CHAMP和GRACE-A/B反演大气密度数据评估分析

利用Colorado大学公开发布的2001-2008年CHAMP和GRACE-A/B三颗卫星加速度计反演的400km高度上的大气密度数据,以大气模式NLRMSISE-00为参考,分析反演数据与模式值的误差特点、产生误差的原因、密度的变化及合理性,并通过卫星轨道两行根数（TLE）的反演结果进行验证,主要结论如下.CHAMP密度值整体稍高于GRACE-A/B,CHAMP密度与模式值之间的误差整体小于GRACE-A/B,2007-2008年 GRACE-A/B与模式的相对误差变化起伏较大.2001年CHAMP与模式存在整体偏差,通过相似空间环境条件下的密度变化比对以及利用TLE的反演结果验证,确定2001年的CHAMP反演密度整体偏低.CHAMP及GRACE-A/B密度变化个例显示,卫星密度值会出现一些个性化特征,使用时应根据需求进行分析处理.研究结果可为合理应用该数据提供参考.      

典型热层密度模式误差分析

以CHAMP卫星2001年5月15日至2008年12月31日期间2755天的加速度计反演热层大气密度数据为基准,对JB2008和MSISE00两种模式的反演误差进行了统计分析.发现这两种模式整体上均高估了热层大气密度,但JB2008模式的精度优于MSISE00模式.JB2008和MSISE00模式的平均相对误差分别为2.2%和17.6%.对空间环境简要分类,统计各类型事件下热层实测和模式密度的纬度和地方时特性,发现MSISE00模式具有较好的地方时特性,而JB2008模式具有较好的纬度特性.研究结果对掌握目前热层密度模式误差特性及指导模式改进方向具有一定意义.      

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.      

基于电子密度同化的热层大气密度预报方法

采用热层电离层耦合模式TIEGCM和集合卡尔曼滤波同化方法,利用同化COSMIC电离层掩星电子密度数据优化热层电离层参量,并将模式预报的大气密度与CHAMP卫星大气密度数据进行对比,分别开展模拟和实测数据的同化预报实验.在模拟数据同化实验中,状态向量包含温度、风场和离子成分的实验结果表明,仅优化温度即可达到最优的热层大气密度预报效果.在实测数据同化实验中,将温度作为状态向量参数,优化结果表明,循环同化过程中模式预报的大气密度相对偏差的均方根误差在48h内从38%减小到27%,同化稳定时间至少需要30h.预报过程中大气密度预报效果的改善持续时间为34h.这表明电子密度同化能够改善热层大气密度的预报精度,设计的实验方案合理可行,可获得较长的预报时效.      

基于轨道预报的热层模式评估

在太阳活动高低年的地磁平静/扰动环境下,利用不同热层大气模式J77,DTM78,MSIS00,JB2008和CHAMP加速度计反演密度,分析有无先验信息条件下的轨道预报误差.结果表明无先验信息的精密轨道预报中,热层模式的性能可能被弹道系数等参数偏差干扰,此时预报误差不能作为模式性能的评价标准.先验信息对轨道预报精度提升非常明显,尤其是地磁扰动期先进热层模式性能得以展现,轨道预报误差为无先验信息情况下的10%～25%.目前热层模式的主要缺陷存在于地磁扰动期.各模式之间的差异是:JB2008模式可以通过线性和单一频率周期项补偿,而J77及DTM78等模式还存在更多频率的误差.本文对不同情况下精密轨道预报的研究结果可为空间碎片碰撞预警等工程实践提供参考.      

一种基于温度参数的热层密度修正方法

热层大气的阻力效应是影响低轨航天器大量空间操作的重要因素, 尤其是经验密度模式, 其固有的至少15%的内符合误差已严重制约航天器轨道计算精度的提高. 针对广泛应用的经验密度模式, 选择物理背景简明、关联参数较少的JACCHIA71模式, 以地磁平静条件下的全球散逸层顶温度最小值T_c及125 km高度拐点温度T_x为对象, 建立密度相对于上述温度参数的条件方程, 推导密度相对于温度参数的解析偏导数, 并给出其最小二乘解. 同时, 利用CHAMP卫星数据对模式进行修正, 模式平均误差从40%降低至3%左右. 通过TG01飞行器的轨道预报比较, 修正前后轨道预报位置精度从2 km提升至1 km左右. 经过CHAMP卫星和TG01飞行器的实测数据检验, 验证了修正算法的正确性和有效性.      

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.      

大气密度模型球谐修正研究

大气模型修正是提高模型精度的一种重要方法.利用CHAMP卫星高精度加速仪反演的密度数据,采用球谐函数的形式对NRLMSISE-00模型进行修正.为了消除轨道高度变化对密度修正结果的影响,将密度数据同化到同一高度处,计算修正之后的密度误差,进而对未来三天的密度进行预报.结果表明,经球谐修正后,修正误差和预报误差均有显著降低.在太阳活动高年,修正误差可降至10%左右,提前1~3天预报精度分别提高31.34%,21.39%和13.75%;太阳低年时修正误差可降至14%左右,提前1~3天预报精度分别提高55.03%,47.79%和43.60%.      

