Forecasting the solar modulation potential: Tests of time series models

IF 1.8 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Gordon Reikard
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Abstract

This study analyzes the predictability of the solar modulation potential using time series models. Recently, new data sets for the modulation potential have become available, at daily, monthly, and annual resolutions. At lower frequencies, the data show the well-known 11-22-year cycle. Both the periodicity and amplitude vary over time. At higher resolutions, the probability distribution has heavy tails, while the data show the intermittent outliers characteristic of multifractal processes. Forecasting experiments are run using regressions in levels and differences, frequency domain methods, models with sinusoidal terms and neural networks. For the daily data, all the models achieve high degrees of accuracy at proximate horizons. As the horizon extends, accuracy falls away rapidly. At 27 days, corresponding to one solar rotation, a transfer function in differences achieves a more accurate forecast than either regressions or neural nets, since it is able to replicate the range of the data. At the annual resolution, both the regression and neural net predict well at horizons of 1 year. Again, forecast accuracy deteriorates sharply as the forecast horizon extends. At the monthly resolution, forecasting is problematic. The resolution is not low enough to bring out the low frequency cycles, but there is so much short-term dependence that the data are completely dominated by serial correlation. Any model incorporating proximate lags will generate inertial forecasts. Any model using lower frequency cyclical terms will be unable to pick up on near-term patterns. The forecasting skill of time series models appears limited to short horizons. The recommendation for forecasting over longer intervals is some combination of physics and statistical models.

预测太阳调制潜力:时间序列模型试验
本研究利用时间序列模型分析了太阳调制潜能值的可预测性。最近,我们获得了新的调制势数据集,分辨率包括日、月和年。在较低频率下,数据显示出众所周知的 11-22 年周期。周期和振幅都随时间变化。分辨率越高,概率分布的尾部越大,而数据则显示出多分形过程所特有的间歇性异常值。使用水平和差分回归、频域方法、正弦项模型和神经网络进行了预测试验。对于每日数据,所有模型都能在较近的范围内达到较高的准确度。随着水平线的延长,精确度迅速下降。在 27 天(相当于太阳公转一周)时,差分传递函数比回归和神经网络都能实现更准确的预测,因为它能够复制数据的范围。在年度分辨率下,回归和神经网络在 1 年的范围内都能很好地预测。同样,随着预测范围的扩大,预测精度也会急剧下降。在月分辨率下,预测存在问题。分辨率不够低,不足以显示低频周期,但短期依赖性太强,数据完全被序列相关性所支配。任何包含近似滞后项的模型都会产生惯性预测。任何使用低频周期项的模型都无法捕捉近期模式。时间序列模型的预测技能似乎仅限于短期。对于较长周期的预测,建议采用物理和统计模型相结合的方法。
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来源期刊
Journal of Atmospheric and Solar-Terrestrial Physics
Journal of Atmospheric and Solar-Terrestrial Physics 地学-地球化学与地球物理
CiteScore
4.10
自引率
5.30%
发文量
95
审稿时长
6 months
期刊介绍: The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is an international journal concerned with the inter-disciplinary science of the Earth''s atmospheric and space environment, especially the highly varied and highly variable physical phenomena that occur in this natural laboratory and the processes that couple them. The journal covers the physical processes operating in the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, magnetosphere, the Sun, interplanetary medium, and heliosphere. Phenomena occurring in other "spheres", solar influences on climate, and supporting laboratory measurements are also considered. The journal deals especially with the coupling between the different regions. Solar flares, coronal mass ejections, and other energetic events on the Sun create interesting and important perturbations in the near-Earth space environment. The physics of such "space weather" is central to the Journal of Atmospheric and Solar-Terrestrial Physics and the journal welcomes papers that lead in the direction of a predictive understanding of the coupled system. Regarding the upper atmosphere, the subjects of aeronomy, geomagnetism and geoelectricity, auroral phenomena, radio wave propagation, and plasma instabilities, are examples within the broad field of solar-terrestrial physics which emphasise the energy exchange between the solar wind, the magnetospheric and ionospheric plasmas, and the neutral gas. In the lower atmosphere, topics covered range from mesoscale to global scale dynamics, to atmospheric electricity, lightning and its effects, and to anthropogenic changes.
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