dataesr/scientific-paragraphs-categorization huggingface dataset:
FORWARD has also become a central tool to compute synthetic spectro-polarimetric images comparable with current (CoMP) and future (DKIST) ground-based instrumentation. In addition, the CHIANTI database  (Landi et al. 2013 ) can be used to produce synthetic brightness profiles in different emission lines typically observed in the chromosphere and corona in the X, UV and visible ranges. Creating synthetic profiles out of such models using CHIANTI or FORWARD, spectra and images will be compared to Solar Orbiter's unique RS data. Solar Orbiter's EUI, Metis and Solar OrbiterHI will give us a new viewpoint to test background coronal models using forward modelling or image inversion (section 4. 3). Synthetic RS observations from the polar regions will be very useful at checking the relative locations of streamers and coronal holes against EUI and Metis images.
Third, modelling can be used to simulate data in parts of the map that are not in any of the areas where observational data is available at the reference time. This can be as simple as taking into account the solar (differential) rotation from the closest point in time at which observational data was available (as in  Auchère et al. 2005 ), but more complex models exist (as mentioned in Sect. 4. 1. 2 for the magnetic field). A database obtained by producing synchronous EUV maps from SOHO/EIT in 1996 to 2010 is available as part of the MEDOC database  5. These maps are produced by taking 60 • on either side of the central meridian, and by computing differential rotation of older data in the rest of the solar sphere. Furthermore, for each pixel in the map, data from several EIT images are used, with weights related to the duration between the observation time (for these older data) and the reference time for the map.
The accuracy of this type of approach using SDO/AIA data is tested in  Guennou et al. (2012a,b). Many strategies have been explored to find the most probable solution through the vast parameter space while minimizing the number of ad-hoc assumptions, e. g. Markov-chain Monte Carlo algorithms  (Kashyap & Drake 1998) , regularization in L2  (Hannah & Kontar 2012) , sparse inversion in L2  (Warren et al. 2017)  or L1  (Cheung et al. 2015). DEM inversion is still an active field of research, and there is no single method adopted by the community as providing robust and undisputed results. The choice of method generally depends on the application and results from a compromise between computing time, accuracy and robustness. A database of DEM inversions obtained by assuming a Gaussian DEM on all SDO/AIA data at a cadence of 30min is avail-able as part of the MEDOC database 6.
During the Solar Orbiter era, it is planned to continue maintaining this database and completing it with new DEM inversion results. Possible evolutions include the use of functional DEMs with a high-temperature tail, that would be more adapted to active regions, or the use of other instruments such as the NOAA operational instrument GOES-16/SUVI. On Solar Orbiter, the SPICE spectrometer will have the capability to provide data for DEM inversions. The EUI 17. 4 mn channel could also help constrain the DEM, as well as STIX in the high-temperature range (see e. g  Schmelz et al. 2009 , for DEM inversions from Hinode/XRT combined with RHESSI). However, DEM inversions involving SPICE cannot be computed systematically, as this depends on the choice of wavelength windows in the study used for the SPICE observation.
Magnetic connectivity: The previous sections of the paper have discussed strategic developmenets aiming at improving our understanding of coronal plasma parameters. To establish physical connection between RS observations of the Sun and IS measurements made along its orbit implies estimating systematically (and automatically) the magnetic connectivity between the Sun and the S/C at all times, both for past events and as a forecast (with at least a few days lead time). The outcome of this modeling effort will serve both Solar Orbiter science operators with a tool to aid observation planning, and the scientific community with an invaluable new tool for data analysis. A number of difficulties arise, the major ones being the need to coordinate many different datasets and numerical models, the large number of sources of uncertainty in the data, the intrinsic biases of the models, and the need to couple different physical domains.
