Mathematics – Probability
Scientific paper
Dec 2007
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007agufmsa11a0290g&link_type=abstract
American Geophysical Union, Fall Meeting 2007, abstract #SA11A-0290
Mathematics
Probability
0350 Pressure, Density, And Temperature, 1610 Atmosphere (0315, 0325), 3270 Time Series Analysis (1872, 4277, 4475), 7959 Models, 7969 Satellite Drag (1241)
Scientific paper
A database of 5 years of CHAMP accelerometer neutral density measurements was examined for long and short term bias trends. Techniques applied were the same as those used in detecting a long term negative slope in satellite drag density to model ratios. This trend was attributed to thermospheric cooling. The straight line fits were found by standard least square methods. The estimate of the variance of the slope and the probability that the slopes were not zero was found using a Monte-Carlo technique. The reason standard estimates are not applicable is that the data is highly correlated. The Monte-Carlo technique assumed that the data was approximately equally spaced (one day apart) with missing data points. Each value for the Monte-Carlo technique was found as follows. Burg's algorithm with 50 linear predictor coefficients was used to estimate the autocorrelation of the data minus the straight line. The autocorrelation estimates were then used to estimate the power spectral density (PSD). The square root of the PSD was used to weight a set of frequency estimates from a set of pseudo Gaussian random numbers. The inverse FFT of the result plus the straight line is a simulated data set. Any missing data were placed in the simulated data set and the slope of a straight line fit to the simulated data was estimated. As an addendum to this study, CHAMP data were evaluated to detect and quantify unmodeled latitudinal variations.
Bowman Bruce R.
Grossbard Neil
Marcos Frank A.
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