第23太阳活动周热层大气密度对太阳辐射指数F10.7响应的模拟研究

利用NCAR-TIEGCM计算了第23太阳活动周期间（1996—2008年）400km高度上的大气密度，并统计分析大气密度对太阳辐射指数FF_10.7的响应.结果表明，在第23太阳活动周内，大气密度的变化趋势与太阳辐射指数FF_10.7的变化趋势基本一致，但是大气密度在不同年份、不同月份对太阳辐射指数FF_10.7的响应存在差异.第23太阳活动周内太阳辐射极大值和极小值之比大于4，而大气密度的极大值与极小值之比则大于10.太阳辐射低年的年内大气密度变化不到2倍，而太阳辐射高年的年内大气密度变化可达2倍甚至3倍.大气密度与FF_10.7指数在北半球高纬的相关系数比南半球高纬的相关系数大.在低纬地区，太阳辐射高年大气密度与FF_10.7指数的相关系数比低年的大.不同纬度上，大气密度与太阳辐射指数FF_10.7的27天变化值之间的相关系数都大于其与81天变化值之间的相关系数.      

基于卫星加速度数据反演的热层大气密度与NRLMSISE-00模式结果的比较研究

利用CHAMP/STAR加速度数据反演的热层大气密度与NRLMSISE-00模式反演的热层大气密度进行比较, 结果表明, 热层大气密度在春秋季期间高于冬夏季, 并且太阳活动高年比低年更加显著; 日照面和阴影区大气密度的比值在低纬地区由太阳活动高年的4下降到太阳活动低年的2左右, 中纬地区大约由3变化到1.5, 高纬地区变化较小; NRLMSISE-00模式能够较好地模拟热层大气密度的变化趋势, 但是磁暴期间模式精度较差. 统计结果表明, 模式整体比反演结果偏高, 2002-2008年相对偏差分别为16.512%, 20.004%, 18.915%, 18.245%, 25.161%, 33.261%和41.980%; NRLMSISE-00模式在高纬地区的相对偏差为27.337%, 高于中低纬地区的24.047%; 模式在中等太阳活动水平相对偏差较为稳定, 基本在15%左右.      

广州地磁Z分量日变幅的谱特征

利用1972—1993年广州地磁资料，分析了Z分量日变幅的年平均、年变化和半年变化等低频成分的逐年变化，以及小于60天的短周期变化特征．同时对1972—1993年的F10.7日均值进行了谱分析．结果指出，广州地磁Z分量日变幅的年平均与太阳活动指数F10．7的年平均存在良好的线性相关；具有幅度大约5nT夏季极大的年变化，与太阳活动没有明显相关，是一种季节效应；存在春秋分极大的半年变化，幅度与太阳活动有关，高年的幅度明显大于低年；具有明显的与太阳自转相关的27天左右的变化和明显的与行星波有关的接近16日、10日、5日、2日等短周期变化．广州地磁Z分量日变幅的这些谱特征，有助于更深入地了解中低层大气对电离层影响的物理机制．     

Time series modeling and analysis of trends of daily averaged ionospheric total electron content

A 10.7 cm solar radio flux F10.7, geomagnetic planetary equivalent amplitude (Ap index), and period variations were considered in this paper to construct a linear model for daily averaged ionospheric total electron content (TEC). The correlation coefficient of the modeled results and International GNSS Service (IGS) observables was approximately 0.97, which implied that the model could accurately reflect the realistic variation characteristics of the daily averaged TEC. The influences of the different factors on TEC and its characteristics at different latitudes were examined with this model. Results show that solar activity, annual and semiannual cycles are the three most important factors that affect daily averaged TEC. Solar activity is the primary determinant of TEC during periods with high solar activity, whereas periodic factors primarily contribute to TEC during periods with minimum solar activity. The extent of the influences of the different factors on TEC exhibits obvious differences at varying latitudes. The magnitude of the semiannual variation becomes less significant with the increase in latitude. Furthermore, a geomagnetic storm causes an increase in TEC at low latitudes and a decrease at high latitudes.     

