The force produced by thermospheric density is the largest non-gravitational perturbation acting on low orbit space-crafts. It is difficult for existing atmospheric density models to satisfy space mission requirements because of the 15%-20% deviations in the models. With the NRLMSISE-00 empirical model as the density reference standard, an equation for temperature corrections and density is established by correcting the temperature parameters of Jacchia-Roberts empirical density model to calibrate density. The improved Gauss-Newton correction algorithm is chosen to avoid divergent solution of the equation in specific regions. The spatial-temporal characteristics of temperature corrections are captured by Empirical Orthogonal Function (EOF), and are then compared with the characteristics of temperature corrections captured by the traditional Spherical Harmonics (SH). Results show that more than 85% and 80% variations of temperature corrections are involved in the first 4 EOFs and the first 9 SH expansion functions. The first EOF reflects the overall bias of temperature corrections. The coefficients corresponding to the second to fourth EOF show that the temperature corrections have diurnal periodicity, and the coefficients obtained with the SH also have diurnal periodicity. Jacchia-Roberts model is calibrated by the reconstructed temperature corrections using the first 4 EOFs and the first 9 SH expansion functions, and the calibrated density deviations of Jacchia-Roberts reduce by 9.06% and 5.37%, respectively. It is confirmed that the EOF method has better efficiency than the SH method in temperature parameter correction and density model calibration.
ZHANG Houzhe
,
GU Defeng
,
DUAN Xiaojun
,
WEI Chunbo
. Atmospheric density model calibration using empirical orthogonal function[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018
, 39(S1)
: 722263
-722263
.
DOI: 10.7527/S1000-6893.2018.22263
[1] MONTENBRUCK O, GILL E. Satellite orbits-models methods and applications[M]. Heidelberg:Springer, 2000:83-102.
[2] EMMERT J T. Thermospheric mass density:A review[J]. Advances in Space Research, 2015, 56(5):773-824.
[3] MARCOS F A. New satellite drag modeling capabilities:AIAA-2006-0470[R]. Reston, VA:AIAA, 2006.
[4] DOORNBOS E. Thermospheric density and wind determination from satellite dynamics[M]. Heidelberg:Springer, 2012:41-43, 155-168.
[5] STORZ M F, BOWMAN B R, BRANSON M J I, et al. High accuracy satellite drag model (HASDM)[J]. Advances in Space Research, 2005, 36(12):2497-2505.
[6] 魏凤英. 现代气候统计诊断与预测技术[M].北京:气象出版社, 2007:106-113. WEI F Y. Modern technology of climate statistic diagnosis and prediction[M]. Beijing:China Meteorological Press, 2007:106-113(in Chinese).
[7] JACCHIA L G. New static models of the thermosphere and exosphere with empirical temperature profiles:SAO-SR-332[R]. Washington, D.C.:NASA, 1971.
[8] ROBERTS C E. An analytic model for upper atmosphere densities based upon Jacchia's 1970 models[J]. Celestial Mechanics, 1971, 4(3-4):368-377.
[9] BATES D M, WATTS D G. Nonlinear regression analysis and its applications[M]. Hoboken, NJ:John Wiley & Sons Inc., 1988:41-43.
[10] WANG Z M, YI D Y, DUAN X J, et al. Measurement data modeling and parameter estimation[M]. Boca Raton, FL:CRC Press, 2011:208-211.
[11] 张尧庭, 方开泰. 多元统计分析引论[M].北京:科学出版社, 1982:322-328. ZHANG Y T, FANG K T. An introduction to multivariate statistical analysis[M]. Beijing:Science Press, 1982:322-328(in Chinese).
[12] MARCOS F A, LAI S T, HUANG C Y, et al. Towards next level satellite drag modeling:AIAA-2010-7840[R]. Reston, VA:AIAA, 2010.