As a first step and in order to address the problem of magnetic connectivity, the MADAWG proposed the development of a new modular modeling framework/pipeline that brings together different models from the community and that can be set up to work equally well for forecasting connectivity and for post-event analysis. This 'connectivity tool' accessible at http://storms-connectSolarOrbiter. irap. omp. eu/ relies on a back-end pipeline that works continuously and in realtime, and on web-based front-ends. The tool relies on simple and well established models for robustness and ease of analysis during operations. It performs multiple simultaneous estimates of connectivity based on different combinations of models and datasets rather than choosing only one model/data combination. This is a way to assess the quality and uncertainty of the connectivity estimates.
Several tracing methods are available at both stages according to the operation mode. Differences between operation in forecast or in science mode consist essentially in using archived or forecast magnetograms, and on using in-situ measurements of solar wind speed to constrain the shape of the Parker spiral. Finally, the last module traces the magnetic footpoints at the surface of the sun taking into account the estimated Sun-S/C propagation delays for different types of phenomena and perform estimations of connectivity uncertainty compared to the RS instruments fields of view. Observation and model data storage is ensured by the PI institution of each respective dataset, and only a minimal subset of parameters is requested and downloaded by the server running the connectivity tool to initiate each of its modules in order to minimise the required daily data transfer rate.
Although this process can be repeated every day throughout the RS window, the adjusted pointing profile will only take effect 3 days later than the time the LLD were acquired. The LLD for each instrument have been defined to best serve evaluation of instrument health, data quality and S/C pointing accuracy. In addition, LLD can be used to shed a light on the current solar activity, including the presence of potential targets, to provide context for the connectivity models, and to decide which data to prioritize or select for downlink. 7 As it stands, EUI LLD contain full disk, highly compressed images in 304Å and 171Å at 15 to 30 minute cadence plus one higher quality dataset per day. PHI LLD consists of one full disk magnetogram and a continuum intensity image at medium resolution. Metis LLD provides one set of white-light and ultraviolet images of the corona and light curves in 8 coronal sectors.
Methodology to help with data management onboard The connectivity tool can contribute to the prioritisation of data downlink for instruments with internal memories (PHI, EUI, STIX) and selective downlink for EPD and RPW, by helping the instrument teams to make decisions on what datasets have higher scientific importance and are therefore worth being downlinked in priority. These could correspond to, for example, flare source (RS) data connected to an event that has been measured IS or vice versa, or an IS burst mode dataset that is probably linked to a region that has been observed by RS high resolution imagers. Once data have been acquired by Solar Orbiter, the LLD data can be used to improve and/or validate the connectivity diagnostic between a solar feature or event and its impact at the S/C position.
Connectivity tool for science The modelling infrastructure developed and implemented for the Connectivity tool will be equally useful for the analysis of the downlinked Solar Orbiter data, and more broadly for post-event analysis based on other (past or future) missions. The tool includes a "science mode" that follows the same strategy than the one described previously for the forecast (or operations) mode, differing only on the combinations of models and datasets available to drive the tool, and being more oriented towards past-event analysis. By design, the connectivity tool aims at promptly providing Sun-to-S/C connectivity estimation given any complete set of surface magnetogram / coronal field, solar wind model (or asymptotic wind speed values, at minimum) and S/C orbital position. As such, the connectivity tool will provide the users multiple ways of relating events occurring in the interplanetary space and at the surface of the Sun (or in close proximity).
Multiple S/C analysis, including determining if, e. g, different S/C are connected to the same region of the Sun and/or whether they are placed in the same propagation path will also be within close reach. Even though the connectivity tool already couples together a chain of well identified models (for operational reliability), it should be seen as a modelling framework providing a series of protocols and interfaces that are open to the wider scientific community and ready to interact with different models and databases. As such, it will be possible to drive the connectivity tool (or even just some of its modules) using external data, and equally to serve external models with data generated by any of its different layers (see section 5. 2. 2). Examples illustrative of this synergy are global heliospheric models, such as EUHFORIA  (Pomoell & Poedts 2018)  and ENLIL  (Odstrcil & Pizzo 1999) , that can benefit from the connectivity tool performs studies requiring knowledge of the connectivity (and propagation delays) across the solar corona, below their numerical domain.
