-The forecast of the time of arrival (ToA) of a coronal mass ejection (CME) to 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. We propose a statistical approach for the computation of the ToA using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.
-The forecast of the time of arrival (ToA) of a coronal mass ejection (CME) to 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. We propose a statistical approach for the computation of the ToA using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.
The forecast of the time of arrival of a Coronal Mass Ejection (CME) to Earth is of critical importance for our high−technology society and for the Earth's upper atmosphere status and LEO satellites. We realized a procedure based on the Drag−Based Model which uses probability distributions, rather than exact values, as input parameters, and allows the evaluation of the uncertainty on the forecast. We tested this approach using a set of CMEs whose transit times are known, obtaining extremely promising results. We realized a real−time implementation of this algorithm which ingests the outputs of automated CME tracking algorithms as inputs to provide early warning for those CME approaching Earth. We present the results of this real−time fast warning procedure for the case of the 2018 February 12 th CME.
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.
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