地磁活动对气候要素影响的研究进展

地磁活动是太阳爆发现象引起地球近地空间磁场扰动的重要空间天气过程之一.地球磁场的变化具有多种时间尺度,其中从数十年到数世纪的长时间地磁场变化主要是由地核磁场引起的,而从数秒到数年的短时间地磁变化与太阳活动有关.近年来,越来越多的统计研究表明,地磁活动与太阳活动和地球气候变化之间存在着显著的相关性.地球磁场和地球大气系统的耦合现象驱动着人们探索地磁活动对地球天气和气候系统影响的研究.本文的目的就是综述国内外地磁变化对气候影响的研究进展,介绍我们最新的研究成果,探索地磁活动对气候要素的影响特征和可能机理过程,为深入研究地磁活动对地球天气和气候的影响提供基础和依据,以期对地磁活动和气候要素关系有进一步的认识.     

利用地磁脉动预报地球同步轨道相对论电子通量的方法研究

本文利用低纬地磁台站的Pi1、Pi2地磁脉动（Pi1-2）资料和地球同步轨道的Pc5地磁脉动资料,对2004年1月到2006年12月38个磁暴事件的地磁脉动参数进行了统计分析.在此基础上,考虑相对论电子的局部加速机制,并加入损失机制,建立了一个初步的磁暴期间地球同步轨道相对论电子通量对数值的预报模型.利用该模型,我们对上述38个磁暴事件进行预报试验,最优化结果是：相对论电子通量对数值的预测值和观测值之间的线性相关系数为0.82,预报效率为0.67.这说明该模式具有较好的预报效果,也表明利用地磁脉动参数进行相对论电子通量预报是可行的.     

A note on the statistics of the largest geomagnetic storms per solar cycle

This note points out a problem with the way in which extreme value distributions have been fit to the intensities of the largest geomagnetic storms per solar cycle. An alternative method is described. This method is applied to observations of the three largest geomagnetic storms in solar cycles 11–22.     

Ionospheric index of solar activity based on the data of measurements of the spacecraft signals characteristics

The method for estimating an ionospheric index of solar-activity (IISA) based on the processing of spacecraft radio signals is suggested. The IISA values have been obtained by comparison between the measured and calculated variations of radio-signal characteristics. To calculate the variations of radio-signal characteristics, the straight rays approximation and the solar-activity index (Wolf numbers W and/or values of F_10.7 solar flux) as a control parameter of the ionospheric model have been used. The suggested method was tested using spacecraft radio signals from the radio-navigation system “CIKADA”. The reduced differences of phases (ΔΦ) for frequencies 150 and 400 MHz were measured and the same characteristics were calculated by integration along the ray of radio-wave propagation between the receiver and the satellite. The IRI-95 has been used as a background ionospheric model. The satellite co-ordinates were determined using the orbit parameters recorded in the navigation messages. Minimization of the difference measured and calculated ΔΦ using arbitrary time steps, or during whole time intervals of observation, gives the IISA corresponding the satellite pass. Daily IISA values were obtained by averaging over all communication contacts during a day (20–30 passes). Testing this approach based on the measurements during March/April 1997, 1998, shows that on magnetically quiet days differences between IISA and the primary solar activity indices are about 5%.     

不同太阳活动及地磁条件下的电导率分布变化

电离层电导率在不同的太阳活动和地磁条件下会发生变化. 本文通过中性大气经验模式NRLMSISE_00（Neutral Atmosphere Empirical Model_2000,简称NRLMSISE_00）和电离层经验模式IRI_2001（International Reference Ionosphere_2001,简称IRI_2001）计算电离层的电子、离子碰撞频率以及电导率,并简要讨论了120 km和300 km高度上的电导率在不同季节、不同太阳活动和地磁指数下的经纬分布. 结果显示,电导率的分布与日照密切相关,且随太阳活动的变化而变化. 磁暴时电导率随地磁活动的变化相对于随太阳活动的变化要小,在120?km高度,磁暴期间电导率在低纬地区和高纬地区发生不同变化,且Pedersen电导率和Hall电导率变化趋势相反,向两极靠近,电导率变化幅度略有增长;在300?km高度上,磁暴对低纬地区和高纬地区电导率的影响要比120?km处大,Pedersen电导率和Hall电导率变化趋势相同,且越向两极靠近电导率的变化幅度越大.     

