Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method

Abstract

Due to limited number of weather stations and interruption of data collection, the temperature field data may be incomplete. In the past, spatial interpolation is usually used for filling the data gap. However, the interpolation method does not work well for the case of the large-scale data loss. Matrix completion has emerged very recently and provides a global optimization for temperature field data reconstruction. A recovery method is proposed for improving the accuracy of temperature field data by using sparse low-rank matrix completion (SLR-MC). The method is tested using continuous gridded data provided by ERA Interim and the station temperature data provided by Jiangxi Meteorological Bureau. Experimental results show that the average signal-to-noise ratio can be increased by 12.5%, and the average reconstruction error is reduced by 29.3% compared with the matrix completion (MC) method.

Time Series Recovery Using Adjacent Channel Data Based on LSTM: A Case Study of Subway Vibrations

“…Research on lost data recovery is usually based on historical data to build a data recovery model [1]. At present, the research methods for data recovery can be roughly divided into three categories: The first is traditional mathematical models, including the moving average method, historical trend method, residual grey model, and interpolation method [2][3][4][5][6]. The second is intelligent recovery methods, including nonparametric regression and neural networks [7,8].…”