dataesr/scientific-paragraphs-categorization huggingface dataset:
As such, there has been recent interest in using VOD to assess biomass changes  [37, 39, [178] [179] [180] [181]  or plant water stress strategies  [177]. The VOD might prove especially useful in regions where soil moisture retrieval is especially complicated by dense vegetation coverage (e. g. , forested landscape). C or X-band do not penetrate much into the canopy and are more sensitive to the top part of the canopy. Figure  15  shows that L band is sensitive up to very dense canopies (300 Mg/hectare) enabling the probing of almost all vegetation canopies  [182]. A further advantage of the VOD is that data can be acquired even in the presence of clouds. C-band senses a thicker layer of the canopy and branches so that the signal somewhat differs from the X-band signal especially in humid regions with dense canopy coverage such as the Amazon rainforest (Figure  16 ).
The lower frequency signal, in L-band, senses a thicker vegetation layer corresponding to the above ground biomass such as the ones derived in [183] (Figure  15 ). Most of our observations of the land surface relate to surface variables (soil moisture, biomass, vegetation coverage among others) but ultimately one of the key objectives is to better constrain the vegetation water, energy and carbon fluxes. Recently, direct observations of a proxy for photosynthesis have been made possible, at the global scale using Solar-Induced Fluorescence (SIF)  [184] [185] [186] [187] [188] [189] [190] [191] [192] ). SIF is a by-product of photosynthesis, which is directly related and even nearly-proportional to photosynthesis in most conditions and is a direct indicator of vegetation stress  [156]. One of the advantages of SIF is that it provides information that is not included in the other datasets (e.
Orography For every grid point, models have associated values for surface elevation (orography), sub-grid orography statistics, land cover (used as land/sea mask), lake cover and depth, glacier cover, low and high vegetation type, low and high vegetation cover, albedo, LAI, and soil texture. Within the ECMWF system, these fields are derived from different external sources (e. g. , albedo and LAI from MODIS, vegetation type and cover from GLCC/AVHRR, water bodies from Globcover). Global datasets come in different resolutions, data formats and projections and need to be interpolated, or merged in the case of non-global coverage. In light of the changing climate signal also affecting surface physiography, multi-year climatological data at 1 km resolution for all surface fields is desirable. One-km resolution represents a challenge for current global NWP and Earth system models, but it is relevant to allow direct comparison with present and future Sentinel missions monitoring the Earth system.
Soil Depth While earth-observing satellite data have been widely used in modeling land processes (e. g. , global land cover  [210] ), in situ measurements as constrained by satellite observations are needed to estimate other variables. One such variable is the Depth To Bedrock (DTB), which is essential for accurate land surface modeling of the energy, water, carbon cycle, and dynamic vegetation. In general, a lower boundary condition is needed to solve the governing equation for vadose zone soil moisture. The presence of bedrock is a player in soil hydrology as can change the water flow and storage  [211]. Earlier land models assume a free drainage bottom condition (i. e. , without considering groundwater), while more recent models include an unconfined aquifer which implicitly assumes a globally constant bedrock depth.
However, no conditions are satisfactory without a DTB estimate  [212]. Ref. [213]  has recently developed the world's first global 1 km soil and alluvial thickness dataset (Figure  18 ) by combining geomorphologic theory with the best available data for topography (using satellite elevation measurements), climate, ecosystem  [210] , and geology as input. This dataset utilised different approaches to separately estimate soil and alluvial thickness for upland hillslopes, upland valley bottoms, and lowlands, as the character of soil depth is fundamentally different for these units: upland hillslopes have relatively shallow soils (∼1 m), while valleys and lowlands have relatively deeper soils/alluvium (10 s of meters or more) (Figure  18 ). These three units are distinguished at the 90 m pixel scale using a valley network extraction algorithm as well as criteria related to geologic age.
On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U. S. and Europe and on lowlands using DTB observations from groundwater wells in the U. S. This dataset is publicly available at: https://daac. ornl. gov/SOILS/guides/Global_ Soil_Regolith_Sediment. html. A similar product based on global spatial ensemble prediction models with various input datasets is also available  [214]. The impact of this DTB dataset on land surface modeling has been demonstrated  [215]  implementing variable DTB in the Community Land Model (CLM4. 5) with 0. 9 • latitude x 1. 25 • longitude grid boxes. The greatest changes in the simulation with variable DTB are to baseflow, with the annual minimum generally occurring earlier in the year comparing to fixed soil depth simulations.
