dataesr/scientific-paragraphs-categorization huggingface dataset:
Synthesis The use of OSEs and OSSEs, along with regular assessment of the impact of observations on data assimilation systems, is central to the CMEMS strategy. Results depend on the data assimilation systems themselves. To derive robust results, the use of multiple systems is preferable, when feasible. Regardless, OSEs and OSSEs provide relevant information on how observations constrain ocean analyses and forecasts that feed downstream applications and users. It is important to note that most of the OSEs/OSSEs studies described above are based on integrated global and regional ocean observing systems. They take into account the role of modeling and data assimilation (i. e. , a model forecast provides better a priori information compared to climatology). The impact of a given observing system is not analyzed independently of the other components (e.
Given the potential impact of SAR altimetry, continuous effort is required to improve SAR processing for Sentinel-3 A/B (and future C/D) and refine the resolution of the associated products from 7 to 1 km. Continuity of Cryosat-2 altimeter high-latitudes observations of the ocean and sea-ice observations is required. Sea level in the leads would strongly improve the coverage of these regions and would maximize the use of Cryosat-2 data in CMEMS. It will also be important to include data from recently launched opportunity missions (e. g. , CFOSAT and HY2B) to ensure the robustness of the CMEMS multi-mission altimeter system. Same holds for wave and wind measurements from these missions. Developing Near-Real-Time (NRT) processing of SWOT is essential for demonstrating its impact on operational applications.
Finally, improving MDTs (based on the GRACE and GOCE satellite missions and in situ observations) is of utmost importance, given the impact in data assimilation systems  (Le Traon et al. , 2017b; Hamon et al. , in press). As far as ocean color data is concerned, with the presence of two-concurrent low Earth orbit operational satellites, the Copernicus Sentinel 3 A/B Ocean and Land Color Instrument (OLCI) sensor will have a dramatic impact on Ocean Color products and their quality. Monitoring rapidly evolving BGC phenomena (e. g. , river outflows, phytoplankton and harmful algae blooms, sub-mesoscale features) and the coastal zone are a strong user requirement, implying an additional requirement for ocean color geostationary satellite, which would provide unique capabilities for such monitoring.
The exploitation of the high-resolution (< 60 m) multispectral sensor capabilities on board the Sentinel-2 A/B constellation is also of great interest to CMEMS. Sentinel-2 data, with a resolution between 10 and 60 m and a revisit time of 5 days at the equator, complements the lower-spatial-resolution (300 m) daily global coverage offered by the Sentinel-3 constellation; these data are highly relevant to developing new high-resolution coastal ocean color products, improving sea-ice detection and, potentially, deriving bathymetry near the coast. CMEMS delivers near-real-time and reprocessed satellitebased information products of the sea-ice cover for the polar and global oceans. The products (e. g. , the Tactical Navigation Ice Charts) are directly accessed by end users and ingested by the MFCs to constrain (and/or validate) their forecasts and re-analyses.
Sea-ice concentrations are assimilated at Arctic and Global MFCs, both in real-and delayed-time mode. Ice thicknesses from Cryosat2 and SMOS are now assimilated at the Arctic MFC; the global MFC plans to assimilate such products in the near future. Sea-ice drift data are currently assimilated in the CMEMS Arctic system, but, so far, this has limited impact with respect to the assimilation of sea-ice concentration and thicknesses. Specific CMEMS requirements for polar and snow monitoring were outlined in CMEMS (2016). Sea-ice concentration from passive microwave radiometry is, by far, the better-controlled sea-ice quantity entering the operational systems. CMEMS (2016) stated the importance of ensuring continuity and improving the quality of sea-ice concentration products, both for climate monitoring and near-real-time applications.