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.      

HASDM修正方法分析与评估

分析Jacchia70（J70）热层模式原理、美国空军高精度卫星拖曳模型（HASDM）的修正方法及选取球面调和函数的原因.推导模式密度对球谐系数（SH）的偏导数,给出利用模式密度泰勒展开进行线性化处理、迭代求解球谐系数的具体过程.针对2003年10月29日大磁暴事件,基于CHAMP和GRACE A/B卫星加速度计实测密度,进行修正方法的性能评估.统计相对误差的均值及标准差变化:改进前分别为-81.7%,74.4%;改进后分别为-5.9%,53.1%,验证了改进算法的有效性.从热层上下边界温度角度,详细分析了热层模式动态修正原理,研究结果为类HASDM修正模式的工程应用提供了理论基础.      

利用二维核回归估计的大气密度模式修正

传统经验大气密度模式预测大气密度存在的较大误差会引起低轨卫星轨道预报误差,对卫星的再入轨、控制计划、碰撞规避及精密定轨造成不利影响.利用天宫一号卫星探测数据,针对大气NRLMSISE-00模式计算的误差特点,在地磁相对平静(Ap ≤ 30)的时间段内,对相近地方时和纬度的模式误差分布进行分析发现,相近地方时和纬度的模式误差分布基本相同.利用二维核回归估计方法,对与预测点相近地方时和纬度的样本误差进行加权,估计预测点处的模式误差,进而按距离预测日期天数的长短,采用加权修正法对模式预测结果进行修正,修正后大气模式误差的均方差(RMS)由14.09%降至4.05%.研究结果表明,该修正方法可以显著提高大气密度预报精度.      

Orbital phase spectroscopy of X-ray pulsars to study the stellar wind of the companion

High Mass X-ray Binary Pulsars (HMXBP), in which the companion star is a source of supersonic stellar wind, provide a laboratory to probe the velocity and density profile of such winds. Here, we have measured the variation of the absorption column density along with other spectral parameters over the binary orbit for two HMXBP in elliptical orbits, as observed with the Rossi X-ray Timing Explorer (RXTE) and the BeppoSAX satellites. A spherically symmetric wind profile was used as a model to compare the observed column density variations. In 4U 1538-52, we find the model corroborating the observations; whereas in GX 301-2, the stellar wind appears to be very clumpy and a smooth symmetric wind model seems to be inadequate in explaining the variation in column density.     

Storm-time atmospheric density modeling using neural networks and its application in orbit propagation

Upper atmospheric densities during geomagnetic storms are usually poorly estimated due to a lack of clear understanding of coupling mechanisms between the thermosphere and magnetosphere. Consequently, the orbit determination and propagation for low-Earth-orbit objects during geomagnetic storms have large uncertainties. Artificial neural networks are often used to identify nonlinear systems in the absence of rigorous theory. In the present study, an attempt has been made to model the storm-time atmospheric density using neural networks. Considering the debate over the representative of geomagnetic storm effect, i.e. the geomagnetic indices ap and Dst, three neural network models (NNM) are developed with ap, Dst and a combination of ap and Dst respectively. The density data used for training the NNMs are derived from the measurements of the satellites CHAMP and GRACE. The NNMs are evaluated by looking at: (a) the mean residuals and the standard deviations with respect to the density data that are not used in training process, and (b) the accuracy of reconstructing the orbits of selected objects during storms employing each model. This empirical modeling technique and the comparisons with the models NRLMSIS-00 and Jacchia-Bowman 2008 reveal (1) the capability of neural networks to model the relationship between solar and geomagnetic activities, and density variations; and (2) the merits and demerits of ap and Dst when it comes to characterizing density variations during storms.     

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%.     

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.     

Improved orbit predictions using two-line elements

The density of orbital space debris constitutes an increasing environmental challenge. There are two ways to alleviate the problem: debris mitigation and debris removal. This paper addresses collision avoidance, a key aspect of debris mitigation. We describe a method that contributes to achieving a requisite increase in orbit prediction accuracy for objects in the publicly available two-line element (TLE) catalog. Batch least-squares differential correction is applied to the TLEs. Using a high-precision numerical propagator, we fit an orbit to state vectors derived from successive TLEs. We then propagate the fitted orbit further forward in time. These predictions are validated against precision ephemeris data derived from the international laser ranging service (ILRS) for several satellites, including objects in the congested sun-synchronous orbital region. The method leads to a predicted range error that increases at a typical rate of 100 m per day, approximately a 10-fold improvement over individual TLE’s propagated with their associated analytic propagator (SGP4). Corresponding improvements for debris trajectories could potentially provide conjunction analysis sufficiently accurate for an operationally viable collision avoidance system based on TLEs only.