Technical Reports

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.

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.

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.

Large scale TID preliminary catalogue-based machine learning algorithm.

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.

In this contribution, we propose a new short-term forecast model of Large Scale Travelling Ionospheric Disturbances (LSTIDs) occurrence at specific locations in Europe. The model uses as input data time series with the characteristics of LSTIDs drivers and detected events. The concept underpinning the selection of the input data is based on the phenomenological scenario that the intensity of the auroral electrojets is regulated by the Lorentz force and the Joule heating generates Atmospheric Gravity Waves (AGWs) in the lower thermosphere and LSTIDs in the ionosphere. Based on this scenario, the TEC gradients and the intensity of the auroral electrojets are representative drivers for LSTIDs occurrence. Detected LSTID events and their characteristics are calculated with the HF Interferometry method (HF-INT) over European Digisonde stations. The method looks for coherent oscillation activity in the Maximum Usable Frequency, MUF(3000)F2, and sets bounds to time intervals for which such activity occurs into a given region. HF-INT provides the Spectral Energy Contribution (SEC), which is the contribution of the LSTIDs to the total variability for a given time series. These features (drivers and detected characteristics) are used for the identification of LSTIDs utilizing Machine Learning tools and, more specifically, different types of classifiers, ranging from the traditional k-Nearest Neighbor classifier (k-NN), the Feedforward Neural Networks (FNNs), up to the more advanced Temporal Fusion Transformers (TFTs). Several experiments are performed for two distinct scenarios: (a) values of SEC greater than 50% indicating moderate and strong LSTID activity, and (b) values of SEC greater than 70% indicating strong LSTID activity. The performance is assessed through the F1-score metric, which takes values between 0 and 1 (the higher its value, the better the classifier performance). The forecasting accuracy decreases from 0.9 to 0.6 approximately with increasing forecasting horizon up to two hours ahead for TFT, while the FNNs have the next best performance, and k-NN has inferior performance.

December 2024 | 10.5281/zenodo.14537424

This report provides a description of the codes that estimate the climatological pattern of MSTIDs, developed in the T-FORS project. The MSTID climatology is based on the detrended TEC data and can be used as a long-term forecasting tool. The validation confirms the expected diurnal and seasonal variability of the physical phenomenon. Some evidence is also provided for the detection of extreme MSTID activity, however near-real time data are needed to confirm our capability to design alert messages.

December 2024 | 10.5281/zenodo.14557814