In  [235] , the same idea was used and lake depth of shallow sub-Arctic lakes and ponds was determined by using Landsat Thematic Mapper (TM) and European Remote Sensing (ERS)-1 Synthetic Aperture Radar (SAR) data. Landsat TM image is used to map lake bathymetry and multi-date ERS-1 images acquired during winter are utilised to determine when and which lakes freeze to the bottom during winter. Lake depth estimates computed by this approach correspond well with in situ measurements, especially in the tundra zone; however, the approach needs to be further tested and improved for automatic use  [235]. While at the moment still no complete global lake depth dataset exist, local bathymetry dataset can be found at least for large lakes (see Section 6. 3. 4 of the GCOS report  [60] ). Beyond a few large lakes, the collection of local datasets is a difficult task; therefore, the most common practise for lake modeling in global NWP is to use in situ measurements and indirect estimates of lake mean depths based for instance on the geological origin of lakes  [236].
Merits and limitations of the tiling methodology when accommodating lakes and forest within the same model grid-box have been assessed by  [238]. They conclude that the tiling method captures well the influence on surface fluxes of the contrast between the "lake" and "forest" surface boundary conditions, on seasonal and diurnal time scales (see Figure  20 , upper left and upper right panels, respectively). The behavior of simulated lake surface temperatures (Figure  20 , bottom panel) and the period of ice cover in large inland water bodies worldwide was verified using satellite-based products (see  [230] ). Particularly relevant for validating lake simulations (temperature and ice cover) in NWP is, for example, data provided by the ATSR Reprocessing for Climate, ARC-Lake lake surface water temperature and ice cover dataset  [239].
Satellite-derived observations from ARC-Lake have been used in recent years to validate simulations from the Canadian small lake model within the Canadian land surface scheme  [240] , global lake temperature simulations performed within the CNRM-CM5 climate model  [232] , and in validating the coupling of FLake to the UK Met Office Unified Model  [233] , among others. The impact of introducing interactive inland water bodies in ECMWF's IFS has been examined by a set of dedicated analysis experiments. However, the lake model (FLake,  [231] ) does not currently consider a mass balance of water (only for the ice components), whereby a static dataset is used to represent the extent and bathymetry of the world's lakes. Monitoring lake depth globally is not currently achievable although inversion methods (e.
Similarly, a dynamic soil moisture global dataset based on EO data  [255]  has been obtained by characterizing the dry-down trends. Since EO soil moisture only captures near surface water, it can only be used for two of the three evaporation components (soil surface and interception) and not the larger transpiration. Ref. [256]  quantified the power spectra of rainfall, EO soil moisture and modelled soil moisture allowing for inferring the low-pass filter effect of soil hydrology. Neither of these analyses have yet been used to evaluate land-surface model systematically, but the principle shows great promise. These new methods can further be backed up with in situ observations such as drying rates from Flux Tower or soil moisture sensors. Producing accurate forecasts of near-surface parameters becomes increasingly important as near-surface temperature, humidity and wind are used in a widening range of applications and are themselves highly relevant during extreme events (e.
Recent advances in EO products are allowing model developments to be compared across the entire Arctic domain rather than just using limited in situ data at a single point. For the Arctic cryosphere, this is also important as the region is changing so rapidly. Field campaigns that have provided large datasets for model development such as SHEBA  [279]  may well be less representative of the ice conditions in recent years as much of the multi-year ice has been lost. There are several products that are available operationally to describe the sea ice field. They are derived from brightness temperatures from various instruments and scatterometer data. These describe: the cover in terms of concentration, type, and edge; physical properties such as emissivity, skin temperature and thickness; and the dynamic properties in the form of motion (drift).
Towards Enhanced Global-Scale Local-Relevant Monitoring and Forecasting Global monitoring and forecasting for environmental, weather and climate applications needs an accurate specification of the Earth surface as boundary condition to the atmosphere. This need can be satisfied at various levels of precision and complexity. However, some properties do not receive enough attention or they are simply neglected and therefore introduce errors in the surface representation. Others are approximated, due lack of information, by drastically simplified biogeophysical processes, typically when satellite datasets are not used. To enhance global-scale forecasting that maintains high relevance at local scale, it is necessary to pay more attention to the mapping of surface properties and parameters, in association with the parameterisation schemes used.
g. , reservoirs) and others as a result of climate change (e. g. , melting of glaciers). Satellite observations provide a means for defining spatially varying lakes in NWP, but this concept has not yet been realised fully. Future studies should investigate how NWP can benefit from the real-time monitoring of inland surface water bodies. A more dynamic representation of lakes should be a future ambition of forecast centres. One of the recent possibilities to observe changes in ecosystems and take them into account in NWP is to use the 30 m (1 ) horizontal resolution Global Water Surface Dataset (GWSD) created by Joint Research Centre (JRC). JRC used Landsat 5, 7 and 8 individual images over the past 32 years to map the spatial and temporal variability of global surface water and its long-term changes.
