【摘要】 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,对缺失的半小时温度观测数据进行高精度插补.本文构建的深度学习模型,采用了基于编码-解码结构的序列-序列深度学习结构(BiLSTM-I),模型编码层采用双向LSTM-I网络,解码层分别采用LSTM解码结构与全连接两种解码结构.试验分析结果表明,本文设计的BiLSTM-I深度学习温度插补方法要优于其他方法,可满足了高精度温度数据插补需要,而且LSTM解码结构的BiLSTM-I模型具有更好的数据插补精度.文章最后还分析了BiLSTM-I深度学习模型的泛化能力,结果表明BiLSTM-I模型具有不同温度缺失窗口长度的插补能力.更多还原

【Abstract】 Complete and high-precision temperature observation data are important input parameters for agrometeorological 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 study, 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 for high-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.更多还原