Status-In situ Observations From the CMEMS in situ TAC The CMEMS in situ Thematic Assembly Center (INS-TAC) is the main interface between CMEMS and the global, regional and coastal in situ observing networks. Its role is to collect, process and quality control the upstream in situ data required to both constrain and directly validate modeling and data assimilation systems and to directly serve downstream applications and services. The main types of in situ observing systems aggregated by INS-TAC include all the platforms identified in  Ryder (2007)  and many others. These systems were made available over the years, when their quality was deemed appropriate to meet service requirements. The INS-TAC provides vertical profiles and time-series data coming from different types of instruments (e.
g. , the US National Center for Environmental Information (NCEI) World Ocean Database (WOD), the US National Data Buoy Center, the Australian Integrated Marine Observing System (IMOS) and European [SeaDataNet, International Council for the Exploration of the Sea (ICES), EMODnet] aggregators. At the European scale, the INS-TAC mainly collaborates with the EuroGOOS Regional Operational Oceanographic Systems (ROOS) and their task teams, which operate and coordinate most of the regional monitoring systems. Several EuroGOOS task teams have been established in recent years, in particular, for coordinating different observing platforms in Europe [FerryBox, Tide gauges, Gliders, HF radars, Argo floats (Euro-Argo), Fixed platforms (EMSO) and Animal-borne instruments]. One of their objectives is to link data management standards within CMEMS INS-TAC with those of existing global networks.
In some cases, these task teams, such as the HF Radar EuroGOOS Task Team, have enabled the integration and enhancement of a completely new technology network. At the global scale, the most important source of profile data for INS-TAC is the Argo network (about 4,000 platforms cycling every 10 days) and its extensions to the deep ocean and biogeochemical parameters. It is complemented by XBT lines (about 50 lines, half of which were active in 2018), sea mammals in high latitudes and, in delayed mode, the GO-SHIP hydrographic sections (60 lines in 2018) and other research cruises observations from US-NCEI and CCHDO (CLIVAR and Carbon Hydrographic Data Office). In European seas, glider data, especially in the Mediterranean and the North West shelves, as well as Conductivity, Temperature, Depth (CTD) monitoring for the Nordic and Baltic Seas can be found in the INS-TAC database.
Global ocean research cruise data from SeaDataNet complement delayed mode coverage. For time-series data, the most important source of observations is the DBCP network, which operates more than 1,400 drifters and 20 Arctic buoys. It is complemented by the GOSUD and Voluntary Observing Ships (VOS) network that provide both SST and SSS observations, as well as surface carbon data. The Argo network also provides time-series of temperature and salinity at the surface and at drifting depth, along with derived velocity information. In European seas, FerryBox lines (mainly the Baltic and North West shelves), tide gauges all along the European coasts, and coastal monitoring stations operated by EuroGOOS ROOS members also are integrated in the database. Finally, as for profile data and for reprocessed products, timeseries data sets are complemented with historical research data from the US-NCEI-WOD database, from SeaDataNet NODCs, and for biogeochemical data, from EMODnet-Chemistry and ICOS (Integrated Carbon Observation System) Ocean Thematic Center.
g. , surface drifters, ThermoSalinoGraphs (TSG) and FerryBoxes]. Non-assimilated observations are, however, essential for independent qualification/validation of analyses and forecasts and for evaluating model and system improvements. In particular, model velocities are systematically validated with observations from surface drifters, Argo, HF radars and moorings, while tide gauges allow validating sea-level variability in coastal regions. The BGC, sea-ice and wave analysis and forecasting systems are currently constrained by satellite observations only and do not yet assimilate in situ observations. BGC in situ observations are used for model development and analysis validation. BGC Argo floats increasingly provide observations of oxygen, chlorophyll, pH and nitrate. The GLODAP data base includes in situ data for oxygen, nutrients, dissolved inorganic carbon and alkalinity from other platforms than the Argo floats.
