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摘要: 在对气象数据进行插值的过程中, 如果只考虑数据的空间信息而忽视数据在时间上的关联, 必然影响插值的精度.针对具有时空特性的气象数据, 提出一种将时空Kriging方法与弹性网方法相结合的新方法.该方法主要利用弹性网算法解决时空Kriging算法中的时空变异函数矩阵为病态矩阵而无法求逆的问题, 通过弹性网算法获得变异函数矩阵方程的稀疏解, 从而提高时空插值的精度.在实际观测的气温数据和AQI数据上的仿真实验验证了该方法对气象时空数据插值的准确性.Abstract: In the process of interpolating meteorological data, if we only consider the spatial information of data and ignore the correlation of data in time, it will inevitably affect the accuracy of interpolation. For the meteorological data with time and space characteristics, this paper combines the spatial-temporal Kriging method with elastic net algorithm. This method uses the elastic net algorithm to solve the problems that the spatial-temporal variational function matrix in the spatial-temporal Kriging algorithm is ill-posed and we can not find its pseudoinverse. The elastic net algorithm is used to obtain the sparse solution of the variational function equation to improve the accuracy of spatial temporal interpolation. The simulation experiments on the observed temperature data and AQI data verify the high accuracy of the proposed method for spatial and temporal data interpolation.
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Key words:
- Spatial-temporal data /
- spatial-temporal Kriging /
- interpolation /
- elastic net algorithm
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图 4 STKriging对射阳市气温数据插值效果
Fig. 4 Interpolation curves of STKriging on temperature data of Sheyang City
图 8 STKriging对青岛市AQI数据插值效果
Fig. 8 Interpolation curves of STKriging on AQI data of Qingdao City
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