太阳绕太阳系质心运动22年周期及其与太阳活动的联系

对于太阳活动22年周期的成因机制长期存在着争论.本文借助于行星会合指数以及开普勒第三定律,对太阳绕太阳系质心运动周期进行了分析计算.结果发现,太阳绕太阳系质心运动存在22.1826年显著周期,这与太阳磁场变化的22.20年周期相吻合.并从太阳系角动量守恒的角度解释了两者之间的成因联系:在太阳绕太阳系质心运动的准22年周期中,太阳系质心与太阳质心逐步接近而后逐步分离.当两个质心之间的距离接近零的时候,太阳轨道角动量与自转角动量叠加,会导致太阳自转角速度的加快;当两个质心之间的距离逐渐远离的时候,则导致太阳自转角速度的减慢.这可能是引发太阳活动和太阳磁场变化的原因.这一新认识为太阳活动准22年周期成因机制的解释提供了新的线索和依据.     

基于中国地磁台网数据的太阳静日期间地磁场Z分量日变化幅度分析

本文应用中国地磁台网完成数字化改造后自2008年以来的地磁场观测数据,分析了过去12年(2008—2019年)中国地区地磁场Z分量的日变化幅度特征.重点研究了地磁场Z分量在太阳活动高低年的月变化、季节变化情况以及对其在云南地区和中国东南部地区同纬度台站之间进行了对比分析.研究表明,Z分量日变化幅度在同一月份或季节随太阳活动的变化主要来自电离层电导率的影响;但在太阳活动高年,电离层中性风的作用可能会使分点月的Z分量日变化幅度大于或等于夏至月;由于云南地区地下电性结构的复杂性,地下电导率可能在分点月和夏至月对云南地区的Z分量日变化幅度起到了放大作用,使其增加了约12%~41%.研究结果为地磁场日变化及地磁异常提取的背景信息选择提供了新的参考.

     

基于广义回归神经网络的行星际/太阳风参数和地磁指数的紫外极光强度建模

极光卵极光强度的空间分布是太阳风-磁层-电离层能量耦合过程的重要表现,并且随着空间环境参数和地磁指数的变化而变化,是空间天气的重要指示器.建立合适的极光强度模型对亚暴的预测以及磁层动力学的研究具有重要意义.本文基于Polar卫星的紫外极光成像仪（Ultraviolet Imager,UVI）数据,采用两种不同的极光强度表征方法,即曲线拟合方法（从UVI图像数据中提取极光强度沿磁余纬方向上的曲线特征,Curve Feature along the Magnetic Co-latitude Direction of the Auroral Intensity,CFMCD_AI）和网格化方法（从UVI图像数据中提取极光强度的网格化特征,Gridding Feature of the Auroral Intensity,GF_AI）,来构造极区极光强度特征数据库.然后,利用该数据库,采用广义回归神经网络（Generalized Regression Neural Network,GRNN）构建了以行星际/太阳风参数（行星际磁场三分量、太阳风速度和密度）和地磁指数（AE指数）为输入参数的两种极光强度预测模型（GRNN_CFMCD_AI模型和GRNN_GF_AI模型）.利用图像质量评价指数结构相似度（structure similarity,SSIM）作为极光强度模型预测结果和对应的UVI图像的相似性评价标准（完全相似为1,不相似为0,一般认为SSIM大于0.5是具有较好的相似性）,对两种极光强度模型进行了性能评价.结果显示,GRNN_GF_AI模型预测结果对应的SSIM值范围为0.36~0.77,均值为0.54,性能优于GRNN_CFMCD_AI模型的.

     

Observations of Antarctic precipitable water vapor and its response to the solar activity based on GPS sensing

Predicting global climate change is a great challenge and must be based on a thorough understanding of how the climate system components behave. Precipitable water vapor (PWV) is one of the key components in determining and predicting the global climate system. It is well known that the local surface temperature and pressure have a direct influence on the production of PWV. However, the influence of solar activity on atmospheric dynamics and their physical mechanisms is still an open debate, where past studies are focused at mid-latitude regions. A new method of determining and quantifying the solar influence on PWV based on GPS observations to correlate the GPS PWV and total electron content (TEC) variations is proposed. Observed data from Scott Base (SBA) and McMurdo (MCM) stations from 2003 to 2005 have been used to study the response of PWV to solar activity. In the analysis, the effects of local conditions (wind speed and relative humidity) on the distribution of PWV are investigated. Results show significant correlation between PWV and solar activity for four geomagnetic storms, with correlation coefficients of 0.74, 0.77, 0.64 and 0.69, which are all significant at the 95% confidence level. There was no significant correlation between TEC and PWV changes during the absence of storms. On a monthly analysis, a strong relationship exists between PWV and TEC during storm-affected days, with correlation coefficients of 0.83 and 0.89 (99% confidence level) for SBA and MCM respectively. These indicate a statistically significant seasonal signal in the Antarctic region, which is very active (higher) during the summer and inactive (lower) for the winter periods.