Description
A t test is normally used to determine if the mean values of two datasets are significantly different from each other. Therefore, it is possible to be implemented in the difference of integrated water vapor (IWV) time series obtained from two techniques, e.g. GPS and EARI, in order to detect the potential temporal shifts in the data from the two techniques. A penalized maximal t test (PMT) to empirically construct a penalty function that evens out the U-shaped false-alarm distribution over the relative position in the time series being tested. In addition, if there is a positive autocorrelation existing in the time series being tested and its effects are ignored, it is highly possible that wrong changepoints will be detected. Therefore, a penalized maximal t test modified to account for first-order autoregressive noise in time series (PMTred) was used on the synthetic data for the homogenization test.
To generate empirical critical values (CVs) for the PMTred test, we carried out a large number of Monte Carlo simulations for different sample length N. For each case, we simulated 1 000 000 homogenous independent and identically distributed (IID) Gaussian time series with a mean of zero and standard deviation of one. To take the lag-1 autocorrelation into account, we created the autocorrelated time series using a first-order autoregressive model (AR1). A changepoint is detected when the PMTred test statistic PTmax is larger than the CV corresponding to a 99.9% confidence level.
Primary author
Dr
TONG NING
(NT)
