On the importance of proper noise modelling for long-term precipitable water vapour trend estimations
On the importance of proper noise modelling for long-term precipitable water vapour trend estimations
复制标题
关于正确的噪声建模对于长期可降水水汽趋势估计的重要性
DOI：
10.2113/gssajg.110.2-3.211
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发表时间：
2007
影响因子：
1.8
通讯作者：
C. L. Merry
中科院分区：
文献类型：
--
作者：
A. Combrink;M. Bos;R. Fernandes;W. L. Combrinck;C. L. Merry
时间序列的可降水量（PWV），来自连续的全球定位系统（GPS）观测，分析了两个南非站HRAO和SUTH。由于水蒸气是一种主要的温室气体，观测到的大气PWV的变化可能是天气和气候变化的指示。我们的主要贡献是一个现实的噪声模型的PWV观测，使人们能够得出正确的结论的意义，推导出PWV的增加或减少，为给定的时间跨度超过五年。它表明，PWV残差后得到的拟合趋势和年度信号的数据，由于简单的模型的排除短期的分散，远远大于PWV的不确定性提供的GPS分析软件。虽然一个更好的解决方案，为相关的不确定性，通过使用这些PWV残差的方差的不确定性重新标度，它表明，阿尔马（1，1）噪声模型更好地代表相关的统计不确定性比简单的白色噪声模型。由阿尔马（1，1）推导的PWV趋势不确定性大约是重新标度的白色噪声模型的2倍。最后，有人认为，年度信号的变化，防止任何趋势估计使用时间序列短于约五年。定量措施，以确定所需的连续GPS观测数据的最小周期测量PWV的趋势，以指定的精度。作为我们的研究结果，我们的结论是，没有统计学上显着的PWV的趋势，观察在1998年和2006年之间的两个GPS站。
Time-series of precipitable water vapour (PWV), derived from continuous Global Positioning System (GPS) observations, are analysed for the two South African stations HRAO and SUTH. Since water vapour is a major greenhouse gas, observed changes in atmospheric PWV could be indicative of weather and climate change. Our main contribution is a realistic noise model of the PWV observations which enables one to draw correct conclusions about the significance of the derived PWV increase or decrease for given time spans longer than five years. It is demonstrated that the PWV residuals that are obtained after fitting a trend and yearly signal to the data are, due to the simple model’s exclusion of short-term scatter, much larger than the PWV uncertainties provided by the GPS analysis software. Although a better solution for the associated uncertainties is obtained by using the variance of these PWV residuals for the uncertainty rescaling, it is shown that the ARMA(1,1) noise model better represents the associated statistical uncertainties than the simple white noise model. The ARMA(1,1)-derived PWV trend uncertainties are approximately 2 times greater than those for a rescaled white noise model. Finally, it is argued that the variability of the annual signal prevents any trend estimation using time series shorter than about five years. A quantitative measure is presented to determine the minimum period of continuous GPS observational data required to measure PWV trends to a specified accuracy. As result of our study, we conclude that no statistically significant PWV trends are observed at the two GPS stations between 1998 and 2006.