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.      

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.      

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.      

太阳极紫外辐射对热层大气密度的影响

对2001-2021年SOHO卫星的极紫外辐射测量数据,以及CHAMP,GRACE-A和SWARM-C卫星资料推导出的高分辨率大气密度数据进行统计分析,发现大气密度与极紫外测量值的相关系数大于密度与F10.7指数的相关系数,证实极紫外辐射在不同地方时的影响程度存在显著差异,从而驱动大气密度的周日变化。利用三颗卫星的高度差异揭示极紫外辐射对大气密度的加热效应在350～500 km范围随着高度增加而减弱。统计得到极紫外辐射影响在地方时和纬度上的空间差异：对夏季半球的影响大于冬季半球;在白天,对中纬度地区的影响高于赤道和高纬度地区;在夜间,密度对辐射的斜率在夏季半球高纬度地区存在峰值,在冬季半球中纬度存在谷值,模型DTM2000和NRLMSISE00未能准确刻画。为了改进经验模型,提出基于球谐函数的拟合方法,优于主流模型周日效应采用的表达式,对周日效应建模和修正提供有益借鉴。利用昼夜间能量传输和热层大气经向环流机制探讨了统计结果的物理机制。     

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.      

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.      

Periodic Variations of Drag Coefficient for the ANDE Spherical Satellites During its Lifetime

A drag coefficient (C_D) inversion method is introduced to study the variations of the drag coefficient for orbital satellites with spherical geometry. Drag coefficients of the four micro satellites in the Atmospheric Neutral Density Experiment (ANDE) are compiled out with this new method. The Lomb-Scargle Periodgram (LSP) analysis of the four ANDE satellites' C_D series has shown that there are obvious 5, 7, 9, and 27 days' period in those data. Interesting results are found through comparing the LSP analysis with series of the daily solar radio flux at 10.7 cm (F_10.7 index), the Ap index, and the daily averaged solar wind speed at 1AU. All series in the same time interval have an obvious period of about 27 days, which has already been explained as the association with the 27 days' solar rotation. The oscillating periods less than 27 days are found in series of C_D, Ap and solar wind speed at 1AU, e.g., the 5, 7, 9 days period. However, these short periods disappeared in the time series of F_10.7 index. The same periodicities of 5, 7, 9 days in Ap and solar wind are presented at the same time interval during the declining phase of solar cycle 23. While in the ascending phase of solar cycle 24, these short oscillations are not so obvious as that in the declining phase of solar cycle 23. These results provide definite evidence that the C_D variations with period of 5, 7 and 9 days are produced by a combination of space weather effects caused by the solar wind and geomagnetic activity.      

Use of two-line element data for thermosphere neutral density model calibration

Traditional empirical thermospheric density models are widely used in orbit determination and prediction of low-Earth satellites. Unfortunately, these models often exhibit large density errors of up to around 30% RMS. Density errors translate into orbit errors, adversely affecting applications such as re-entry operations, manoeuvre planning, collision avoidance and precise orbit determination for geodetic missions. The extensive database of two-line element (TLE) orbit data contains a wealth of information on satellite drag, at a sufficiently high spatial and temporal resolution to allow a calibration of existing neutral density models with a latency of one to two days. In our calibration software, new TLE data for selected objects is converted to satellite drag data on a daily basis. The resulting drag data is then used in a daily adjustment of density model calibration parameters, which modify the output of an existing empirical density model with the aim of increasing its accuracy. Two different calibration schemes have been tested using TLE data for about 50 objects during the year 2000. The schemes involve either height-dependent scale factors to the density or corrections to CIRA-72 model temperatures, which affect the density output based on a physical model. Both schemes have been applied with different spherical harmonic expansions of the parameters in latitude and local solar time. Five TLE objects, varying in perigee altitude between 280 and 530 km, were deliberately not used during calibration, in order to provide independent validation. Even with a single daily parameter, the RMS density model error along their tracks can already be reduced from the 30% to the 15% level. Adding additional parameters results in RMS errors lower than 12%.     