Similarly, in situ sea-ice observations, more specifically the thickness of the different ice and snow categories from the unified sea-ice Climate Data Record (CDR) data base in the Arctic, are used to validate the ice model. Significant wave height and period (peak and mean) from buoys are essential for validating global wave products. In the future, the following variables should be observed with better global coverage: • BGC measurements: oxygen, nitrate, pH, CO2 fugacity, alkalinity, Chl-a. • Deep temperature and salinity measurements (below 2,000 m). • In situ velocity observations. • In situ sea-ice observations, including thickness, temperature and snow depth. • Open-ocean wave measurements. The main need is to collect measurements for under-sampled variables (e. g. , velocity, BGC) and under-sampled regions (e.
During the International Polar Year (IPY) (2007-2009) several Ice-Tethered Profilers (ITPs) were deployed, registering temperature and salinity profiles. These proved able to cut, by half, errors in water mass properties in the ARC MFC reanalysis  (Xie et al. , 2017) , especially in the Atlantic water layers. Since the IPY, the number of ITPs has severely dropped, so augmenting the number of ITP, up to the level of the IPY (5 to 10 ITPs) or higher, would enable higher forecast quality in the Arctic. The perennial ice coverage available for ice-tethered equipment, however, has also further diminished in the meantime, limiting the zone where ITPs can operate for a long time. Marine mammals make a more agile sampling of the seasonal ice zone but their data need delayed-mode processing and are not assimilated in real-time forecasts.
Research cruises are more frequent in the summer, but data are seldom transmitted in real-time. Acoustic tomography can also provide integrated temperature observations of high accuracy, but their use in data assimilation is still underdeveloped. BGC data in Arctic waters, nutrients in particular, are necessary for predicting primary production, but not all observations are publicly available or harmonized for processing long time series. Autonomous platforms, like BGC-Argo should, therefore, be prioritized. Operational wave buoys data are not publicly available, except in Iceland; although historical data exist in the offshore industry. As a rule of thumb, the safety of shipping and fishing activities will require a dozen of those, evenly spaced along the Northern Sea route. With reduced ice cover, observations of waves by accelerometers in sea-ice are also expected to become increasingly important.
North West European Shelf The North West Shelf (NWS) MFC routinely downloads and quality controls satellite data, as well as in situ temperature and salinity profiles from Argo floats (only available in the off-shelf areas), drifters, gliders, moored buoys, marine mammals, and research ship observations for data assimilation. For verification and validation purposes, drifter-derived currents, HF Radar and tide gauges are used. There is a need to increase the in situ data coverage, especially on the shelf. Currently, the low spatial and temporal sampling of the sub-surface in the North, Irish and Celtic Seas, coupled with a lack of current and air-sea flux observations, hinders progress in identifying and reducing model biases. The number of BGC observations is unsatisfactory and, it should be noted, the BGC Argo observations, so useful elsewhere, will not be available on the shelf.
Iberian-Biscay-Ireland The Iberian-Biscay-Ireland (IBI) MFC assimilates temperature and salinity vertical profile data from Argo floats. Product quality metrics are computed using data from multiple platforms, such as moored buoys, Argo and BGC-Argo floats, drifters, gliders, XBTs, CTDs, HF Radar. The coverage of in situ data in shelf/coastal areas is still too scarce. In the future, it will be important to increase the number of near-real-time in situ observations in shelf and in coastal areas of the IBI region. HF Radars are useful for monitoring high-frequency surface circulation dynamics. To better monitor the three-dimensional circulation on the shelf, more acoustic Doppler current profiler (ADCP) observations (if possible, available in NRT) are needed. In situ BGC data coverage in the IBI area certainly must be enhanced.
Mediterranean Sea There is a recognized concern about the status of the Mediterranean observing system and its future. There are important gaps due to the lack of instruments and poor data policy, in particular for central-eastern Mediterranean Sea and the northern African coast, leading to a strong North-South West/East imbalance. When looking more specifically at platform types, at MED-MFC Argo floats, CTD and XBT platforms are assimilated into the system and also used for quality assessment, while moorings are used only to assess the quality of the system through independent validation. The existing coverage of the Argo network is about 60 active floats in the Mediterranean Sea, which is twice the standard Argo density. This should be, at least, maintained as recommended by the Euro-Argo ERIC (European Research Infrastructure Consortium)  (Bittig et al.
