dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The present research project Water Budget over Dome C ( H2O-DC ) has been approved by the  Year of Polar Prediction (YOPP) international committee. The permanently manned Concordia station is jointly operated by the  French Institut Paul-Emile Victor (IPEV)  and the  Italian Programma Nazionale Ricerche in Antartide (PNRA). We would like to thank all the winter personnel who worked at Dome C on the different projects: HAMSTRAD, aerosol lidar, radiosonde, and BSRN. The authors also acknowledge the  CALIPSO science team  for providing the CALIOP images. We acknowledge the NCEP_Reanalysis 2 data provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their website at https://www. esrl. noaa. gov/psd/(last access: 2 April 2020) and the  NOAA Air Resources Laboratory  for access to the HYSPLIT model through https://www.
ready. noaa. gov/HYSPLIT. php(last access: 2 April 2020). We would like to thank the two anonymous reviewers for their beneficial comments. Financial support. This research has been supported by  IPEV  ( French Polar Institute ,  Institut Paul-Emile Victor ) in the framework of the project no. 910  (HAMSTRAD, microwave radiometer) and  PNRA (Programma Nazionale di Ricerche in Antartide)  in the framework of the projects numbered  OSS-006  ( BSRN Observatory ),  AC3. 05  ( PRE-REC , aerosol lidar), and  OSS-10  (RMO OBS, radiosondes). Review statement. This paper was edited by  Corinna Hoose  and reviewed by two anonymous referees.
Recent EURATOM research efforts on Emergency Preparedness and Response (EP&R) have been focussed on programs addressing some main knowledge gaps clearly identified in the outcomes of investigations carried out in Europe in response to the Fukushima accident. The PREPARE and FASTNET projects tried to solve similar problems adopting very complementary and synergic approaches. The main achievements of both projects are detailed in this paper. In particular, the problem of the fast estimation of time-dependent, longlasting Source Terms is discussed. This problem is not only a technical one, but is also related to the experience and skill of the code users. As the EP&R is spanning a wide range in Europe, certainly far beyond the borders of individual states, it is mandatory creating a common and shared understanding of emergencies.
Introduction Research and Development in the area of Nuclear and Radiological Emergency Management and Preparedness under the EURATOM umbrella went on in the last years with two major projects, namely, the PREPARE Collaborative Project (from 1 February 2013 to 31 January 2016, coordinated by KIT)  [1, 2]  and the FASTNET Research and Innovation Action (from 1 October 2015 to 30 September 2019, coordinated by IRSN)  [3, 4]. The first project was funded through the FP7-Euratom program and the second through the H2020-Euratom program. Both projects aimed at improving the existing Emergency Preparedness and Response (EP&R) in Europe, and at addressing and closing some important gaps identified during, and in the aftermath of, the Fukushima Daiichi accident  [5]. The outcomes of the analysis of the European reaction to the Japanese accident showed several important and common issues, which can be summarized as follows: missing early and rapid information on the potential Source Term (ST); absence of coordinated plan at European level to estimate the ST; absence of a harmonized response to the safety of the European residents living in Japan; partly chaotic communication with the public; insufficient guidance on how to deal with incoming goods from Japan.
Fast and reliable ST assessments, not necessarily of a strongly conservative nature, are at the very heart of the problem. In this regard, PREPARE included among its goals the initial development and implementation of tools to derive a ST, using inversion algorithms as well as real-time ambient gamma dose-rates measured at the boundary of NPPs. To achieve the same goal, FASTNET developed fast-running tools to predict STs using a minimum set of plant data. Both approaches are valid and both need to be implemented and further strengthened; nevertheless, in case of missing dose-rate data, only the second approach can work, while dose-rate data may help correct calculated STs if wrong assumptions have been made in the calculations themselves. An example of synergy between the two approaches could be found in the fact that source inversion algorithms need a first-guess ST, the accuracy of which impacts directly the effectiveness of the inversion; this first-guess Source can be obtained with the tools developed in FASTNET.