These delays can be fed back to the connectivity tools to improve its estimates. On the other hand, the information provided by the tool can be used to test and validate different heating and acceleration scenarios used by MULTI-VP. The model will also provide detailed phase speed profiles (e. g sound and Alfvén speeds). Future developments of this and other similar models will include multi-species plasmas and allow for fuller exploitation of Solar Orbiter's IS data, namely that of SWA. The connectivity tool is implemented as a platform that enables data queries between different solar and space plasma databases using criteria based on connectivity rather than simple spatio-temporal interval searches. Criteria for an array of different phenomena could then be set, from wind plasma propagation to shocks and wavefronts, and SEPs.
Analysis of the uncertainty in magnetic connectivity The estimation of surface to S/C connectivity necessarily involves coupling a variety of datasets and models, each of them having their own biases (that cannot always be precisely quantified). It is therefore necessary to devise a method that assesses the quality and the reliability of the connectivity estimations produced by the tool. As explained previously, the connectivity tool goes through two main steps (in its simplest mode of operation): tracing the "Parker spiral" across the interplanetary medium, and tracing the magnetic path across the corona. The former is affected mostly by the uncertainties related with the asymptotic wind speed forecasted at the (future) s/c orbital position, and by the corresponding wind acceleration profile, leading to a continuous region of connectivity at the top of the corona.
The latter is more challenging, being affected by the complex topology of the corona itself, such that each connectivity point can be associated with multiple disconnected regions of connectivity at the surface of the Sun. These attributes altogether make a conventional uncertainty and error propagation analysis unfeasible. So far, we therefore favoured a different strategy, based on computing several estimations of connectivity simultaneously, based on multiple combinations of datasets, models, and/or on different sets of parameters for the same models (e. g, different wind speeds at the s/c position). This approach is a form of ensemble modeling, for which the added redundancy (due to making multiple estimations of the same quantity) translates to a better sampling of the region of uncertainty.
2018). The Solar Orbiter instrument teams have committed to providing fully calibrated data, along with higher level data products derived from the directly detected data, further obviating the need for outside researchers to use mission-specific software. Depending on the product, the higher level data files may be in FITS or CDF format (e. g. FITS files containing heliographic synoptic maps) or in a more suitable format for viewing, such as JPEG2000 browse images for use within JHelioviewer  (Müller et al. 2017 , , see also section 6. 4. 1), MP4 for movies, or PDF for time-series plots.
Python is seen as the wave of the future, not only for heliophysics but for science in general. The use of Python for Solar Orbiter is assisted by the the SunPy 13 project (SunPy  Community et al. 2015) , which aims to do for Python what SolarSoft does for IDL, provide an integrated analysis environment for heliophysics data. The Connectivity Tool described in section 5. 1 was mostly written in Python and exploits the SunPy package. Aside from the specific language used for software development, the importance of version control has become better appreciated within the heliophysics community. Many packages written in Python, including SunPy, use GitHub 14 for version control and distribution to the community. Solar Orbiter instrument teams that plan to distribute software to the community in Python in conjunction with SunPy also plan to use GitHub.
2019 ). This will include uncalibrated Level 1 files, calibrated Level 2, and some derived Level 3 products such as mosaics, FIP maps, heliographic maps, and time-distance plots ("J-maps"). Some non-13 https://sunpy. org 14 https://github. com 15 https://virtualsolar. org FITS Level 3 products may also be served through the VSO as appropriate.