基于CHAMP卫星观测数据对热层大气密度的经验正交分析

对2003年(太阳活动较高年)至2007年(太阳活动低年) CHAMP卫星的热层大气密度观测数据进行了经验正交函数(EOF)分析, 得到了400 km高度上白天平均大气密度ρ的太阳活动周变化与年度变化等不同变化分量. 研究结果表明, ρ受太阳活动影响较大, 其太阳周变化分量与F10.7指数变化之间的相关系数可高达94.5 %; ρ的太阳周变化分量随纬度增加而减小, 且在中高纬地区, 南半球的值明显大于北半球的值, 在低纬地区则出现基本对称的双峰分布, 即赤道质量密度异常(EMA)结构. 在ρ的年变化中, 呈现出明显的季节变化, 即夏季低冬季高; 同时ρ的年变化幅度随太阳活动增加而增强, 随纬度增加而增强. 将本文结果与经验模式NRLMSISE00在观测条件下的输出数据进行对比, 发现两者的太阳周变化与年变化分量基本一致, 但本文观测数据的太阳周成分随纬度变化略小, 年变化幅度略大, 且NRLMSISE00模式不能再现EMA结构. 研究结果对揭示热层气候学变化特征具有重要意义.      

Comparison of the observed and modeled low- to mid-latitude thermosphere response to magnetic activity: Effects of solar cycle and disturbance time delay

The performance of JB2008 and NRLMSISE-00 models, in describing the response of the thermosphere to magnetic activity are evaluated against total mass density retrieved from accelerometer measurements made onboard CHAMP satellite during 5 years. We show that the global low- to mid-latitude disturbance amplitude is correctly described by the JB2008 model for low solar activity conditions and by both the JB2008 and the NRLMSISE-00 models for high solar activity conditions. For low solar activity conditions, statistics based on almost 3 years of data confirm the large underestimation by the NRLMSISE-00 model quantified by Lathuillère et al. (2008) for the year 2004. We also found that the time delay between low- to mid-latitude global thermosphere disturbance and magnetic activity is statistically well estimated by the NRLMSISE-00 and JB2008 models for disturbed conditions. For moderately disturbed conditions however, the time delay estimated by the JB2008 model is too large by about 3 h. For very disturbed conditions, we found different time delays during day-time and night-time, using new geomagnetic proxies with a 30-min time resolution.     

不同强度磁暴期间热层大气密度响应特征分析

利用CHAMP卫星数据，对2002-2008年12个不同强度磁暴事件期间的热层大气密度变化特征进行分析，并研究对应磁暴期间大气模式NRLMSISE-00分布特征.结果表明，大磁暴期间日侧大气密度峰值从高纬到低纬的时间延迟为2h，中小磁暴期间的延迟时间为3～4h；春秋季暴时大气密度分布基本呈南北对称分布，而夏冬季大气密度的分布是夏半球大于冬半球，春秋季暴时大气密度大于夏冬季；NRLMSISE-00大气模式得到的热层大气密度很好的体现了半球分布以及季节分布的特征，但模式模拟结果偏小；Dst指数峰值比ap指数峰值更能反应大气密度的变化情况.      

低纬地磁Z分量的半年变化

利用武汉、广州、泉州和琼中等4个低纬地磁站连续多年的地磁资料，计算了各月5个磁静日Z分量日均值与中午1100---1300时段平均值之差(Dz)，对每年12个Dz采用多元回归分析方法，得到各年的半年变化幅度和相位．结果表明：4个站的Dz每年都有半年变化现象；半年变化幅度与太阳活动有关，一般来说，太阳活动高年Dz半年变化幅度明显大于太阳活动低年；太阳活动本身的半年变化，对Dz半年变化幅度有显著的调制作用；Dz半年变化的相位在3—4月(或9—10月)，即极大值出现在分季；低纬地区地磁Z分量存在显著的半年变化，能够反映赤道电急流也有明显的半年变化，这再一次证明，赤道电急流幅度的半年变化，通过“喷泉效应”使得电离层，f0F2产生半年变化，其是产生，f0F2半年变化的一个主要因素．     

太阳活动与热层大气密度的相关性研究

为分析太阳活动对热层大气的影响,使用250km,400km,550km高度处热层大气密度与太阳F_10.7指数数据,研究了二者的周期变化及相关关系. 结果表明,热层大气密度的变化与太阳活动呈现相似的变化趋势;两者均具有显著的27天及11年周期变化特征,热层大气密度还存在7～11天及0.5年和1年的变化特征,且高度越高越明显;热层大气密度对太阳活动的最佳响应滞后为3天,无论何种地磁活动水平下,400km高度处相关性高于250km,550km处相关性最小,且太阳活动下降相期间高于上升相;250km,400km和550km高度处热层大气密度和太阳活动的统计结果分别为饱和、线性和放大关系;高度越高的热层大气密度对太阳活动响应越敏感.      