Results of the test are reported in Figure  2. Concerning the transport of radionuclides in water, the aim was to extend the Hydrological Dispersion Module of RODOS (RODOS-HDM), incorporating in it also the MOIRA DSS. MOIRA is a DSS created for the management of fresh water ecosystems contaminated by radionuclides and heavy metals  [9]. An important issue for PREPARE, which is also of interest for FASTNET, is training and exercising. Two exercise sessions were organised, one focused on radiological assessment, supported by the use of JRODOS, and the other consisting of a more extensive table-top exercise with a simulated accident scenario. In addition, two table-top exercises were organized, dealing with a transport accident and with monitoring of a large scale cross-border contamination, respectively.
The FASTNET project The FASTNET (Fast Nuclear Emergency Tools) project started in October 2015 and is expected to end in September 2019. It gathers 20 partners, coordinated by IRSN, together with IAEA. The aims of FASTNET are centred on three major pillars: the development of a reference SA scenarios database  [10] , inclusive of time-dependent, isotopic STs, created using best-estimate SA codes (ASTEC  [11] , MAAP  [12]  and MELCOR  [13] ); the extension of existing methods (3D3P) and fastrunning codes (PERSAN and RASTEP) to predict STs to all current nuclear power plant technologies deployed in Europe (PWR, BWR, VVER, CANDU, EPR, including a generic model for SFPs) and their further development; the dissemination of best-practices on the use of the methods and tools developed within the project to estimate STs in real-time and during conditions typical of real emergencies.
To address this major challenge, it was suggested within FASTNET to improve and disseminate a methodology for the diagnosis of plant status and for the prognosis of accident sequence, the 3D/3P (developed by IRSN), as well as two European fast-running tools, the French code PERSAN (developed by IRSN) and the Swedish code RASTEP (developed by LR), by extending their capabilities to all European NPP technologies, and to start to disseminate best practices in their use. The 3D/3P, acronym for Triple Diagnosis/Triple Prognosis, is an analytical method which enables providing a simplified quick diagnosis of plant condition and the prognosis of a postulated future situation, evaluating the status and integrity of the typical three barriers of the defence-in-depth: fuel and cladding, primary circuit, and reactor containment.
Chemical phenomena are modelled by very simple correlations. Initial core inventories are provided. Like for 3D/3P, PERSAN had been developed by IRSN for PWRs, and within FASTNET it has been extended to other reactor types, including BWR, VVER, CANDU and SFPs. RASTEP  [15]  is a probabilistic code which can select, among a set of several pre-calculated STs for a given reactor type, the ST with the highest probability of occurrence for given plant conditions. The code is made of three main components: a Graphical User Interface (GUI, see Fig. 4 ), a Bayesian Belief Network (BBN) for each reactor type, and a database of pre-calculated accident sequences with related STs. These can be either obtained from the outcomes of PSA-2 studies of a given, real plant, or calculated ad-hoc with SA codes using generic plant schemes.
Aim of the BBN is to connect partially available plant status data to one or more possible and compatible end states, represented by given STs, like in faulttree analyses. Introducing some plant conditions, some branches of the BBN are either isolated and further excluded from the analysis, or kept "open" and navigated up to an end state (or states) with associated conditional probability or likelihood of occurrence. The more information on the plant status is provided by the user, the higher is the probability of reaching a good ST for the situation under scrutiny. The approach is clearly based on the assumptions of having a sufficiently large database of sequences to cover the most of the accidental situations and a robust BBN to map correctly the database. The GUI is used to provide information to the code by answering a limited set (some tens) of simple questions on the safety barriers and safety safeguards.
Their availability or unavailability determines which boughs of the tree are to be selected and, in case of more than one final plant status, which probabilities can be associated with the different results. RASTEP has been initially developed by LR for SSM for BWR and within FASTNET its use has been expanded to include also PWR, VVER and CANDU. The extension consisted in the creation of dedicated BBNs for each reactor type as well as the ad-hoc database of reference STs. To fill the RASTEP databases, another goal of FASTNET was the development of a comprehensive database of reference STs, calculated with best-estimate codes like ASTEC, MAAP and MELCOR, for as many sequences as possible: a huge effort indeed for the partners because were not the STs already available, they had to be evaluated from scratch.