太阳E10.7指数的反演及预报方法

针对高层大气密度预报和轨道预报业务中对新型太阳紫外辐射指数E_10.7的需求,基于TIMED-SEE观测仪器提供的0.1～105 nm太阳辐射强度数据,开展了E_10.7指数反演和中期预报研究。 E_10.7指数是太阳光谱中波长为0.1～105 nm的辐射流量,单位与F_10.7指数相同（sfu,1 sfu=10–22 W·m–2·Hz–1）。 TIMED-SEE观测仪器提供的0.1～105 nm太阳辐射强度实测值具有高时间分辨率、延迟时间短和易获得的优势,利用最小二乘法拟合可反演出准实时的E_10.7指数,均方根误差为5.445 sfu。利用高阶自回归模型对E_10.7的中期预报效果尚佳,未来27天的预报值平均相对误差为7.83%。利用同样方法还开展了E_10.7指数81天中心滑动平均值未来27天预报试验,未来27天的预报值平均相对误差仅为3.63%。      

Solar EUV and decimetric indices and thermospheric models

Data bases and limits of applicability of existing empirical thermospheric models are reviewed by using these models together with solar EUV irradiance data in studying the solar activity effect on composition, density and temperature. For two rather short aeronomy missions of the AEROS A and B satellites solar EUV indices as proposed by Schmidtke are used in comparison with the 10.7 cm solar flux F in determining the solar activity effect in in-situ composition measurements sampled by the same satellites at 250, 310 and 380 km altitude. No advantage of solar EUV indices over F could be determined.     

Drag and energy accommodation coefficients during sunspot maximum

Conditions appropriate to gas-surface interactions on satellite surfaces in orbit have not been successfully duplicated in the laboratory. However, measurements by pressure gauges and mass spectrometers in orbit have revealed enough of the basic physical chemistry that realistic theoretical models of the gas-surface interaction can now be used to calculate physical drag coefficients. The dependence of these drag coefficients on conditions in space can be inferred by comparing the physical drag coefficient of a satellite with a drag coefficient fitted to its observed orbital decay. This study takes advantage of recent data on spheres and attitude stabilized satellites to compare physical drag coefficients with the histories of the orbital decay of several satellites during the recent sunspot maximum. The orbital decay was obtained by fitting, in a least squares sense, the semi-major axis decay inferred from the historical two-line elements acquired by the US Space Surveillance Network. All the principal orbital perturbations were included, namely geopotential harmonics up to the 16th degree and order, third body attraction of the Moon and the Sun, direct solar radiation pressure (with eclipses), and aerodynamic drag, using the Jacchia-Bowman 2006 (JB2006) model to describe the atmospheric density. After adjusting for density model bias, a comparison of the fitted drag coefficient with the physical drag coefficient has yielded values for the energy accommodation coefficient as well as for the physical drag coefficient as a function of altitude during solar maximum conditions. The results are consistent with the altitude and solar cycle variation of atomic oxygen, which is known to be adsorbed on satellite surfaces, affecting both the energy accommodation and angular distribution of the reemitted molecules.     

双通道辐射量计算太阳极紫外辐射E10.7指数方法初探

从太阳极紫外辐射研究的重要性出发, 介绍了太阳极紫外辐射E_10.7指 数及其作用, 详细阐述了利用两个能道的太阳辐射观测值计算极紫外辐射E_10.7指数的计算方法. 利用该方法对实测太阳辐射数据进行处理, 计算获得了2000-2005年的每日E_10.7指数, 并将计算结果 与Solar2000模式的输出结果进行对比分析, 验证了该计算方法的可行性, 对比结果表明, 最大相对误差在20%以内, 平均相对误差均在10%以内.      

Measuring atmospheric density using GPS–LEO tracking data

We present a method to estimate the total neutral atmospheric density from precise orbit determination of Low Earth Orbit (LEO) satellites. We derive the total atmospheric density by determining the drag force acting on the LEOs through centimeter-level reduced-dynamic precise orbit determination (POD) using onboard Global Positioning System (GPS) tracking data. The precision of the estimated drag accelerations is assessed using various metrics, including differences between estimated along-track accelerations from consecutive 30-h POD solutions which overlap by 6 h, comparison of the resulting accelerations with accelerometer measurements, and comparison against an existing atmospheric density model, DTM-2000. We apply the method to GPS tracking data from CHAMP, GRACE, SAC-C, Jason-2, TerraSAR-X and COSMIC satellites, spanning 12 years (2001–2012) and covering orbital heights from 400 km to 1300 km. Errors in the estimates, including those introduced by deficiencies in other modeled forces (such as solar radiation pressure and Earth radiation pressure), are evaluated and the signal and noise levels for each satellite are analyzed. The estimated density data from CHAMP, GRACE, SAC-C and TerraSAR-X are identified as having high signal and low noise levels. These data all have high correlations with anominal atmospheric density model and show common features in relative residuals with respect to the nominal model in related parameter space. On the contrary, the estimated density data from COSMIC and Jason-2 show errors larger than the actual signal at corresponding altitudes thus having little practical value for this study. The results demonstrate that this method is applicable to data from a variety of missions and can provide useful total neutral density measurements for atmospheric study up to altitude as high as 715 km, with precision and resolution between those derived from traditional special orbital perturbation analysis and those obtained from onboard accelerometers.     

