The Heliospheric Space Weather Center project is the result of the synergy between the Aerospace Logistics Technology Engineering Company (ALTEC S.p.A.) and the INAF-Astrophysical Observatory of Torino, both located in Turin, Italy. The main goal of this project is to provide space weather medium and short-term forecast, by combining remote-sensing and in situ open data with novel data analysis technologies, giving to scientists the possibility of designing, implementing, and validating space-weather algorithms using extensive data sets

Casti, M.

Chiesura, G.

Mulone, A. F.

Susino, R.

Telloni, D.

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DOI 10.1393/ncc/i2019-19048-3Colloquia: SoHe3 2018IL NUOVO CIMENTO 42 C (2019) 48The Heliospheric Space Weather Center: A novel space weatherserviceM. Casti(1), A. F. Mulone(1), R. Susino(2), G. Chiesura(1), D. Telloni(2),R. De March(1), E. Antonucci(2), R. Messineo(1), A. Bemporad(2),F. Solitro(1), S. Fineschi(2), E. Magli(3), G. Nicolini(2), F. Caronte(1)and M. Messerotti(4)(1) ALTEC S.p.A. - Corso Marche 79, 10146 Torino, Italy(2) INAF, Osservatorio Astrofisico di Torino - Via Osservatorio 20, 10025 Pino Torinese,Italy(3) Politecnico di Torino - Corso Duca degli Abruzzi 24, 10129 Torino, Italy(4) INAF, Osservatorio Astronomico di Trieste - Loc. Basovizza 302, 34149 Trieste, Italyreceived 28 December 2018Summary. — The Heliospheric Space Weather Center project is the result of thesynergy between the Aerospace Logistics Technology Engineering Company (AL-TEC S.p.A.) and the INAF-Astrophysical Observatory of Torino, both located inTurin, Italy. The main goal of this project is to provide space weather mediumand short-term forecast, by combining remote-sensing and in situ open data withnovel data analysis technologies, giving to scientists the possibility of designing,implementing, and validating space-weather algorithms using extensive data sets.1. – IntroductionThe functioning and the reliability of electrical/electronic and space-based technolo-gies are essential elements for modern society. As a consequence, our vulnerability tospace weather phenomena is rapidly increased, giving rise to technological hazard relatedto navigation and timing systems, electrical power grids, radio communication, etc. Forthis reason, it is extremely important to be able to predict such phenomena, for limitingtheir impact [1].Space weather phenomena have their origins in the Sun and propagate through the he-liosphere, until reaching Earth magnetosphere. Their signatures are observable in severaldata sets, acquired by in situ and remote sensing space and ground-based instruments,with different time-scales. For this reason, a space weather forecast system should beable to manage several data types providing the event forecast within a processing timeas short as possible.Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0) 12 M. CASTI et al.The Heliospheric Space Weather Center (HSWC) [2] has been developed aiming atanalyzing solar and heliospheric data, providing a quick response. Data analysis is per-formed by three different pipelines, which detect Coronal Mass Ejections (CMEs) andretrieve the physical features necessary for forecasting their impact time on the Earthmagnetosphere. One of the main feature of the HSWC is the use of metadata for enablingdata access.In this paper at first, a brief description of used data and algorithms is presented.The system architecture is described afterwards, in order to highlight its features. Then,data management elements are pointed out with a particular interest on the adopteddata model. At last, conclusions and future prospects are discussed.2. – Solar data for space weather forecastThe Heliospheric Space Weather Center main objective is to detect Coronal MassEjections in their early stages and to provide a forecast related to their impact on Earthmagnestosphere. Since the absence of a well-defined forecast classification in literature,we referred to medium-term forecasting as predictions up to 2 days and to short-termforecasting as predictions up to 2 hours. The medium-term and short-term forecastprediction services provided by the Heliospheric Space Weather Center consist of high-speed solar wind streams and magnetic clouds ejected by the Sun identification plusthe alerting for phenomena reaching the Earth’s magnetosphere. Aiming at this, threedifferent pipeline, each of them based on a specific type of data, have been implemented.2.1. Medium-term forecast . – The set of algorithms composing the data pipeline formedium-term forecast processes images of the solar corona acquired by the LASCOcoronagraph, payload of the NASA SOHO satellite. These data are available with afrequency of about 12 minutes and are acquired by two different detectors, i.e. C2 andC3. Each one of them observes a different portion of the solar corona: C2 field of viewgoes from 1.5 to 6 solar radii, while C3 images the corona from about 3.5 to 30 solarradii. These images give the opportunity to observe CMEs in