NRLMSISE-00大气模型与GRACE和CHAMP卫星大气密度数据的对比分析

利用GRACE(Gravity Recovery And Climate Experiment)和CHAMP(Challenging Mini-Satellite Payload)卫星2002-2008年的大气密度数据与NRLMSISE-00大气模型密度结果进行比较,分析了模型密度误差及其特点.结果显示,NRLMSISE-00大气模型计算的密度值普遍偏大,其相对误差随经纬度变化,在高纬度相对较小;相对误差随地方时变化,在02:00LT和15:00LT左右较大,10:00LT和20:00LT左右较小.通过模型密度相对误差与太阳F_10.7指数的对比分析发现,在太阳活动低年模型相对误差最大,而在太阳活动高年相对误差较小;将模型结果分别与GRACEA/B双星和CHAMP卫星的密度数据进行比较,发现对于轨道高度更高的GRACE卫星轨道,模型相对误差更大;在地磁平静期,相对误差与地磁ap指数(当前3h)相关性不强,但是在大磁暴发生时,误差急剧增大.      

SZ-5 Cabin's Height Changes during Three Super-storms in 2003

In this work, the daily height variations of SZ-5 (Shenzhou-5) cabin from 22 October to 28 November in 2003 are analyzed, which includes the period of the Halloween Storm and the Great November Storm. The significant orbital decays have been observed at the end of October and in late November due to the great solar flares and the severe geomagnetic storms. According to the equation of the air-drag-force on a spacecraft and the SZ-5 orbital decay information, the relative daily average thermospheric density changes during the three 2003 super-storms are derived and the results are compared with the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar Extended Model (NRLMSISE-00). The results show that the daily average thermospheric density (at the altitude of SZ-5, about 350 km) in storm time enhances to approximately 200% as much as that in the quiet time but the empirical model may somewhat underestimate the average thermospheric density changes and the daily contributions of geomagnetic storms to the density enhancements during these severe space weather events.      

Thermosphere densities derived from Swarm GPS observations

After the detection of many anomalies in the Swarm accelerometer data, an alternative method has been developed to determine thermospheric densities for the three-satellite mission. Using a precise orbit determination approach, non-gravitational and aerodynamic-only accelerations are estimated from the high-quality Swarm GPS data. The GPS-derived non-gravitational accelerations serve as a baseline for the correction of the Swarm-C along-track accelerometer data. The aerodynamic accelerations are converted directly into thermospheric densities for all Swarm satellites, albeit at a much lower temporal resolution than the accelerometers would have been able to deliver. The resulting density and acceleration data sets are part of the European Space Agency Level 2 Swarm products.To improve the Swarm densities, two modifications have recently been added to our original processing scheme. They consist of a more refined handling of radiation pressure accelerations and the use of a high-fidelity satellite geometry and improved aerodynamic model. These modifications lead to a better agreement between estimated Swarm densities and NRLMSISE-00 model densities. The GPS-derived Swarm densities show variations due to solar and geomagnetic activity, as well as seasonal, latitudinal and diurnal variations. For low solar activity, however, the aerodynamic signal experienced by the Swarm satellites is very small, and therefore it is more difficult to accurately resolve latitudinal density variability using GPS data, especially for the higher-flying Swarm-B satellite. Therefore, mean orbit densities are also included in the Swarm density product.     

Recent advances in upper atmospheric structure

A possible quantitative explanation of the semi-annual variation in thermospheric density has been obtained in terms of a semi-annual variation in the computed globally averaged vertical energy carried by propagating tides from the lower and middle atmosphere into the thermosphere. The effect is primarily due to seasonal changes in the distribution of water vapor and in the solar declination angle and Sun-Earth distance. An MSIS-83 empirical model of the thermosphere, representing a revision of the earlier MSIS models, has been prepared. The database used covers a wider range of solar activity than previous models and an improved magnetic storm representation is included. Atomic oxygen profiles in the 100 to 160 km altitude region of the auroral thermosphere have been recalculated from measured quenching of N₂(A³∑_u⁺) using the latest laboratory rates and the results are in good agreement with the mean CIRA 1972 profile. A new empirical model of thermospheric variations with geomagnetic activity has been developed incorporating variations with local magnetic time, latitude dependent terms which can vary with the magnitude of the geomagnetic disturbance, and an altitude dependent expression for the equatorial wave. A new index ML, derived from the AL index, has been developed that appears to have promise to represent the variations of thermospheric species with geomagnetic activity. Satellite measured values of solar UV flux, ground-based observations of CaK plages, sunspot numbers and 10.7 cm solar radio flux have been analyzed for temporal variations. Some differences have been identified and the significance to empirical and theoretical upper atmosphere models is discussed.     

