Published: September 2, 2018 19:59:37 hours
Scientists say they have developed a new method for analyzing the Earth's magnetic field data that could provide better short-term predictions of geomagnetic storms. The Earth's magnetic field extends from pole to pole and is strongly influenced by the sun's solar wind, according to the research published in the journal Chaos. This "wind" is a stream of charged particles that are constantly being thrown out of the surface of the sun.
Incidental sudden flashes of brightness known as solar flares release even more particles, said researchers from the Potsdam Institute for Climate Impact Research in Germany. Sometimes the flares are followed by coronal mass ejections that send plasma into space. The resulting stream of charged particles travels millions of miles from the sun to the earth. The storms are serious and interfere with a number of key technologies, including GPS signaling and satellite communications. They can also cause damage to electrical power networks on the surface. Solar activity seems random, making it difficult for us to predict these storms.
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The researchers at the Potsdam Institute have developed the method based on a technique that has been developed for systems that are far removed from the equilibrium. The magnetic field of the earth fits into this paradigm because the field is driven far away from the equilibrium by the solar wind. Systems that are far from being in equilibrium often undergo abrupt changes, such as the sudden transition from a calm state to a storm. The researchers used hourly values of the Storm-time, or Dst, index. DST values give the average deviation of the horizontal component of the magnetic field of the earth relative to the normal value.
This deviation occurs when a large burst of charged particles comes out of the sun and the field generated by the earth is weakened. The DST values form a single stream of numbers known as a time series, researchers said. The time series data can then be converted back into a 2D or 3D image by plotting one data point against another at a fixed amount of time in the future for forecasts, they said.
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