基于深度学习的温度观测数据长时间缺失值插补方法

完整高精度的温度观测数据是农业气象灾害监测、生态系统模拟重要的输入参数.由于野外气象观测条件的限制,气象观测数据缺失现象是常态,数据插补方法是气象数据应用必要处理步骤.本文针对野外小气象观测站站点半小时温度观测数据长时间缺失值问题,结合同一地点较低频次的人工温度观测,构建了新的温度缺失值插补深度学习模型,对缺失的半小时...

Interpolation of Long Time Missing Values of Temperature Based on Deep Learning

Complete and high-precision temperature observation data are important input parameters for agro-meteorological disaster monitoring and ecosystem simulation. Due to the limitation of meteorological field observation conditions, missing meteorological observation data is common. In response, interpolation becomes a necessary processing step before meteorological data application. In this paper, we construct a new deep learning model for interpolation of missing temperature data, which is employed to interpolate the missing half-hour temperature observations with high accuracy together with the low-frequency manual temperature observations at the same location. The deep learning model has a sequence-to-sequence deep learning structure based on the coding-decoding structure. A bidirectional LSTM-I (BiLSTM-I) network is used for the coding layer of the model, and an LSTM decoding structure and a fully connected decoding structure are respectively adopted for the decoding layer. The experimental analysis results show that the designed BiLSTM-I deep learning method for temperature interpolation is better than other methods. It can meet the need forhigh-precision temperature data interpolation. Particularly, the BiLSTM-I model with the LSTM decoding structure has higher data interpolation precision. The generalization ability of the BiLSTM-I deep learning model is also explored, and the results show that the model is effective in data interpolation for different lengths of the temperature missing window.