The reference STs had obviously to be given in terms of time-dependent isotopic releases, which was really challenging for partners using those SA codes, which only deal with chemical classes. The database is also a set of reference scenarios against which it is possible to test and validate the behaviour of the fastrunning codes. This implies that they should contain not only data for the temporal progression of the accident sequences and time-dependent STs, but also many thermal-hydraulic time-dependent data on the primary circuit, as well as physical data on the containment status. Accordingly, given the precious nature of the information contained in the FASTNET database, it was decided to transfer it to the IEC of IAEA for the purposes of maintenance in time, beyond the lifetime of FASTNET, and for further future expansion.
IAEA CPs would be allowed, in principle, to search the database on-demand, in case of specific needs (including training), or during real emergencies, which might be similar to a scenario already available in the database. The development of the FASTNET database proved to be a very ambitious, time-consuming, and highly demanding task. Two problems are still pending concerning the sequences currently available: the number of sequences itself, and the quality control of their data. As of today, the database comprises about 120 sequences, and a few more are planned to be added before the end of the project. Despite this big number, the database is far from being complete and exhaustive, and many more years of work should be needed to reach a level, which can be considered more or less satisfactory for EP&R needs.
However, on one side it confirms the need of having fast-running codes, on the other cannot be seen as an excuse to limit the use of bestestimate codes for general EP&R needs. Concerning the quality control of the provided STs, this was obviously beyond the scope and the limited resources of FASTNET, and therefore the FASTNET database is to be considered for now "as is". A further and final aim of the FASTNET database was to provide data for the preparation of another extremely important product of the project, namely, training in the form of emergency exercises. To address the above-mentioned problem of training in EP&R, the FASTNET project envisaged a twofold approach. On one side a one-week training on 3D/3P, PERSAN and RASTEP has been organized, during which the participants (not limited to project partners but open also to interested stakeholders forming the s.
This exercise was very challenging, because of the time constraints, however proved to be enormously useful in getting more experience in the realtime use of the FASTNET products. In the objective the STs can be used in different atmospheric dispersion codes and also shared among different emergency responders, they are to be standardized in terms of format of data and files. To address this requirement, a few years ago, IAEA developed the IRIX (International Radiological Information Exchange Format)  [16] , which is an xml-based information exchange format designed to facilitate web-based exchange of relevant emergency information and data among organisations that respond to nuclear and radiological incidents and emergencies, and in particular the exchange of emergency information among national authorities that have responsibilities assigned under the Convention on the Early Notification of a Nuclear Accident.
An important byproduct of the FASTNET project has been the adoption in the fast-running tools, as well as in the exporting options of the database for the IAEA, of the IRIX format. While still under development (the current version is 1. 0), the IRIX format allows decoupling from an IT point of view ST calculation tools from atmospheric dispersion codes. Within FASTNET, IRIX output capabilities were introduced for PERSAN, RASTEP and the database, while input capabilities have been introduced in JRODOS. During the second exercise, some partners were therefore able to use PERSAN or RASTEP in conjunction with JRODOS thanks to the IRIX input/output functionalities (see Fig. 5 ). This is of course to be maintained for the future, since new and improved versions of the IRIX standard may be foreseen in the incoming years.
Conclusions Both PREPARE and FASTNET projects tried to close some gaps identified in EP&R capabilities in Europe; they both tried to implement in practice some lessons learned from the Fukushima Daiichi accident. Both gave complementary contributions to solve fundamental problems of EP&R. Much has been done, as detailed in the previous paragraphs, but much still needs to be achieved. For example, one major challenge, which was anticipated and actually experienced in the FASTNET project, is related to the dialogue between the severe accident management scientific community and the emergency management one. These two communities have the same final aim of protecting people through increase in safety; they, however, speak different languages and are used to tackle similar problems but with different perspectives.