Specific Requirements for the Validation of Satellite Data Products The calibration and validation of the upstream (Level-2) satellite data are the responsibility of ESA and EUMETSAT, which are working to develop a dedicated in situ observation system to ensure the acquisition of high-quality in situ measurements, accompanied with their uncertainties, also referenced to as "Fiducial Reference Measurements" (FRM). These measurements can be used by CMEMS TACs and MFCs for their validation activities. It is important that CMEMS requirements are taken into account when designing and developing new platforms for acquiring FRM. Satellite-based sea-level (and geostrophic velocity) measurements are validated using tide gauge networks, drifting buoys, gliders, HF radar, ADCP and Argo profilers. While, at the global scale, the number of available in situ observations is adequate to routinely conduct validation, at the regional level, particularly for the Mediterranean Sea and Black Sea, available tide gauge datasets are still too limited for a robust validation.
Acknowledgments This work was supported partially by the  JSPS Core-to-Core Program , A. Advanced Research Networks : " Japan- Europe Research Hub for Translational Medical Engineering (JETMeE )". The authors acknowledge the support from  SMMiL-E partners , the  French State-Region  plan contract  CPER , and  IRCL (Research Institute against Cancer of Lille). The authors thank  M. Kumemura  ( Kyushu Institute of Technology ) for useful comments on the manuscript. The authors also thank  T. Fujii  ( The University of Tokyo ) and  S. Kaneda  ( Kogakuin University ) for providing original high-resolution images for Fig. 3, andM. C. Tarhan  ( LIMMS, SMMiL-E ) for providing original high-resolution images for Fig. 4.
The modelling was built upon the lessons learned from estimating and projecting sustainable future supply of forest biomass for different countries in Europe  (Barreiro et al. 2016; Schelhaas et al. 2017). The results complement existing outlook studies, especially, the European Forest Sector Outlook Study II (EFSOS-II; UNECE/FAO 2011). While the EFSOS-II data provides information on the overall production and consumption of forest products between 2010 and 2030 under four policy scenarios, this study focused on harmonised definitions, assumptions, and methodology to account for the administrative restrictions affecting forest use and, thus, wood supply for forest products.
When defining the categories, the harmonised definitions of  Lind et al. (2016)  were followed as closely as possible, but because of different forestry practices and availability of the information affecting the exact stratification, the rules applied for distinguishing FNAWS and FRAWS are presented country-specifically in the description file associated with the dataset. We projected the forest growing stock, above-ground carbon, and fellings for the period of 2015-2040 accounting for the administrative restrictions described above. These attributes were selected as they are widely included in the measurement and estimation protocols of the European NFIs  (Vidal et al. 2016)  and their development is informative on the effects of administrative restrictions. The definitions of these attributes were harmonised according to experiences from earlier international processes and projects  (Korhonen et al.
For mathematical details, the reader is referred to Sirkiä The EFDM was used for the projections of 20 countries following the method described by  Vauhkonen and Packalen (2017)  to adapt the EFDM corresponding to the current forest area distribution and business-as-usual forest management applied in each country (Fig. 1 ). The EFDM runs were parameterised and operated by national NFI teams' representatives, who also provided the required information on country-specific forestry dynamics. Each participant was instructed to use v. 2. 0 of the EFDM, which can be downloaded from https://github. com/ec-jrc/efdm and run in the R statistical environment (R Core Team 2016) as open source code under the European Union Public License (EUPL). The participants were asked to initialise the EFDM using the most recent available NFI data as input.
g. the level of aggregation similar to other growth simulators typically applied in the area). The different PPF points were then produced by altering allowable activity probabilities (Fig. 1 ) for the FAWS, FNAWS, and FRAWS categories according to the definitions of PPFs. The transition probabilities were derived from the best available sources such as repeated measurements of permanent inventory plots; growth data from temporary inventory plots; growth simulators; or expert opinion, according to what was considered relevant and feasible for each country. The parameterisation of the factor levels, transition probabilities, activity probabilities, and output coefficients, in addition to possible deviances from the aforementioned documents used by some participants for their analyses, are presented in the description file associated with the dataset.
