Scientists are closer to predicting the timing and intensity of solar flares

Satellite-based technology is widely used every day. People rely on it to get directions, communicate, and handle financial transactions.

Solar flares can disrupt those signals. During these events, the ionosphere experiences changes in total electron content (TEC) that can affect navigation and communication.

Dr. Sarat C. Dass from the Indian Institute of Science, Bangalore has been studying these variations to find new ways to predict them more accurately.

What is a solar flare?

A solar flare is caused by the release of energy stored in the Sun’s magnetic fields. These fields can become twisted or tangled due to the Sun’s rotating surface and convection currents.

When these magnetic fields suddenly reconnect or “rearrange,” they release a burst of energy in the form of light, heat, and radiation. This energy is emitted as a flare.

Solar flares are often associated with sunspots, regions of intense magnetic activity on the Sun’s surface. The size and intensity of a flare depend on the strength and complexity of the magnetic fields involved in the eruption.

The challenge of solar flares

Solar flares have different categories, and X-class flares are considered among the most severe. They can release large amounts of energy that intensify geomagnetic activity and produce sudden spikes in ionospheric measurements.

Researchers have noted that strong flares can alter electron densities, which triggers abrupt shifts in satellite signal paths.

There is a lot at stake because airlines, emergency services, and financial networks depend on uninterrupted geolocation data. Higher error margins in positioning can be costly or even life-threatening in certain situations.

A new approach with deep learning

The increasing interest in machine learning has led to models that analyze and forecast ionospheric conditions with better precision. One such model uses bidirectional long short-term memory networks, a neural architecture that processes data forward and backward for improved context. 

The choice of the Adam optimizer is often praised for its ability to handle large volumes of data and adjust learning rates efficiently.

These specialized algorithms examine a variety of inputs, such as geomagnetic activity indicators and solar flux measurements, to predict TEC changes. The goal is to make real-time forecasts that can keep up with the ionosphere’s sudden fluctuations.

Step toward stable communication

“The Bi-LSTM-AO model exhibited exceptional accuracy in predicting TEC values across these dates, consistently outperforming the IRI-2020 model,” said Dr. Dass, lead author of the study.

The scientists compared the new model against traditional tools like the IRI-2020 and found that higher accuracy is especially noticeable during periods of intense solar activity.

Classic methods often struggle to handle abrupt shifts, but deep learning shows resilience when solar and geomagnetic conditions change quickly. Researchers see these improvements as a step toward stable positioning and communication services.

Why accurate predictions matter

Millions rely on precise location-based tools throughout the day. Small errors in timing or path calculations can create large uncertainties in real-time navigation.

Unexpected glitches have the potential to slow emergency response and disrupt shipping operations, both domestic and international.

In addition, companies invest considerable resources to shield systems from space weather issues. With more exact predictive models, they can reduce unforeseen outages and keep people connected and safe.

Potential for future applications

Users of satellite services may notice fewer dropouts or timing lags if advanced models are used to adjust signals on the fly. These adaptations can help mitigate the negative effects of powerful solar flares.

Industries reliant on precise time stamps, such as financial markets and data centers, might also benefit when transaction errors become less frequent.

Researchers anticipate that even stronger methods could come from combining novel architectures with expanded data sources. Partnerships among scientists worldwide can lead to more data sharing, better coverage, and faster model updates.

The push toward improved TEC forecasting has already shown it can increase confidence in satellite-based systems. By linking advanced neural networks with robust data inputs, experts have reduced the gap between predictions and actual conditions.

These results hint at exciting possibilities for real-time monitoring, especially during solar flares that might otherwise catch users off guard.

The study is published in the journal Advances in Astronomy.

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