Forecasting space weather over short horizons: Revised and updated estimates

Abstract

Space weather reflects multiple causes. There is a clear influence for the sun on the near-earth environment. Solar activity shows evidence of chaotic properties, implying that prediction may be limited beyond short horizons. At the same time, geomagnetic activity also reflects the rotation of the earth's core, and local currents in the ionosphere. The combination of influences means that geomagnetic indexes behave like multifractals, exhibiting nonlinear variability, with intermittent outliers. This study tests a range of models: regressions, neural networks, and a frequency domain algorithm. Forecasting tests are run for sunspots and irradiance from 1820 onward, for the Aa geomagnetic index from 1868 onward, and the Am index from 1959 onward, over horizons of 1–7 days. For irradiance and sunspots, persistence actually does better over short horizons. None of the other models really dominate. For the geomagnetic indexes, the persistence method does badly, while the neural net also shows large errors. The remaining models all achieve about the same level of accuracy. The errors are in the range of 48% at 1 day, and 54% at all later horizons. Additional tests are run over horizons of 1–4 weeks. At 1 week, the best models reduce the error to about 35%. Over horizons of four weeks, the model errors increase. The findings are somewhat pessimistic. Over short horizons, geomagnetic activity exhibits so much random variation that the forecast errors are extremely high. Over slightly longer horizons, there is some improvement from estimating in the frequency domain, but not a great deal. Including solar activity in the models does not yield any improvement in accuracy.