Other modelling approaches The calibration of the EFDM proved difficult based on NFI data from the southern Belgium (Wallonia), Denmark, and UK because of reasons such as (1) high fragmentation and The projections in Wallonia were simulated using an inhouse Forest Simulation Software (FSS). It integrates treelevel, distance-independent growth, regeneration, and harvesting models that were fitted on data from the Regional Forest Inventory of Wallonia (RFIW) measured between 1994 and 2015. These models take into account the species composition, stand density, site characteristics, tree size, social status, and the type of forest ownership. Aerial photographic interpretation was used to update the status of each permanent forest sample plot monitored by the RFIW. The fitted models and the results of the photointerpretation were applied on 95,310 virtual forest stands generated from the most recent RFIW data to provide outputs comparable with those based on the EFDM.
In Denmark, the modelling was based on the method described by  Johannsen et al. (2017). Similarly to the EFDM, the model applies transition probabilities in a Markov model setup for the development of growing stock volume and carbon. The fellings are based on the deduced area development and activity data are based on yield models. This modelling approach yields outputs comparable with the EFDM based on the NFI results from Denmark. The model for UK takes mensuration data from the NFI plots (measured 2010-2015) along with the Forestry Commission's Sub-compartment database (SCDB) and uses it to assign a yield class and predictive growth curve based on data collected since 1919 by the Forestry Commission in yield plots and thinning and spacing experiments. These growth curves are applied under an agreed set of management assumptions for each subcomponent of woodland within the plots.
Access to the data and metadata description The dataset can be downloaded using the following reference and doi  (Vauhkonen et al. 2019). Data from: Harmonised projections of future forest resources in Europe. Dryad Digital Repository, https://doi. org/10. 5061/dryad. 4t880qh. The metadata of the entire dataset are available at https:// metadata-afs. nancy. inra. fr/geonetwork/srv/eng/catalog. search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9 The dataset covers two files named dataset. csv and README. xlsx. The dataset. csv file is provided as a standardised, comma-separated (csv) text file. It contains the country-specific projections of the forest growing stock volume, above-ground carbon, and fellings in the area analysed. The README. xlsx file contains altogether 29 worksheets as described below.
The "Information" sheet provides a brief description and a reading instruction for the dataset. "DATA_DICTIONARY" provides the acronyms, definitions, and units for all variables used in the dataset. "PARAMETER_ENVIRONMENT" provides details on how the future projection model (cf. Fig. 1 ) was parameterised by the NFI data and forestry practices of each country. "CLASSIFICATION_RULES" presents the definitions according to which FNAWS and FRAWS were distinguished from FAWS in countries that applied these categorizations in their analyses. "TOTAL_RESULTS" shows how the results can be combined and example graphs prepared for discovery and exploratory purposes using some limited Excel functionality. The remaining sheets present the results for each of the 23 countries and the "template" on which the information was requested.
Technical validation Central characteristics were extracted from the metadata of the simulations and used to describe the extent and reliability of the analyses of each country (Table  1 ). According to Table  1 , the analyses covered altogether approximately 130 mill ha of forests in Europe, of which 5. 9% and 5. 8% were FNAWS and FRAWS, respectively. The definitions of FNAWS and FRAWS for those countries that excluded these categories from FAWS varied as presented in the description file associated with the dataset. The proportion of FAWS, FNAWS, and FRAWS of the total forest area as well as the key simulation parameters for the future projections varied considerably between the countries as shown in Table  1  (see also the next section). As a further validation of the dataset, the following results were examined and verified to demonstrate that they credibly depict the different European countries: & Analyses of the temporal development of the growing stock volume, above-ground carbon, and harvests, which can be illustrated and analysed for individual countries, country groups, or, as in Fig.