FASTNET was the first European project on EP&R in which these two communities were gathered together and were asked to cooperate; there was then an additional operational aim within FASTNET: to find a common language, harmonize the practices, use the tools that are most relevant and easy to use for them, and facilitate their appropriation of the common methodology proposed. This first dialogue attempt was certainly fruitful, but not complete. In the future it is then highly recommended that opportunity is given to strengthen the developed links between these two communities, for example by organizing (1) operational trainings based on every technology and the feedback from the exercises organized within FASTNET; (2) a new series of exercises, targeting the protection of population and having a higher level of reality (full-scale formats, scenarios based on every technology, etc.
). It must in fact be recognized that much more training is needed on the fast-running tools developed, especially in their use in emergency centres. As evidenced for the PREPARE project and as already introduced before about the outcome of the FASTRUN OECD/NEA project, training in EP&R is really an absolute need for Europe. The development of fast-running codes is per se not enough if potential emergency responders are not properly trained in dealing with such tools and the phenomena they describe. Further development of the reference SA database is also necessary. The complementarity between the results of PREPARE and FASTNET should be taken to the level of productive interaction, for example by using STs derived from fast-running tools to aid the procedures of ST estimate from dose-rate measurements (inverse methods).
6 μm for geolocation and cloud detection (ecostress. jpl. nasa. gov). On board the ISS, which has an irregular orbit (rather than a regular polar or geostationary orbit), ECOSTRESS collects measurements continuously between ~52°N and ~52°S at different times of day. The overpass return frequency for any same spot on Earth is 1-5 days, depending on latitude, with some areas measured multiple times in a single day (particularly the higher latitudes where the ISS orbital direction shifts) within the ECOSTRESS swath width of 384 km (Figure  1 ). The pixel size at nadir is 38 m × 69 m, which is resampled by the ECOSTRESS data production software to 70-m × 70-m pixels for noise reduction and ease of use. As such, ECOSTRESS now provides a combination of good spatial and temporal resolutions with diurnal cycle sampling.
, 2013 ) (L4_ESI_PT-JPL; L4_ESI_ALEXI), and a water use efficiency (WUE) product (L4_WUE)  (Fisher & ECOSTRESS algorithm development team, 2018) , the latter of which originally incorporated gross primary production from MODIS  (Zhao et al. , 2005) , but switched to a native 70 m gross primary production product retrieved using the Breathing Earth System Simulator algorithm  (Ryu et al. , 2011). At the outset, the ECOSTRESS Early Adopters program was the largest in NASA history, with a large community of scientists using ECOSTRESS data for a wide variety of applications (ecostress. jpl. nasa. gov/early-adopters). NASA develops Early Adopters programs for new missions to enable support for learning to use the data products while the mission and data production are still in development. Nonetheless, it is essential that ECOSTRESS data are validated first to provide an assessment of accuracy and error before these scientific investigations can be established.
Moreover, within vegetation classes there was large site variation, adding to the sampling diversity. Many different agricultural crops were included, ranging from corn and potatoes to almond orchards and rice fields, for example. Forest sites ranged from recently planted pine trees to 100 +year-old oak forests. Alpine grasslands, peat bogs, saltwater marshes, and desert grasslands also contributed to the diverse data set. Large synthesis databases coinciding with the recent time period of this analysis were not yet available, so we collated sites individually. Potential sites were identified from a wide variety of methods and sources:  The data received varied widely in level of processing, from extensive to raw. As needed, we formatted data for consistency, including renaming data gaps with NaN, conversion of timestamps to ECOSTRESS timestamps, and resampling of time steps finer than 30 min to 30-min time steps (for ease of analysis, understanding that ECOSTRESS does not overpass exactly on the hour/half-hour).
We matched data to quality flags and excluded data that were flagged from the source data as either "poor quality" or "not a direct observation"  (Foken et al. , 2004; Foken & Wichura, 1996; Göckede et al. , 2008). Given the recency of these data, we identify these data as initial estimates, which may be subject to change once ingested into repositories and network-wide QA/QC applied. This limits the quality of our analysis, partially obviated by the large number of sites. Finally, we processed the data through the FLUXNET 2015 data processing standards and code for half-hourly energy balance closure (fluxnet. fluxdata. org/data/fluxnet2015-dataset/data-processing) (Foken, 2008; Stoy et al. , 2013; Twine et al. , 2000; Wilson et al. , 2002). The method produces a range of uncertainty on the eddy covariance measurements as well.
