We present the development of physics-based models of solar-terrestrial regions from the solar surface to the Earth’s atmosphere at NICT. Our models consist of three regions: (1) the solar surface and solar wind, (2) the Earth’s magnetosphere-ionosphere, and (3) a model of the whole atmosphere from the troposphere to the ionosphere, called the Ground to Topside Model of Atmosphere and Ionosphere for Aeronomy (GAIA). We also have a solar wind and CME model, Space-weather-forecast-Usable System Anchored by Numerical Operations and Observations (SUSANOO). Furthermore, we have developed a high-resolution plasma bubble model. The coupling of these models is a future work.

Ray tracing techniques have been used to investigate numerical effects on the propagation of acoustic waves in a non-hydrostatic dynamical core discretised using an Arakawa C-grid horizontal staggering of variables (Arakawa & Lamb 1977) and a Charney-Phillips vertical staggering of variables (Charney & Phillips 1953) with a semi-implicit timestepping scheme. It is found that the space discretisation places limits on resolvable wavenumbers and redirects the group velocity of waves towards the vertical. Wave amplitudes grow exponentially with height due to the decrease in the background density, which can cause instabilities in whole-atmosphere models. However, the inclusion of molecular viscosity and diffusion acts to damp the exponential growth of waves above about 150 km. This study aims to demonstrate the extent to which numerical wave propagation causes instabilities at high altitudes in atmosphere models, and how processes that damp the waves can improve these model’s stability.

In the current paper, we investigate topological invariants, calculated by HMI LOS magnetograms as complexity descriptors of solar magnetic fields. We compared them with the physical parameters provided by the Space-weather HMI Active Region Patches (SHARP). We have repeated forecasting experiment of Stanford Solar Observatories Group with the same positive and negative active region patches database, but replace SHARP parameters with topological invariants of corresponding LOS magnetograms. The classification results turned out practically identical to those obtained by the Stanford Solar Observatory group. This means that using LOS magnetograms retains enough complexity for magnetic field description.

We apply a novel pre-flare tracking of sunspot groups towards improving the estimation of flare onset time by focusing on the evolution of the 3D magnetic field construction of AR 11429. The 3D magnetic structure is based on potential field extrapolation encompassing a vertical range from the photosphere through the chromosphere and transition region into the low corona. The basis of our proxy measure of activity prediction is the so-called weighted horizontal gradient of magnetic field (WGM) defined between spots of opposite polarities close to the polarity inversion line of an active region. The temporal variation of the distance of the barycenter of the opposite polarities is also found to possess potentially important diagnostic information about the flare onset time estimation as function of height similar to its counterpart introduced initially in an application at the photosphere only in Korsós et al. (2015). We apply the photospheric pre-flare behavioural patterns of sunspot groups to the evolution of their associated 3D-constructed AR 11429 as function of height. We found that at a certain height in the lower solar atmosphere the onset time may be estimated much earlier than at the photosphere or at any other heights. Therefore, we present a tool and recipe that may potentially identify the optimum height for flare prognostic in the solar atmosphere allowing to improve our flare prediction capability and capacity.

Forecasting Solar Energetic Particle (SEP) fluence, as integrated over an SEP event, is an important element when estimating the effect of solar eruptions on humans and technology in space. Current real-time estimates are based on SEP measurements at a single location in space. However, the interplanetary magnetic field corotates with the Sun approximately 13° each day with respect to Earth, thus in 4 days a near-Earth spacecraft will have changed their connection about 60° from the original SEP source. We estimate the effect of the corotation on particle fluence using a simple particle transport model, and show that ignoring corotation can cause up to an order of magnitude error in fluence estimations, depending on the interplanetary particle transport conditions. We compare the model predictions with STEREO observations of SEP events.

This paper summarizes the first tool that is able to predict Ground Level Enhancements (GLE). It makes real-time predictions of the occurrence of GLE events from the analysis of soft X-ray and differential proton flux measured by the GOES satellite network. Before the development of this tool, space weather systems have been warning users about evolving GLE events by processing neutron measurements recorded on ground level. This tool, called HESPERIA UMASEP-500, can predict GLE events before the detection by any neutron monitor (NM) station. The prediction performance measured for the period from 1986 to 2016 is presented for two consecutive periods, because of their notable difference in performance. For the 2000-2016 period, this prediction tool obtained a probability of detection (POD) of 53.8% (7 of 13 GLE events), a false alarm ratio (FAR) of 30.0%, and average warning times (AWT) of 8 min and 15 min with respect to the first NM station’s alert and the GLE Alert Plus warning, respectively. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 637324.