JHelioviewer JHelioviewer  (Müller et al. 2017 ) is a Java-based visualization tool for solar image data. Together with the web application https://helioviewer. org, it is part of the joint ESA/NASA Helioviewer Project 16. The large data volumes from modern solar observatories are reduced to manageable levels by compressing images using the JPEG2000 standard  (Taubman & Marcellin 2002) , and serving them through the JPIP streaming protocol. This optimizes the delivery of usable resolution within the selected region of interest while still delivering the full (compressed) resolution in the background. In addition, JHelioviewer supports loading some calibrated datasets in FITS format and will support the Solar Orbiter metadata standards. The current capabilities of JHelioviewer include the visualisation of time series of image data from multiple sources (e.
2019). JHelioviewer can also receive data over the IVOA SAMP protocol  17. One application is the serving of calibrated science data directly from the ESAC archive. The program state itself can also be transmitted in this manner, opening possibilities for remote collaboration. The connection to JHelioviewer over SAMP can also be initiated from a web page, which makes this feature particularly useful for catalogues of events or models (demos at http://swhv. oma. be/test/samp/). EUHFORIA model results have recently been integrated in JHelioviewer and will be available on a daily basis to complement the Solar Orbiter data and both will be visualized side by side through the JHelioviewer interface (see Figure  17  and section 6. 4. 1). EUHFORIA will model self-consistently the propagation of the solar wind, the deformation, deflection and erosion of CMEs evolving in the inner heliosphere.
6, CDAWeb, Cluster Science Archive, LatHyS database) thanks to standardized protocols (SAMP, EPN-TAP) and web-services. Figure  18  illustrates some of these functionalities by displaying the orbits of Solar Orbiter and PSP together with their magnetic connection to the Sun; at 30R s from the Sun the radial velocity modelled by MULTI-VP  (Pinto & Rouillard 2017 ) is colour-coded. Other functionalities include, for example, displaying radial and Parker field line connectivity for S/C onto Carrington maps, representation of the Heliospheric Current Sheet  (Pei et al. 2012)  or instrument fields of view. Screen shots and video captures are possible. Future implementations will take benefit of the results of the magnetic connectivity tools discussed in Section 5. 1.
The tool offers also access to predefined catalogues of CME trajectories (such as those created by the HELCATS projectwww. helcats-fp7. eu) or the possibility to fit traces observed in J-maps directly. The same functionalities can be exploited to locate CIRs and their arrival times. Once a propagation has been carried out with the tool and a launch time has been estimated at the Sun, the user can then navigate between different databases and other tools such as AMDA or JHelioviewer webpage. For SEPs studies the tool provides estimates of S/C magnetic connectivity to the visible solar disk, locations of flare occurrence, browsing of simulation results of the solar corona (PFSS, MHD) and access to the results of 3D reconstructions of shock waves. Future developments include the incorporation of J-maps from future heliospheric imagers such as WISPR  (Vourlidas et al.
2016 ) and Solar OrbiterHI  (Howard et al. 2019)  as well as the possibility of visualising J-maps along position angles situated outside the ecliptic plane. 6. 6. IS visualisation and data mining tools AMDA 21 , Automated Multi Dataset Analysis  (Jacquey et al. 2010; Génot et al. 2010) , is developped by the CDPP for more than 12 years and specializes in data visualisation and analysis of plasma physics data, from ground-based facilities, space missions or simulations and models. The software enables easy manipulation of parameters (scalars, vectors or spectrograms) which may be combined directly in the browser interface (see an illustration on Figure  20  here captured from a Firefox window). Mathematical criteria can be applied on vast volume of data to produce event lists or catalogues.
Users have their own workspaces such that all sessions (and associated new parameters, catalogues, plot layouts,. ) are logged and saved on the long term. This simplicity helped popularizing the tool which is used by researchers and students alike. A large variety of data are available either directly (duplication from NASA/PDS for planetary missions, or Cluster Sience Archive, for instance), through web-services (CDAWeb, simulations,. ), or by user upload. On the VO side, SAMP is enabled and all AMDA data are also accessible via EPN-TAP as can be seen on the VESPA portal. In 2014, the ability of AMDA to handle heterogeneous datasets has decided the Rosetta Plasma Consortium leads to ask CDPP to provide a dedicated portal to the team; AMDA has evolved accordingly to allow the Rosetta Plasma Consortium (RPC) group to access their proprietary data during all operations before the ESA/PSA archive went public.