For comparison, we also evaluated a 1-km box around each tower (not all sites were available for this larger comparison). We used the ECOSTRESS L2 and L3 quality flags to filter for high quality ECOSTRESS data. The L3 quality flag product, which itself is the combination of the quality flags of all of the ancillary inputs such as MODIS and Landsat, is provided as integers but must be read as 8-, 16-, or 32-bit binaries. For example, with the MODIS Cloud Mask flag, one-bit representation number can reveal: the cloud mask status flag (Bit 0), the cloud mask cloudiness flag (Bits 1 and 2), day/night flag (Bit 3), sunglint flag (Bit 4), snow/ice flag (Bit 5), and surface type flag (Bits 6 and 7). To decode each flag, one needs to shift the bit to the proper location and read the appropriate length.
Reading a quality flag 51 as an 8-bit binary would result in 00110011. In this example, Bit 0 (read from the right to left) = 1 and would mean the data are useful. Specifically, data marked with bad quality flags in the MODIS forcing data for clouds (MOD06; bit code 119 marks clear-sky conditions) and aerosol optical depth (MOD04; bit codes 85 or 119 mark good conditions) were avoided as they would introduce contamination into the ECOSTRESS ET retrieval. Users will also want to use the ECOSTRESS uncertainty product for assessment of the ET quality, especially relative to the magnitude of the retrieved ET estimate. After quality control and given available cloud-free and high-quality ECOSTRESS acquisitions during this time period, 82 sites and 502 acquisitions were ultimately used for analysis, representing the majority of available site-to-satellite data pairs (supporting information Figure  S1 ).
Discussion These results were for the initial, or Stage 1, validation. Although previous analyses have shown good performance of PT-JPL, results tend to be worse at the instantaneous level (generally better at daily/weekly/ monthly aggregates) (Y. Chen et al. , 2014; Ershadi et al. , 2014; Fisher et al. , 2008; Fisher et al. , 2009; Jiménez et al. , 2018; McCabe et al. , 2016; Michel et al. , 2016; Miralles et al. , 2016; Polhamus et al. , 2013; Purdy et al. , 2018; Talsma et al. , 2018; Vinukollu et al. , 2011). Some studies have shown PT-JPL to have a high bias; so the small bias shown here was an improvement (Y. Chen et al. , 2014; Jiménez et al. , 2018; McCabe et al. , 2016; Polhamus et al. , 2013; Purdy et al. , 2018; Talsma et al. , 2018). We postulate that the results shown here are attributed to five main reasons: (I) the high spatiotemporal resolution of ECOSTRESS; (II) systematic eddy covariance energy balance closure computation; (III) overrepresentation of temperate ecosystems/underrepresentation of tropical ecosystems in the validation data set; (IV) careful treatment and filtering of the ECOSTRESS quality flags and cloud mask; and (V); the high accuracy and precision of the ECOSTRESS measurement, L2(LSTE) product, and the PT-JPL model itself.
, 2008; Jiménez et al. , 2018; Jung et al. , 2010; McCabe et al. , 2016; Michel et al. , 2016; Nemani et al. , 2003; Purdy et al. , 2018). As such, it is relatively easy to capture temperate ET so long as the atmospheric conditions are well tracked. Here, the majority of sites obtained were from the United States and temperate ecosystems, and under clear-sky conditions, which also improve retrieval accuracy. We chose to include all of the sites in the analysis as we did not have enough data to exclude or downweight/upweight sites for a more globally representative assessment, as we have done with much larger synthesis data sets (e. g. ,  Famiglietti et al. , 2018). But this may inflate the interpretation of ECOSTRESS performance at the global scale. We cannot overstate the importance of incorporating the ECOSTRESS quality flags, cloud mask, and uncertainty product in analyses such as validations.