Forecasting the time of arrival of a Coronal Mass Ejection at Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. Here a statistical approach to the computation of the time of arrival using the Drag-Based Model is proposed through the introduction of probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast.

The potential-field source-surface (PFSS) model of the solar corona is a widely used tool in the space weather research and operations. In particular, the PFSS model is used in solar wind forecast models which empirically associate solar wind properties with the numerically derived coronal magnetic field. In the PFSS model, the spherical surface where magnetic field lines are forced to open is typically placed at 2.5 solar radii. However, the results presented here suggest that setting this surface (the source-surface) to lower heights can provide a better agreement between observed and modelled coronal holes during the current solar cycle. Furthermore, the lower heights of the source-surface provide a better match between observed and forecasted solar wind speed.

We developed a flare prediction model based on the supervised machine learning of solar observation data for 2010-2015. We used vector magnetograms, lower chromospheric brightening, and soft-X-ray data taken by Solar Dynamics Observatory and Geostationary Operational Environmental Satellite. We detected active regions and extracted 60 solar features such as magnetic neutral lines, current helicity, chromospheric brightening, and flare history. We fully shuffled the database and randomly divided it into two for training and testing. To predict the maximum size of flares occurring in the following 24 hours, we used three machine-learning algorithms independently: the support vector machine, the k nearest neighbors (kNN), and the extremely randomized trees. We achieved a skill score (TSS) of greater than 0.9 for kNN. Furthermore, we compared the prediction results in a more operational setting by shuffling and dividing the database with a week unit. It was found that the prediction score depends on the way the database is prepared.

We perform simulations of the interplanetary coronal mass ejections relating to the magnetic storm on 17 March 2015. A hierarchical mesh structure is used, which is controlled by an adaptive mesh refinement technique, with fine-scale cells where it matters, the structure of the running shock waves of the coronal mass ejections and co-rotating interactive regions. The initial and the inner-boundary conditions are derived from another simulation, which uses a split dodecahedron grid. The resulting shock-wave with the models adjusted to the observed ejection speed on the sky plane show delays by 20% in the arrival time at the Earth from the observed data. By contrast, the model adjusted to the observed arrival time at the Earth needs the ejection speed 30% higher than that in the above models.

The opportunity of development the short-term forecasting technique of geoeffective solar flares is presented in this study. This technique is based on the effect of growth the fluctuations of horizontal component of geomagnetic field before the solar proton flares, that is considered as a prognostic parameter of solar proton flares.

In this report we present a possible scheme of short-term CME detection forecasting developed on the basis of statistical analysis of solar radio emission regularities prior to “isolated” solar Coronal Mass Ejections registered in 1998, 2003, 2009-2013.

Because of the significant dangers they pose, accurate forecasting of Solar Energetic Particle (SEP) events is vital. Whilst it has long been known that SEP-production is associated with high-energy solar events, forecasting algorithms based upon the observation of these types of solar event suffer from high false alarm rates. Here we analyse the parameters of 4 very high energy solar events which were false alarms, with a view to reaching an understanding as to why SEPs were not detected at Earth. We find that in each case at least two factors were present which have been shown to be detrimental to SEP production.

Space weather processes, in general, are non-linear and time-varying. In such cases ‘data driven models’ such as Neural Network, Fuzzy Logic and Genetic Algorithm based models were proved promising to be used in parallel with the mathematical models based on first physical principles. In particular, with the recent developments in ‘big data’ systems, one of the urgent issues is the development of new signal processing techniques to extract manageable, representative data out of the ‘relevant big data’ to be employed in ‘training’, ‘testing’ and validation phases of model construction. Since 1990, under the EU Frame Work Program Actions, we have developed such models for nowcasting, forecasting, warning and also for filling the data gaps on space weather cases including prediction of orbital spacecraft parameters. In particular, some typical, illustrative examples include the forecasting of the ionospheric critical frequencies foF2, during disturbed conditions, such as solar storms and extreme events; GPS total electon content(TEC); solar flare index during solar maximum and the construction of solar EUV flux variations. The associated input data organisation and the typical errors which have been within the acceptable operational expectations are summarised in terms of absolute values, percent and RMS. The aim of the paper is to show that the data driven approaches are promising for the forecasting of space weather.

