Technical Reports

s/n Title Authors Description Date | DOI
1 MSTID forecasting methodology and data inventory S. Mani (IAP-L), J. Mielich (IAP-L), T. Verhulst (RMI), D. Burešová (IAP-P), V. Barta (FI), A. Belehaki (NOA),  D. Altadill (OE), A. Seggarra (OE), D. Kouba (IAP-P), K. Berényi (FI)

T-FORS project aims at providing new models able to interpret a broad range of observations of the solar corona, the interplanetary medium, the magnetosphere, the ionosphere and the atmosphere, and to issue forecasts and warnings for TIDs several hours ahead. T-FORS expect to develop prototype services based on specifications from the users' community with a comprehensive architectural concept allowing for possible future adjustments in order to develop a real-time operational service. This document is a report for the definition of the MSTID forecasting models.

May 2023 | 10.5281/zenodo.8085474
Compilation of Stakeholders’ Requirements  I. Galkin (BGD), A. Belehaki (NOA), P. Brouard (ONERA), J.-P. Molinie (ONERA), J. Toelle (GFP), S. Unger (GFP), L. Spogli (INGV)

This document is the initial review of stakeholders’ mandatory and desirable requirements gathered among those affected by TID phenomenon. Some of the contributors represent T-FORS consortium members: GFP for high-frequency (HF) communications, INGV for N-RTK, and ONERA for the OTH radar. The requirements are named, described, and assigned an identification number and priority.

June 2023 | 10.5281/zenodo.8159035
Initial T-FORS standars, quality control and best practices
I. Galkin (BGD), A. Belehaki (NOA), C. Cesaroni (INGV)

This document is the initial review of catalogues of data and data-products required for the T-FORS development, focusing on their compliance to the modern data management standards. Best practices for quality control of data-products that result from TID detection methodologies are compiled and specific techniques to assess their quality (i.e., timely availability and scientific reliability of the results) are proposed, including a monitoring system for the quality of the raw observational data. The areas for potential improvements in the TID methodologies are also identified.

December  2023 |
Large scale TID preliminary catalogue-based machine learning algorithm C. Cesaroni (INGV), L. Spogli (INGV), V. Ventriglia (INGV), M. Guerra (INGV)

Large scale TID preliminary catalogue-based machine learning algorithm.

December 2023 | 10.5281/zenodo.10418090
A preliminary code for LSTIDs forecast based on the RNN classifier K. Themelis (NOA), K. Koutroumbas (NOA), A. Belehaki (NOA)

This is a preliminary code that provides forecast of LSTIDs based on the RNN classifier using as features the auroral electrojet indices IL and IU provided by the FMI IMAGE network and the Gradient GNSS TEC Activity Index provided by DLR. The labels are the Spectral Energy Contribution (Spcont) for detected LSTIDs over Digisonde stations (in this case data from the Juliusruh Digisonde are used) calculated with the HF Interferometry method.

December 2023 | 10.5281/zenodo.10442654