihelion or after perihelion. In our case we overlaid the DTM with NAC images acquired before and after perihelion, using the ArcGIS software (Fig. 3 ), to identify which ones provide the best fit to the Ash DTM.
Acknowledgements. OSIRIS was built by a consortium of the  Max-Planck-Institut fur Sonnensystemforschung, Katlenburg-Lindau, Germany; CISAS University of Padova, Italy ; the  Laboratoire d'Astrophysique de Marseille, France ; the  Instituto de Astrofisica de Andalucia, CSIC, Granada , Spain; the  Research and Scientific Support Department  of the  ESA, Noordwijk, Netherlands ; the  Instituto Nacional de Tecnica Aeroespacial, Madrid, Spain ; the  Universidad Politechnica de Madrid, Spain ; the  Department of Physics and Astronomy of Uppsala University, Sweden ; and the  Institut fur Datentechnik und Kommunikationsnetze der Technischen Universitat Braunschweig, Germany. The support of the national funding agencies of  Germany (DLR) ,  France (CNES), Italy (ASI), Spain (MEC), Sweden (SNSB) , and the  ESA Technical Directorate  is gratefully acknowledged.
, 2017). The proposed bi-static Harmony SAR mission aims to provide new data for a wide range of applications, such as ocean-atmosphere interaction, hurricanes, and solid Earth and land ice studies  (López-Dekker et al. , 2019). Harmony also has the capability to overcome several limitations of current sea-ice observing systems. The Earth Explorer 10 candidate mission consists of two companion satellites (Concordia and Discordia), which will fly in formation with one of the Sentinel-1 satellites (from here on it is assumed to be Sentinel-1D). The satellites will carry a multi-angle thermal infrared sensor and two passive instruments that will receive signals from a scene illuminated by the radar of Sentinel-1D. In the stereo phase of the mission, the Harmony satellites will trail and lead Sentinel-1D, which creates line-of-sight diversity.
This is not possible with XTI using two monostatic systems or with repeat-pass interferometry, due to the decorrelation of the surface. In both XTI and stereo formation, SAR spectra are obtained from multiple lines of sight which will improve the wave spectrum retrieval. The objective behind this article is to describe the expected performance and the application of the Harmony mission for sea-ice dynamics, i. e. sea-ice drift and waves. Harmony data also enable estimates of sea-ice topography, but this will not be addressed in this paper. We will introduce the bi-static geometry and the observation concept and show how to derive sea-ice drift fields from the stereo configuration and wave spectra in both the stereo and the XTI configurations. Harmony observations are modelled based upon seaice drift estimates from a sea-ice model, and noise is added using electromagnetic models for the noise-equivalent sigma zero (NESZ) and the backscatter coefficient over sea-ice.
Wave spectra The software package OCEANSAR (https://github. com/ pakodekker/oceansar, last access: 1 June 2020) is used to demonstrate Harmony's ability to measure swell waves in sea-ice-covered regions. Harmony data can be used in two ways to extract wave spectra. If Harmony flies in the XTI configuration, the phase differences between the two satellite systems are a "direct" measure for the height. In both the XTI and stereo formations, the image distortions due to velocity bunching provide a way of inferring wave information from intensity spectra. To the first order, a bi-static observation can be modelled as a monostatic system located at the midpoint between the transmitter and receiver. In this paper, we consider a system of one transmitter and two receivers (XTI mode) so that we have a monostatic and a bistatic system of which the bi-static receiver is located across track.
This also implies that both systems have a common zero Doppler, which makes the interpretation easier. Using OCEANSAR, the RAW signals are modelled using scatterers at a 2 m resolution Lagrangian grid; i. e. the swell waves cause three-dimensional changes to the surface during the burst. As the small waves are quickly damped by the sea ice  (Stopa et al. , 2018) , the surface is assumed to be correlated over the burst length. The average SNR is set to approximately 5 dB, and the bi-static baseline is set to 1000 m. The data are then focused, and they have a resolution comparable to that of the Sentinel-1's interferometric wide-swath (IW) mode. From the single-look complex images, intensities and interferometric phases are computed. These are multilooked using a Hanning window with a size of 12 × 4, which yields a resolution of about 65 m×65 m, since the simulated resolution is approximately 16 m in the azimuth and 5 m in the ground-range direction.
Code and data availability. The software code will be made available as part of the Stereoid software package, which can be found at https://gitlab. tudelft. nl/drama/stereoid (last access: 1 July 2021) and the version used for generating the results of this paper are available at: https://doi. org/10. 5281/zenodo. 5055737 (Kleinherenbrink, 2021). The nExtSIM sea-ice model data will be made available on request.
5 ag/kg to 30 ag/kg (1 ag/kg = 10 -18 g/L; 1 dpm/m 3 = 6. 23 ag/kg) and the need for large seawater volumes that are needed to perform each measurement, limiting the use of this tracer. Initially, 227 Ac measurements were made by alpha-spectrometry after processing of several hundred litres of seawater  (Geibert et al. , 2002; Nozaki, 1984). Subsequent developments on alpha spectrometry allowed (1) reducing sample size below 100 litres and (2) using 225 Ac as yield tracer  (Bojanowski et al. , 1987; Geibert and Vöge, 2008). Recently, 227 Ac analysis by Radium Delay Coincidence Counting (RaDeCC) was introduced, with actinium being concentrated on cartridges with in situ pump, requiring thousands litres of seawater (Le  Roy et al. , 2019; Shaw and Moore, 2002, Kipp et al, 2015). Since then, RaDeCC has become the only method used to analyse 227 Ac in seawater within the GEOTRACES international program  (Geibert, 2015).