Towards More Comprehensive Model Improvement through Joint Use of Multivariate EO Data As illustrated throughout various examples in this paper, satellite-based EO data products are essential to improving and informing model development. However, the potential of the growing suite of EO datasets could be better exploited through the joint use of multiple EO datasets. So far, this is only done in classical data assimilation even though the approach holds promising potential also for improving model structures, and for deriving more accurate, more unique, parameter value sets. For example, Ref. [13]  showed that robust improvements in the parameter calibration of the H-TESSEL model are only possible with multiple observation-based reference datasets. For this purpose, they used several EO datasets in conjunction with in situ measurements.
This allows overcoming their individual shortcomings; e. g. , the satellite-based soil moisture offers unprecedented spatial coverage but is available only during relatively short time periods and is constrained to the surface layer, whereas in situ soil moisture measurements cover longer time periods and multiple soil depths. Such a comprehensive approach helps to advance model development as it can capture and adjust various couplings within a model. In contrast, calibration against individual datasets such as land surface temperature results in degraded model performance with respect to other variables such as soil moisture. This is due to an imperfect representation of the energy-water coupling in the model. In this way, the use of different combinations of reference datasets for training and validation enables a targeted identification of flawed couplings within a model.
While the suite of EO datasets is growing, the complexity of state-of-the-art (land surface and hydrological) models is also increasing. The related increasing number of parameters renders it difficult to sufficiently constrain such models. The potential of a comprehensive model calibration through an adequate ratio between the numbers of model parameters and employed reference datasets has been illustrated in  [8]. They used a conceptual water balance model with few parameters and calibrated it thoroughly against a range of EO and in situ datasets. As a result, this simplistic model reached similar (and partly better) performance in simulating key hydrological variables such as soil moisture and runoff. Despite the success of a simplistic model in this particular case, the increased complexity of state-of-the-art models is required in the context of coupled weather forecasting systems, or Earth system models  [324].
This indicates that the use of emerging new EO datasets is absolutely required to better constrain those complex operational models. As a result, the improved calibration of individual modules, such as H-TESSEL, within a weather forecasting system, can contribute to advance temperature and precipitation forecasting skills, as exemplified in  [325]. Following this avenue, it is critical to better link model development efforts-for example to enable to explicit representation of (to be) observed EO variables-with the planning and design of upcoming EO missions. This way, the growing suite of EO data products can contribute to an improved physical understanding of land-atmosphere interactions, and furthermore to more accurate weather predictions as well as climate projections.
In this example of the Concordiasi campaign, datasets obtained from the field experiment over snow and sea ice have proven invaluable to document the quality and deficiencies of both satellite retrievals and NWP model fields. This has helped to provide directions in which to explore the improvement of models, which will be beneficial for weather prediction and climate monitoring. The validation and diagnostics of numerical weather modelling over snow and sea-ice with Earth observations pose challenges because of the lack of regular in situ data and because satellite observations can be difficult to interpret in those areas. Field experiments can help to provide precious information to assist scientists in this validation work. For instance, the Observations for Climate Model Intercomparisons (Obs4MIP) project under the governance of WCRP (https://esgf-node.
llnl. gov/projects/obs4mips/) is aiming at developing a database of existing observational products that can be directly used for model validation. GEWEX, the core project of WCRP focusing on land-climate interactions (http://www. gewex. org), has a particularly strong emphasis in this area, and oversees the GEWEX Data and Assessment Panel (GDAP), which provides regular assessments and evaluation of existing EO products. The use of EO products to improve the understanding and simulations of global water and energy processes is substantially highlighted in the GEWEX Science Questions  [333]. The GEWEX Strategy 2019-2028 reviews the scope of research in the context of increasing greenhouse gases and changes in climate extremes. Examples are heat waves, heavy precipitation, droughts, or floods, which all are of particular concern  [87].
Figure10. Time-series of the land surface (skin) temperature (LST, Celsius) at the south-pole for in situ observations (BRSN), MODIS satellite data (MODIS), numerical experiment with the bulk (single-layer) snow scheme (CTR) and numerical experiment with the thermal depth of the snow reduced by a factor of 10 (DSN), the latter being a proxy for the potential impact of a multi-layer snow scheme. It is clear that DSN has a larger amplitude LST diurnal cycle than CTR, and a better representation of strong cooling events [127].
Acknowledgments: The authors wish to thank the supporting institutes of the respective affiliation. Adrian Simmons  and  Alan Betts  are thanked for their precious advice and review of early versions of this manuscript. We thank three anonymous reviewers and the assigned Remote Sensing editors for their valuable comments. At ECMWF,  Georg Lentze ,  Anabel Bowen  and  Simon Witter  editorial help and assistance have been greatly appreciated.
