Understanding Strong Neutral Vertical Winds and Ionospheric Responses to the 2015 St. Patrick's Day Storm Using TIEGCM Driven by Data‐Assimilated Aurora and Electric Fields
Understanding Strong Neutral Vertical Winds and Ionospheric Responses to the 2015 St. Patrick's Day Storm Using TIEGCM Driven by Data‐Assimilated Aurora and Electric Fields
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DOI：
10.1029/2022sw003308
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Xian Lu;Haonan Wu;S. Kaeppler;J. Meriwether;Y. Nishimura;Wenbin Wang;Jintai Li;Xueling Shi
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作者：
Xian Lu;Haonan Wu;S. Kaeppler;J. Meriwether;Y. Nishimura;Wenbin Wang;Jintai Li;Xueling Shi
2015年的圣帕特里克日风暴是太阳活动24周中最强的磁暴之一，引起了人们的极大关注。我们通过同时观测高纬度强迫（极光和电场）和电离层-热层（I-T）响应来重新审视这一事件。强迫项被同化，以使用新采用的格点克里格方法驱动热层电离层电动力学环流模型（TIEGCM）（Wu & Lu，2022，https：//doi.org/10.1029/2021SW002880; Wu et al.，2022，https：//doi.org/10.1029/2022SW003146）。与默认运行相比，具有同化的TIEGCM模拟捕获：（a）由于极光增强而导致的次级E区电子密度峰值;（B）强烈升高的离子温度（高达3000 K）伴随着一个强的向北电场（1.80 mV/m）和伴随的离子摩擦加热;（c）电子温度升高;及（d）中性垂直风显著增强（约每秒50米）。均方根误差降低30%-50%。强烈的中性上升流是由极光和电场增强引起的大焦耳加热引起的。数据同化将扑克坪的高度积分焦耳加热增加到50-100 mW/m2的水平，而在全球范围内，其最大值与默认运行相当：能量沉积的位置由数据指导。在同化运行中，旅行大气扰动显示出更强的量级和更大的延伸，导致垂直风变率增加了1.5-3倍。我们的工作表明，模式驱动程序的数据同化有助于产生真实的风暴时间I-T响应，其显示出比以前模拟的更丰富的动态范围，尺度和可变性。
As one of the strongest geomagnetic storms in Solar Cycle 24, the 2015 St. Patrick's Day storm has attracted significant attention. We revisit this event by taking advantage of simultaneous observations of high‐latitude forcings (aurora and electric fields) and ionosphere‐thermosphere (I‐T) responses. The forcing terms are assimilated to drive the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) using a newly adopted Lattice Kriging method (Wu & Lu, 2022, https://doi.org/10.1029/2021SW002880; Wu et al., 2022, https://doi.org/10.1029/2022SW003146). Compared to the default run, the TIEGCM simulation with assimilation captures: (a) secondary E‐region electron density peak due to aurora intensification; (b) strongly elevated ion temperatures (up to ∼3000 K) accompanied by a strong northward electric field (∼80 mV/m) and associated ion frictional heating; (c) elevation of electron temperatures; and (d) substantially enhanced neutral vertical winds (order of 50 m/s). Root‐mean‐square errors decrease by 30%–50%. The strong neutral upwelling is caused by large Joule heating down to ∼120 km resulting from enhanced aurora and electric field. Data assimilation increases the height‐integrated Joule heating at Poker Flat to a level of 50–100 mW/m2 while globally, its maximum value is comparable with the default run: the location of energy deposition becomes guided by data. Traveling atmospheric disturbances in the assimilation run show stronger magnitudes and larger extension leading to an increase of vertical wind variability by a factor of ∼1.5–3. Our work demonstrates that data assimilation of model drivers helps produce realistic storm‐time I‐T responses, which show richer dynamic range, scales, and variability than what has been simulated before.