Periodic Reporting for period 1 - T-FORS (TRAVELLING IONOSPHERIC DISTURBANCES FORECASTING SYSTEM)
Reporting period: 2023-01-01 to 2024-12-31
a. Develop new prediction models based on databases of detected TID characteristics and of their drivers developed in the frames of past Horizon 2020 and national projects, using Machine Learning algorithms to forecast the occurrence and propagation characteristics of large scale TIDs and statistical modelling to estimate the occurrence probability and propagation pattern of medium scale TIDs;
b. Improve scientific understanding of the origin and evolution of TIDs that will lead to a proposed inventory of potential early indicators, assessing the validation results of the prediction models;
c. Develop prototype services based on requirements from the users' community and following harmonized standards and quality control procedures similar to the best practices of meteorological services and relevant community activities;
d. Perform on ground demonstration tests for the validation of the usability of the T-FORS prototype services, analyzing the effects of TIDs on HF skywave radars and relevant applications and the effects on HF direction finding systems;
e. Propose a comprehensive architectural concept, including the densification of ground instrument networks, and new space missions, and possible future adjustments in order to develop a real-time operational service compatible and complementary to the ESA Space Weather services.
Two comolementary models provide Large Scale Travelling Ionospheric Disturbances (LSTID) forecasts with a forecasting horizon up to three hours. The models tackle the complexity of physical processes in various regions of the atmosphere, ionosphere, thermosphere, magnetosphere and heliosphere, and provide forecasting with different time horizons. Figure 1 reports the conceptual flow and datasets describing the chain of the physical processes involved in the occurrence of LSTIDs. The two different models ares:
a. Catalogue-based forecasting (3-hrs forecasting horizon), based on the catalogue of LSTID events provided by the HF Interferometry Method and on the exploitation of CatBoost classifiers;
b. LSTID forecasting (2-h forecasting horizon) over Digisonde locations, based on the Spectral Energy Contribution (SEC) index that it is provided by the HF Interferometry Method and on the exploitation of Temporal Fusion Transformers (TFT) classifiers.
The validation includes statistical evaluation, explainable artificial intelligence approaches, and scientific analysis of case events. The validation results provided an improved scientific understanding on the triggering and propagation of LSTIDs, resulting also in an inventory of early indicators of LSTIDs.
To approach the problem of MSTID forecasting, a climatology of the variability of detrended Total Electron Cnotent (TEC) was established (Figure 2), and extreme MSTIDs are considered present when the observed dTEC falls outside the middle quartiles of variability. This method was tested during periods of strong tropospheric disturbance over central Europe, and periods of substorm activity, when MSTIDs were known to be present from the analysis of ionosonde and Doppler sounding data.
The TIDforecasting algorithms and all the software codes are provided with open access.
The efficiency of the T-FORS products are tested with on ground demonstrations. The experiments use one transmitter of the Nostradamus array in France and as receiver the direction-finding (DF) system in Germany.
The efficiency of the receiver was verified based on the azimuth of arrival of the transmitted signals. In most of the cases of failures in receiving the transmitted signal, T-FORS models using the TFT Machine Learning method, forecasted the occurrence of TID activity over Dourbes Digisonde that is located very close to the reflection point between the Transmitter and Receiver in this experiment.
Following this confirmation phase, the final T-FORS forecasting codes are released with Open Access. A roadmap is also proposed for the transition to operations, including a list of recommended High Level Data Products that meet the ESA and WMO requirements.
The T-FORS forecasts are assessed, using experimental data obtained from experiments. The results provide important evidence regarding the physical characteristics that influence the accuracy of the models’ input data and output results.
The operational implementation of the T-FORS models is considering a data management plan based on real-time data acquisition, quality control, synchronization and normalization. In such environment, the models shall transition to operations, with the development of data products adapted to the users’ needs considering the requested forecasting horizon and the preferred level of precision and sensitivity.