The calibration pipeline of the level zero images obtained from the Magneto-Optical filters at Two Heights (MOTH II) instrument is presented. MOTH II consists of two 20 cm aperture instruments, each using a Magneto-Optical Filter (MOF): one at 5896 Å (Na D2-line), the other one at 7700 Å (K I-line). MOTH II instruments thus provide full disk line-of-sight Doppler velocity and magnetic field measurements at two heights in the solar atmosphere. The developed MOTH II pipeline employs a set of standard calibration corrections, a correction for signal leakage, due to the non-ideal behavior of the polarizers, and the geometrical registration between the eight images acquired by four CMOS cameras, relative to two components of the signal in two circular polarization states, in each of the two channels. MOTH II data are used to investigate atmospheric dynamics (e.g., internal gravity waves and magneto-acoustic portals) and Space Weather phenomena. Particularly, flare forecasting algorithms, based on the detection of magnetic active regions (ARs) and associated flare probability estimation, are currently under development. The possible matching of MOTH II data with SDO/HMI and SDO/AIA images into a flux rope model, developed in collaboration between Harvard-Smithsonian CfA and MIT Laboratory for Nuclear Science, is being tested.

Visible Emission Line Coronagraph (VELC) on board ADITYA-L1 is an internally occulted coronagraph with mirror as its primary objective element. It has a field of view (FOV) starting from 1.05 R⊙ – 3 R⊙. It will observe the corona in continuum centered at 5000 Å and will perform spectroscopic observations of inner corona in two visible (5303 Å and 7892 Å) and one infrared (10747 Å) wavelengths. VELC will be capable of observing the corona with high spatial and temporal resolutions. We present an overview of the inner coronagraph (VELC) design and introduce the concept of an on-board automated coronal mass ejections (CMEs) detection logic proposed for this payload.

The prediction of solar flares, eruptions, and high energy particle storms is of great societal importance. The data mining approach to forecasting has been shown to be very promising. Benchmark datasets are a key element in the further development of data-driven forecasting. With one or more benchmark data sets established, judicious use of both the data themselves and the selection of prediction algorithms is key to developing a high quality and robust method for the prediction of geo-effective solar activity. We review here briefly the process of generating benchmark datasets and developing prediction algorithms.

The Space WEeatherR TOr vergata university (SWERTO) service is an operational Space Weather service based on multi-instrument data from space-based (PAMELA, ALTEA) and ground-based (IBIS, MOTHII) instruments. The service (spaceweather.roma2.infn.it) is located at the Physics Department of the University of Rome Tor Vergata, Italy (UTOV) and will allow registered users to access scientific data from instrumentation available to UTOV researchers through national and international collaborations. It will provide intuitive software for the selection and visualization of such data and results from prototype forecasting codes for flare probability and Solar Energetic Particle (SEP) fluxes. The service is designed to promote access to technical and scientific information by the regional industries which employ technologies vulnerable to Space Weather effects. Basically, SWERTO aims to: i) design and construct a data-base with particle fluxes recorded by space missions and spectro-polarimetric measurements of the solar photosphere; ii) allow an Open Access to the data-base and to prototype forecasts to regional industries involved and exposed to Space Weather effects; iii) implement a tutorial and a FAQ section to help decision makers to became aware of and evaluate the risks from Space Weather events; iv) outreach and customer products. SWERTO has been financed by the Regione Lazio FILAS-RU-2014-1028 grant.

The aim of the Ionosphere Prediction Service (IPS) project is to design and develop a prototype platform to translate the prediction and forecast of the ionosphere effects into a service customized for specific GNSS user communities. The project team is composed by Telespazio (coordinator), Nottingham Scientific Ltd, Telespazio Vega Deutschland, the University of Nottingham, the University of Rome “Tor Vergata” and the Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV). The IPS development is conceived of two concurrent activities: prototype service design and development & research activity that will run along the whole project. Service design and development is conceived into four phases: user requirements collection, architecture specification, implementation and validation of the prototype. A sub-activity analyses also the integration feasibility in the Galileo Service center, located in Madrid. The research activity is the scientific backbone of IPS that will provide the models and algorithms for the forecasting products.

The paper outlines the space-weather-related observations, instrumentation, research and service developments carried out at the Geophysical Centre of the Royal Meteorological Institute (RMI) in Dourbes, Belgium.