Variability study of ionospheric total electron content at crest of equatorial anomaly in China from 1997 to 2007

The variation of TEC data at Wuhan station (geographic coordinate: 30.5°N, 114.4°E; geomagnetic coordinate: 19.2°N, 183.8°E) at crest of equatorial anomaly in China from January 1997 to December 2007 were analyzed. Variability with solar activity, annual, semiannual, diurnal and seasonal variation were also analyzed. The MSIS00 model and ISR model were used to analyze the possible mechanisms of the variabilities found in the results. The TEC data in 1997 and 2001 deduced from another crest station Xiamen (geographic coordinate: 24.4°N, 118.1°E; geomagnetic coordinate: 13.2°N, 187.4°E) were used to contrast. Analysis results show that long-term variations of TEC at Xiamen station are mainly controlled by the variations of solar activities. Typical diurnal variation behaves as a minimum of the TEC in the pre-dawn hours around 05:00–06:00LT and a maximum on the afternoon hours around 13:00–15:00LT. Some features like the semiannual anomaly and winter anomaly in TEC have been reported. The anomaly may be the result of common action of the electric field over the magnetic equatorial and the [O/N₂] at the crest station.     

Thermospheric winds over Abuja during solar minimum period

Monthly variations of averaged nighttime thermospheric winds have been investigated over Abuja, Nigeria (Geographic: 9.06°N, 7.5°E; Geomagnetic: 1.60°S). The reports are based on Fabry-Perot interferometer measurements of Doppler shifts and Doppler broadening of the 630.0 nm spectral emission. The results were obtained during a period of weak solar activity with the solar flux (F10.7) typically below 70 solar flux units. Inspection of the average monthly thermospheric winds from October 2017 to December 2017 found December meridional winds to be more equatorward than the October and November winds. Zonal winds are eastward with pre-midnight maximum speeds going above 100 m/s. Compared to Jicamarca zonal winds in the Peruvian sector for the same month of October, the magnitude of maximum Abuja zonal wind speed is weaker. We compare the observed diurnal variation with the recently updated Horizontal wind model (HWM 14). Most of the observational features of thermospheric wind diurnal variation are captured in the model variation. The HWM14 generally showed good agreement with the Abuja October and November zonal wind observations but overestimates the December meridional winds. Expected longer period analysis of the results from Abuja will stimulate a better understanding of wind climatology over the West African sector.     

低轨航天器弹道系数估算及热层大气模型误差分析

利用低轨（LEO）航天器在轨期间两行轨道根数（TLEs）数据,结合经验大气密度模型NRLMSISE00,反演计算得到其在轨期间的弹道系数B’,以31年B’的平均值代替弹道系数真值,分别通过标准球形目标卫星对比以及物理参数基本相同的非球形目标卫星对比,对弹道系数真值进行了检验;利用不同外形目标卫星弹道系数在不同太阳活动周内的变化规律,结合太阳和地磁活动变化,估计经验大气密度模型的误差分布. 结果表明,利用反演弹道系数31年的平均值来代替真值,其在理论值的正常误差范围内;大气密度模型误差在210～526km高度范围内存在相同的变化趋势,且模型误差随高度增加而增大;在短周期内B’变化与太阳活动指数F_10.7存在反相关性;密度模型不能有效模拟2008年出现的大气密度异常低. 以上结果表明,经验大气密度模型结果需要修正,尤其是在太阳活动峰年和谷年,此外,磁暴期间模型误差的修正对卫星定轨和轨道预报等也具有重要意义.      