On the other hand, the short RSWs require careful target selection to optimize the studies of solar transient activity. The latter comprise half of the So-21 http://amda. cdpp. eu/ lar Orbiter science objectives and hence are too important in the success of the mission to leave progress on them to serendipity alone. Here, space weather-focused tools developed to improve forecasting of transient activity can be deployed to great benefit in conjunction with the other tools described elsewhere in this paper. In the rest of the section, we discuss briefly how space weather efforts can inform and be informed by the Solar Orbiter data analysis and modeling activities. We illustrate that Solar Orbiter will be tremendously important for space weather science even though it is not a surveillance/monitoring platform.
Coupling of a physics-based (SOLPENCO;  Aran et al. 2008)  and an empirical data driven approach (Kahler & Ling 2017) will provide the expected SEP time profile in energies (E>10-; >30-; >100and >330 MeV). Solar Orbiter and SEP forecasting tools have a mutually beneficial relation. On one hand, the application of such SEP forecasting tool to Solar Orbiter operations is straightforward since from the SHARP identifications a forecasting of the radiation environment 3Days in advance could be achieved. This will also include the expected SEP time profile. Thereby, planning teams can evaluate the situation at hand and decide how to proceed. On the other hand, the datasets provided by Solar Orbiter would be uniquely suitable to test the empirical relations and corresponding predictors that form the basis for forecasting/nowcasting tools.
Solar Orbiter Contributions to Space Weather Research Several outstanding questions in space weather research, such as CME initiation, propagation and interaction with the ambient environment, stand to benefit greatly from Solar Orbiter's unique viewpoints and complementary payload. The extended coverage of the photospheric magnetic field and the detailed observations of the CME source regions will improve two key Space Weather issues: (1) modeling of the solar wind (discussed in 4. 1. 2) and (2) understanding of CME initiation. Data from PHI, EUI, and Metis will provide a unique insight on the magnetic field and plasma evolution during magnetic flux rope and sheared arcades ejections. These events present a challenge for modelling as different physical regimes, time and spatial scales, are involved, when the free magnetic energy is built up prior to the eruption and it is quickly and suddenly released.
Conclusions The coming years for heliophysics research promise to unravel a number of outstanding mysteries about our Sun and how it couples to the heliosphere. We described the current state of the art in data-analysis tools and models that can exploit and benefit from the type of data that will be obtained by Solar Orbiter with a particular focus on tools that ease comparison of RS with IS data. The on-going effort by ESA's MADAWG aims at providing the tools necessary to support mission operations during data acquisition and in the process of on-board data management and downlink to Earth. As our understanding of the corona improves over the next years, these tools and techniques will no doubt evolve further strengthening our procedure and methodology. A number of existing assets that have greatly benefited from the last decade of space missions are currently being modified so that Solar Orbiter data can be directly integrated in them.
This requires that data formats follow international standards and exchange protocols that have also been integrated by the MADAWG. Several models not discussed here are currently being developed to integrate data from more instruments on Solar Orbiter and other observatories such as PSP and ESA's ASPI-ICS instrument on PROBA-3. Little work has been done so far at preparing ground-based support for Solar Orbiter and this constitutes one our next challenges. These are exciting opportunities for young researchers in heliophysics that are always welcome to join our dynamic working group.
Acknowledgements. Solar Orbiter is a joint  ESA  and  NASA mission. A. Vourlidas' Solar Orbiter  effort is supported by  NRL  grant  N00173-16-1-G029. P. Pagano would like to thank  D. H. Mackay  and  S. L. Yardley  for their valuable contributions, the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation program  (grant agreement No. 647214 ), and the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (http://www. dirac. ac. uk). This equipment was funded by a  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/K00087X/1 ,  DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure.