Kp forecast models
Kp forecast models
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DOI：
10.1029/2004ja010500
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发表时间：
2005-04
影响因子：
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通讯作者：
S. Wing;J. Johnson;J. Jen;C. Meng;D. Sibeck;K. Bechtold;J. Freeman;K. Costello;M. Balikhin;Kazue Takahashi
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文献类型：
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作者：
S. Wing;J. Johnson;J. Jen;C. Meng;D. Sibeck;K. Bechtold;J. Freeman;K. Costello;M. Balikhin;Kazue Takahashi
[1] 众所周知，磁活跃时间（例如 Kp > 5）很难预测，而正是在这种预测对空间天气用户至关重要的时候。利用朗格朗日点 (L1) 常规可用的太阳风测量和临近预报的 Kps，开发了基于神经网络的 Kp 预测模型，重点是改进活动时间的预测。为了满足不同的需求和操作限制，开发了三种模型：（1）输入即时Kp和太阳风参数并预测提前1小时的Kp的模型； (2) 与模型1具有相同输入并提前4小时预测Kp的模型； （3）仅输入太阳风参数并提前1小时预测Kp的模型（准确的预测时间取决于太阳风速度和太阳风监测仪的位置）。对这些模型和其他主要操作 Kp 预测模型的广泛评估表明，虽然新模型可以更准确地预测所有活动的 Kp，但最显着的改进发生在中等和活跃时间。 Kp 的信息动力学分析表明，当地球空间更直接地受到外部输入（即太阳风和行星际磁场（IMF））驱动时，太阳极小期附近的内部动力学比太阳极大期附近更受内部动力学的支配。
[1] Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely the times when such predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. To satisfy different needs and operational constraints, three models were developed: (1) a model that inputs nowcast Kp and solar wind parameters and predicts Kp 1 hour ahead; (2) a model with the same input as model 1 and predicts Kp 4 hour ahead; and (3) a model that inputs only solar wind parameters and predicts Kp 1 hour ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor). Extensive evaluations of these models and other major operational Kp forecast models show that while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. Information dynamics analysis of Kp suggests that geospace is more dominated by internal dynamics near solar minimum than near solar maximum, when it is more directly driven by external inputs, namely solar wind and interplanetary magnetic field (IMF).