基于活动区面积统计的F10.7指数预报方法

太阳10.7cm射电流量（F_10.7）是反映太阳整体活动的重要指标,其主要源头是日面活动区.F_10.7指数与日面活动区具有显著的相关性,且不同面积的活动区与F_10.7并不遵循相同的线性关系.为进一步提高F_10.7预报的准确性,利用日面活动区面积与F_10.7的相关性,依据面积大小分类,提出F_10.7的预报公式并进行验证.采用2008-2018年SWPC （Space Weather Prediction Center）公布的活动区面积数据和CSWFC （Canadan Space Weather Forecast Center）公布的F_10.7实测数据计算预报公式系数,利用高年（2003年）和低年（1997年）的F_10.7预报验证其结果.研究结果表明,预报结果与实测值的相关系数分别为0.9318和0.9295,二者皆优于SWPC同时期的预报结果（相关系数分别为0.9186和0.8771）.本研究首次基于活动区的变化预测了F_10.7,提高了F_10.7预测的准确性.      

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.     

大气阻力参数修正对低轨空间目标轨道预报精度的改进

对于低轨空间目标, 大气阻力是影响轨道预报精度的主要摄动力. 本文提出了一种 基于空间环境数据和神经网络模型的空间目标大气阻力参数修正方法, 基于目 标的历史两行元根数, 通过模拟得到外推一天轨道预报中预报结果与观测数据 符合最好的阻力调制系数, 分析表明其与太阳F_10.7指数和地磁Ap指数具有很好的相关性. 根据已有数据, 构建神经网络模型, 实现对阻力调制系数 的补偿计算, 从而改进低轨目标外推一天的轨道预报. 结果表明, 神经网络模 型相比两行元根数能够更及时地对空间环境变化进行响应. 将该方案应用于天 宫一号和国际空间站的外推一天轨道预报, 验证了方案的正确性和普适性, 对 地磁扰动引起的较大预报误差改进效果更好, 误差能够降低50%~60%; 平均而言, 预报精度可以提高约30%, 改进成功率达到80%左右.      

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.      

Thermospheric neutral density response to solar forcing

Recent measurements by the Solar EUV (Extreme Ultra Violet) Experiment (SEE) aboard the Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics satellite (TIMED) provide solar EUV spectral irradiance with adequate spectral and temporal resolution, and thus the opportunity to use solar measurements directly in upper atmospheric general circulation models. Thermospheric neutral density is simulated with the NCAR Thermosphere–Ionosphere–Electrodynamic General Circulation Model (TIEGCM) using TIMED/SEE measurements and using the EUVAC solar proxy model. Neutral density is also calculated using the NRLMSISE-00 empirical model. These modeled densities are then compared to density measurements derived from satellite drag data. It is found that using measured solar irradiance in the general circulation model can improve density calculations compared to using the solar proxy model. It is also found that the general circulation model can improve upon the empirical model in simulating geomagnetic storm effects and the solar cycle variation of neutral density.     

太阳活动突发过程对临近空间大气影响的模拟

空间天气对地球及近地空间具有重要影响,大的空间天气事件对中上层大气动力学和成分具有不同的影响。利用全大气耦合模式WACCM,针对太阳耀斑、太阳质子、地磁暴三类事件,以太阳活动平静期2015年5月10-14日的GEOS-5数据为模式背景场,通过F_10.7、离子产生率、Kp及Ap指数设置,分别模拟三类事件对临近空间大气温度、密度和臭氧的影响。结果表明耀斑事件在三类事件中对临近空间大气温度和密度的影响最为显著。平流层大气温度增加是由耀斑辐射增强引起平流层臭氧吸收紫外辐射发生的光化学反应所致,耀斑事件引起平流层和低热层温度增加约为2～3 K,低热层大气相对密度增加在6%以内;太阳质子事件及磁暴事件主要影响低热层,但太阳质子事件和磁暴事件对低热层温度扰动不大于1 K。     

利用卫星两行轨道根数反演热层密度

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