STUDY OF THE TEMPORAL-SPATIAL FLUCTUATIONS AND EMPIRICAL MODEL OF NEAR SPACE ATMOSPHERIC DENSITY
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摘要: 大气密度是飞行器能源动力设计、飞行控制中的重要输入参数. 近年来, 多项研究发现, 对于临近空间高层大气, 常用经验模型如USSA-76, NRLMSISE-00的密度预示值偏大. 另外, 随着飞行器精细化设计趋势, 要求经验模型可预示不同纬度、昼夜和季节条件下的密度特性. 基于此, 本文利用卫星观测数据全面分析了临近空间大气密度随纬度、地方时、日期的变化规律, 包括变化模式和变化幅度. 纬度、日期引起的变化幅度大于地方时引起的变化幅度. 纬度、日期引起的密度变化在不同高度范围表现出不同模式; 变化幅度在78 km附近达最大值, 在22 km和92 km附近达局部极小值. 地方时引起的密度变化随高度递增. 根据大气密度的时空变化特性, 本文提出了临近空间大气密度的时空扰动模型, 该模型能描述临近空间大气密度及其随纬度、地方时、日期的变化规律. 与以往经验模型相比, 时空扰动模型可更好地描述不同高度下密度的时空变化规律. 在相同误差带条件下, 本文模型的置信度明显优于NRLMSISE-00. 本文建模方法合理, 模型结果对临近空间飞行器设计有应用价值.Abstract: Atmospheric density is a fundamental parameter for vehicle designing and flight controlling. In recent years, many researchers have discovered that atmospheric densities in the upper mesosphere and lower thermosphere predicted by the empirical model, such as USSA-76 and NRLMSISE-00, are larger than the measured values. On the other hand, vehicle designing is tending to be more detailed, and engineers hope that the empirical models provide densities under variable latitudes, day-night times and seasons. Based on that, the present work analyzesdependences of near-space atmospheric density on latitude, solar local time and date, by using satellite observed data. Emphasizes are put on density fluctuation patterns and amplitudes. The fluctuation patterns caused by latitude and date vary with altitude, and the amplitudes are largest at 78 km and locally smallest at about 22 km and 92 km. The fluctuation amplitude caused by the solar local time increases monotonically with altitude. Based on the temporal-spatialfluctuationlaw, we proposed the temporal-spatial fluctuation modelfor the near-space atmospheric density, which describes the density fluctuations with latitude, local time and date. The present model describes the temporal-spatial fluctuations better than the existed empirical models at variable altitudes. The confidence coefficient of the present model is much better than NRLMSISE-00 under the same error band. The modeling method in this work is reasonable, and the obtained model could be used in near-space vehicle designing.
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图 1 卫星观测的14~108 km (a) 纬度、(b) 地方时和(c) 日期对大气密度的影响规律
Figure 1. Variations of atmospheric density with (a) latitude, (b) local time and (c) date observed by satellite at altitudes from 14 to 108 km
图 2 高度(a) 70 km和(b) 100 km, (ϕ, d)平面内的大气密度变化规律
Figure 2. Variations of atmospheric density with ϕ and d at altitudes of (a) 70 km and (b)100 km
图 3 纬度、日期、地方引起的密度扰动幅度随高度的变化规律
Figure 3. Profiles of variation amplitudes caused by latitude, local time and date
图 4 卫星测量平均大气密度及其与USSA-76、本文模型的相对偏差
Figure 4. Profiles of averaged atmospheric density observed by satellite and its relative deviation from USSA-76 and present model
图 5 14~108 km高度下, 卫星测量的密度时空扰动项的特征分布范围
Figure 5. Range of temporal-spatial fluctuations of density observed by satellite at altitudes of 14~108 km
图 6 时空扰动测量值与模型值之差在高度14~108 km内的变化. (a)均值及特征分布范围和(b)均方根
Figure 6. Difference of temporal-spatial fluctuations of density between observed values and modeled values at altitudes of 14-108 km. (a) Mean value and range (b) root of mean square
图 7 本文模型描述的(a) 纬度、(b) 地方时和(c) 日期对大气密度的影响在高度14~108 km内的变化
Figure 7. Variations of atmospheric density with (a) latitude, (b) local time and (c) date calculated by present model at altitudes from 14 to 108 km
图 8 (a) 80 km, (b) 90 km, (c) 100 km高度下, SABER数据与三种模型预测结果对比. 红点: SABER, 绿点: NRLMSISE-00模型, 蓝点: 时空扰动模型, 黑线: USSA-76模型
Figure 8. Comparison of three atmospheric density models and observed data at altitudes (a) 80, (b) 90, (c) 100 km. Red symbol: SABER, green symbol: NRLMSISE-00, blue symbol: temporal-spatial variation model, black line: USSA-76
表 1 80, 90, 100 km高度, 太阳活动强(2002)、弱(2008)年份的密度
Table 1. At altitudes 80, 90, 100 km , the global mean density for years with higher solar activity (2002) and lower solar activity (2008)
Altitude/km Global mean density in 2002/(kg·m−3) Global mean density in 2008/(kg·m−3) 80 1.64 × 10−5 1.56 × 10−5 90 3.01 × 10−6 2.79 × 10−6 100 5.36 × 10−7 4.67 × 10−7 
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表 2 高度80, 90, 100 km, 3种大气模型, 不同误差带要求下, 对比SABER数据的置信度
Table 2. Altitudes 80, 90, 100 km, under different error bands, confidence coefficients of the three atmospheric models compared to SABER data
Altitude/km Error band ±30% ±50% ±80% 80 USSA-76 66.1% 85.2% 96.5% 80 NRLMSISE-00 88.6% 98.9% 99.9% 80 present model 99.0% 99.9% 99.99% 90 USSA-76 68.3% 92.1% 99.1% 90 NRLMSISE-00 76.5% 95.9% 99.7% 90 present model 98.7% 99.9% 99.98% 100 USSA-76 73.0% 87.7% 95.2% 100 NRLMSISE-00 75.4% 90.8% 97.7% 100 present model 93.2% 98.5% 99.7%
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