A theoretical framework for the sampling error variance for three-dimensional climate averages of ICOADS monthly ship data
A theoretical framework for the sampling error variance for three-dimensional climate averages of ICOADS monthly ship data
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ICOADS 月度船舶数据三维气候平均值采样误差方差的理论框架
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
J. S. Greene
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文献类型：
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作者：
M. Morrissey;J. S. Greene
来自机遇之船的气象和海洋学数据是世界海洋表面数据库的最大贡献者，因此被广泛用于估计过去 150 年世界海洋气候特性的变化。这些数据对于气候变化研究的重要性强调了充分了解与这些数据平均值相关的误差的必要性。由于船舶是移动平台，因此报告随时间不断变化的位置的观测结果，因此船舶数据的采样误差问题尤其严重。本文开发了一个理论框架，用于评估与每月 1°×1° 经纬度盒平均船舶数据相关的平均采样误差。值得注意的是，平均域内船舶的时空分布对采样误差有很大影响。这在我们的推导中得到了体现。这里开发的框架可用于改进估计与从船舶记录获得的气象和海洋数据的三维箱平均值相关的采样误差的现有方法。该框架是对现有的评估由于仪器、记录等造成的偏差和随机误差的方法的补充。数学上证明，不完全采样导致的不确定性主要是观察数量和它们在箱内的相对位置以及感兴趣变量的固有时空相关结构之间的权衡。这项工作与其他研究的不同之处在于，在推导采样误差的表达式时考虑了数据的三维相互依赖性。
Meteorological and oceanographic data from ships of opportunity are the largest contributor to the world’s ocean surface database and thus are extensively used to estimate the change in climatic properties over the world’s oceans during the previous 150 years. The importance of these data for climate change studies underscores the need to fully understand the error associated with averages of these data. The sampling error problem is especially acute for ship data due to the fact that ships are moving platforms and, thus, report observations from constantly varying locations with time. This paper develops a theoretical framework for assessing the averaged sampling error associated with monthly, 1°×1° latitude-longitude box averaged ship data. It should be noted that the time-space distribution of ships within the averaging domain strongly affects the sampling error. This is shown in our derivation. The framework developed here can be used to improve upon existing methods for estimating the sampling error associated with three-dimensional box averages of meteorological and oceanographic data obtained from ship records. The framework is complimentary to existing methods of assessing biases and random error due to instrumentation, recording, etc. It is demonstrated mathematically that the uncertainty due to incomplete sampling is primarily a trade off between of the number of observations and their relative locations within the box as well as the inherent time-space correlation structure of the variable of interest. This work differs from other studies in that the three-dimensional interdependence of data is taken into account in deriving an expression for the sampling error.