their first manifestationand evolution. In this respect, when a CME is detected, all the images in which it appearsare used to compute its physical features and kinematic parameters. The outcome is aforecast of the detected CME arrival onto the Earth’s magnetosphere up to a few daysahead.2.2. Short-term forecast . – Short-term forecast services are provided by processingdata acquired by in situ instruments, which are on-board of satellites orbiting aroundthe Lagrangian point L1. In particular, these data concern the heliospheric magneticfield, the solar wind velocity, the proton density and temperature. The designed pipelineprocesses in near-real time data acquired by NOAA DSCOVR satellite payloads andpredicts geomagnetic storms. These measurements are available with 1-minute frequencyand the pipeline forecast is provided up to a few hours before the storm occurrence. Whena CME propagates through the heliosphere, the previous listed physical properties of theinterplanetary medium assume a characteristic pattern. The algorithms that composethis pipeline are able to detect the CME transition at L1 and to provide the relatedforecast.2.2.1. Machine learning. Short-term forecast is also provided by the neural networkpipeline, which processes DSCOVR payload data. The final goal is to predict DSTgeomagnetic index few hours in advance. As a matter of fact, DST index measures theTHE HELIOSPHERIC SPACE WEATHER CENTER: A NOVEL SPACE WEATHER SERVICE 3geomagnetic activity and it is used to assess the severity of the magnetic storms caused bythe CMEs. The pipeline is based on a trained neural network to tell if a weak, moderateor intense storm is about to happen in 2, 4 or 8 hours.3. – System ArchitectureThe architecture of Heliospheric Data Center (HDC), a part of the Heliospheric SpaceWeather Center, consists of (i) data stores to archive input data, (ii) a metadata datastore and (iii) processing data stores for preparing data. All of them are handled by theproduct manager, which is a component that offers a transparent data access service.ALTEC defined and developed a framework whose main goal is to process big amountof data allowing a seamless connection between collected information and analyses per-formed by end users. This distributed framework is the ALTEC Space Data Processing(ASDP), designed to provide a flexible system capable to handle and process a largevariety and amount of data. ASDP allows integrating both existing and new codedalgorithms, enabling automatic processing of large data sets and complex pipelines.4. – Data Management4.1. Data Flow . – The main goal of data management is to retrieve data to be pro-cessed from several repositories, e.g. HTTP and FTP. Moreover, input data are het-erogeneous in terms of format and availability frequency. In fact, the HSWC pipelinesprocess images of the solar corona (fts files) and in situ measurements (json, cdf or netcdffiles). The component in charge of retrieving such data is the crawler, which subsequentlytriggers the internal ingestion and processing pipelines. All data ingested and generatedin the Heliospheric Data Center are available for the users.4.2. Data Model . – During the ingestion phase, metadata are extracted from eachproduct. This is a key factor to organize the solar data sets.The HSWC makes use of SPASE [4], the most widespread data model in virtual obser-vatory. SPASE allows describing all the resources in a heliophysics data environment. Itsaim is unifying and improving existing Space and Solar Physics data models, describingaccurately data structure.In order to describe processing pipeline products in output, SPASE is extended usingESPAS. ESPAS is a data e-infrastructure facilitating discovery and access to observa-tions and model predictions of the near-Earth space environment [3]. ESPAS gives theopportunity to relate the products with its processing, which is of the most importance.5. – Conclusions and future prospectsThe Heliospheric Space Weather Center has been developed in order to provide short-and medium-term forecast related to Coronal Mass Ejections and their impact on Earthmagnestosphere. Due to the center capacity to manage different data products, datastores, frameworks and libraries, future developments will be the data archive extensionand the development and integration of new pipelines that use physics-based algorithmand/or exploit deep learning techniques.4 M. CASTI et al.REFERENCES[1] Schrijver C. J. et al., Advances in Space Research, 55 (2015) 12 pp.2745-2807.[2] Casti M. et al., Proc. of the 2017 conference on Big Data from Space (BiDS’17), 1 (2017)pp.359-362 doi: 10.2760/383579.[3] Belehaki A. et al., Advances in Space Research, 58 (2016) .[4] Merka J., Narock T. W., Szabo A., Earth Science Informatics, 1 (2008) pp.35-42.

The Heliospheric Space Weather Center: A novel space weather service

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The Heliospheric Space Weather Center: A novel space weather service

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