Abstract: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.