Background: Systems biology aims to analyse regulation mechanisms into the cell. By mapping interactions observed in different situations, differential network analysis has shown its power to reveal specific cellular responses or specific dysfunctional regulations. In this work, we propose to explore on a large scale the role of natural anti-sense transcription on gene regulation mechanisms, and we focus our study on apple (Malus domestica) in the context of fruit ripening in cold storage. Results: We present a differential functional analysis of the sense and anti-sense transcriptomic data that reveals functional terms linked to the ripening process. To develop our differential network analysis, we introduce our inference method of an Extended Core Network; this method is inspired by C3NET, but extends the notion of significant interactions.
The top-scoring active sub-networks help to uncover regulatory mechanisms that control the expression changes in these experiments. In  [6] , the integration of a static protein-protein interaction network with ageing-related gene expression data is used to identify cellular changes related to age. An interaction from the reference network is put in the network associated to age A if the two related proteins are expressed in the datasets relative to A. The topologies of 37 different age-specific networks are analysed. While the global network topology does not exhibit significant changes with age, the measures of local topology (node degree, clustering coefficient, graphlet degree,. ) reveal a small set of proteins whose centrality values are correlated with age. Another possible approach is to compare networks inferred from data.
We use ECN in our differential analysis to discover the interactions of the core network that are impacted by the integration of the anti-sense transcripts. Our proposal is summarized by the workflow presented in Fig. 1. We then define the notion of AS-impacted genes to identify the genes whose network neighbourhood is drastically different when the anti-sense transcripts are considered. We also study the relationships between the AS-impacted genes and we try to explain how their neighbourhood is rearranged by computing Steiner trees. The rest of the paper is organized as follows. In "Methods" section, we first present the biological samples and the differential functional analysis that we propose to study the impact of anti-sense data on functional enrichment tests. Then we introduce the Extended Core Network Inference algorithm and we evaluate it on artificial datasets.
Biological material We study the anti-sense transcription in the context of apple fruit ripening thanks to microarray data. The AryANE v1. 0 microarray is designed to detected 63,011 predicted sense genes and 63,011 complementary antisense sequences for the Malus domestica genome  [29]. With this microarray, we can identify the sense and antisense expression for the whole predicted genome. We used data produced in order to study the evolution of fruit in cold storage condition for different varieties. For each 22 genotypes we have two samples: one at harvest time (H) and one 60 days after harvest time (60DAH) with fruits kept at 4°C. The data produced by the microarray are the intensity values of loci, a locus being associated to a gene. In order to analyse the microarray data, we use the quantile normalization  [30, 31].
Algorithm Extended Core Network estimates the gene connections using mutual information. We first copula-transform  [37, 38]  the data to have a better estimation of the mutual information. The mutual information M[ i, j] between genes i and j is estimated with the same estimator used in C3NET: M[i, j] = 1 2 log σ 2 I σ 2 J |C| where σ 2 I and σ 2 J are the variance of the expression vectors I and J of genes i and j respectively, and |C| is the determinant of the covariance matrix. The statistical significance of pairwise mutual information is test by re-sampling methods, as C3NET or ARACNE  [27]. Values that are not significant are set to 0 before applying the inference algorithm. The Extended Core Network (ECN) algorithm infers a gene network represented by an adjacency matrix. Given a set of genes G, the first step of the algorithm is to create the zero matrix representing the fact that no genes are connected.
Evaluation on artificial datasets In order to compare our Extended Core Network algorithm with C3NET, we use simulated data. The assessment of a network inference method requires a reference biological network; for some organisms like E. coli  [39, 40]  and S. cerevisiae  [41] , gene networks of reasonable size are considered as established knowledge and are used as benchmarks in the following way. From the reference network, a sub-network of the desired size is extracted; then artificial expression datasets are produced by simulating the activity of the chosen sub-network; the inference method is applied on these simulated data and produces a learned network that can be compared to the original network. We generate our simulated data with sub-networks of E. coli and S. cerevisiae thanks to the SynTREN  [42]  generator and simulator.
The sub-networks are randomly generated using the neighbor addition method which creates a connected network of n = 200 genes. We use SynTREN to create a dataset X of p = 100 samples by simulating the activity of the genes from the selected network. To evaluate the error rate of each inference method, we compare the inferred edges with the edges in the true gene network. This comparison gives us the precision =. S] is run with a specific dataset X k formed by n genes and j ∈ p 2. p samples randomly selected from the dataset X generated by SynTREN. We compare different accepting rates of Extended Core Network with C3NET on two datasets, one from E. coli and another from S. cerevisiae. We test ECN with accepting rates ranging from 20 to 100% with a 10% step and from 0 to 20% with a 1% step. Figure  2  shows the box plots of the F 1 scores obtained by the simulations.
2a  and c  once the accepting rate is higher than 10% and 20% respectively, the F 1 scores drop down to 0. From empirical observations on several simulated datasets, we can notice that an accepting rate between 5 and 10% is a good rate. In the Fig. 2b  and d , we can observe that the rate of 7% (ECN_0. 07) is the best rate value on those specific data.