Climatology of global,hemispheric and regional electron content variations during the solar cycles 23 and 24

We present the results of study on the variations of ionospheric total electron content (TEC) by using global, hemispheric, and regional electron contents computed from the global ionospheric maps (GIMs) for the period from 1999 to 2020. For a low and moderate solar activity, the global and regional electron contents vary linearly with solar 10.7 cm radio flux and EUV flux. While a saturation effect in the electron content verses EUV and F10.7 is found during the high solar activity periods at all regions, the maximum effect is observed at low-latitudes followed by high and mid-latitudes region. The extent of saturation effect is more pronounced for F10.7 as compared to EUV. A wavelet transform is applied to global and hemispheric electron contents to examine the relative strength of different variations. The semi-annual variations dominate in the northern hemisphere, whereas annual variations dominate in the southern counterpart. The amplitude of annual variations in southern hemisphere is found to be higher than northern counterpart at all latitudes. This asymmetry in the amplitude of annual variation is maximum at low-latitudes, followed by mid and high-latitudes, respectively. The semi-annual variations are in-phase in both hemisphere and follow the solar cycle. The northern hemisphere depicts relatively large amplitude of semi-annual variations and exhibit the maximum effect at high-latitudes.     

Solar,geomagnetic and long term effects on thermospheric neutral kinetic temperatures at midlatitude

Measurements of midlatitude thermospheric neutral kinetic temperatures obtained from 1972 to 1979 have been used to investigate the effects of solar and geomagnetic activity, as well as long term effects, on the thermosphere. With these data a simple power law relationship between the temperature and solar activity (expressed as the 2.8 GHz solar radio flux) has been found to give a high correlation. In addition, a linear relationship between temperature changes and geomagnetic activity (expressed as Ap), as well as annual and semiannual effects have been found. The annual variation is found to be indistinguishable in phase from the annual variation of the solar declination angle. The present four parameter formulation gives a better fit to the data than is obtained with available empirical models of the thermosphere, and this has allowed us to investigate the properties and postulates of some of these models.     

一种NRLMSISE-00模型太阳辐射校正方法

根据空间天气的状态,调整大气模型的相关输入参数能够减小模型的计算误差.通过对比CHAMP卫星在轨大气密度探测数据与NRLMSISE-00模式的计算结果发现,通过调整F_10.7的输入,使轨道大气密度积分的模式计算结果与探测结果之间的误差达到最小,此时的F_10.7被称为理想F_10.7输入（F*）.进一步的分析发现,F*与太阳紫外辐射MgII指数存在很好的相关性,因此可以选择其他的太阳紫外辐射代理参数取代F_10.7,从而减小模型计算误差.本文采用神经网络技术,建立新的太阳紫外辐射代理参量F_euv与MgII,F_10.7等的对应模型,能够根据当日参数值计算F_euv.研究结果表明,新的代理参数能够有效减小NRLMSISE-00的计算误差.      

Interplanetary shocks and sudden impulses during solar maximum (2000) and solar minimum (1995–1996)

In this work a study is performed on the correlation between fast forward interplanetary shock parameters at 1 Astronomical Unit and sudden impulse (SI) amplitudes in the H-component of the geomagnetic field, for periods of solar activity maximum (year 2000) and minimum (year 1995–1996). Solar wind temperature, density and speed, and total magnetic field, were taken to calculate the static pressures (thermal and magnetic) both in the upstream and downstream sides of the shocks. The variations of the solar wind parameters and pressures were then correlated with SI amplitudes. The solar wind speed variations presented good correlations with sudden impulses, with correlation coefficients larger than 0.70 both in solar maximum and solar minimum, whereas the solar wind density presented very low correlation. The parameter better correlated with SI was the square root dynamic pressure variation, showing a larger correlation during solar maximum (r = 0.82) than during solar minimum (r = 0.77). The correlations of SI with square root thermal and magnetic pressure were smaller than with the dynamic pressure, but they also present a good correlation, with r > 0.70 during both solar maximum and minimum. Multiple linear correlation analysis of SI in terms of the three pressure terms have shown that 78% and 85% of the variance in SI during solar maximum and minimum, respectively, are explained by the three pressure variations. Average sudden impulse amplitude was 25 nT during solar maximum and 21 nT during solar minimum, while average square root dynamic pressure variation is 1.20 and 0.86 nPa^1/2 during solar maximum and minimum, respectively. Thus on average, fast forward interplanetary shocks are 33% stronger during solar maximum than during solar minimum, and the magnetospheric SI response has amplitude 20% higher during solar maximum than during solar minimum. A comparison with theoretical predictions (Tsyganenko’s model corrected by Earth’s induced currents) of the coefficient of sudden impulse change with solar wind dynamic pressure variation showed excellent agreement, with values around 17 nT/nPa^1/2.