State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China.
Int J Environ Res Public Health. 2021 Sep 30;18(19):10321. doi: 10.3390/ijerph181910321.
Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model's excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios.
完整且分辨率高的温度观测数据是农气灾害监测和生态系统建模的重要输入参数。由于现场气象观测条件的限制，观测数据通常会存在缺失，因此在气象数据应用中需要采用适当的数据插补方法。本文主要关注填补现场气象观测数据中的长时间缺失。提出了一种基于深度学习的模型 BiLSTM-I，通过考虑低频手动获取的温度观测值，能够高精度地插补缺失的半小时温度观测值。BiLSTM-I 采用编码器-解码器结构，有利于充分学习数据的潜在分布模式。此外，BiLSTM-I 模型的误差函数包含了最终估计值与真实观测值之间的差异。因此，误差函数能够更直接地评估插补结果，模型的收敛误差与插补精度直接相关，从而确保在模型收敛时能够最小化插补误差。实验分析结果表明，本文设计的 BiLSTM-I 模型优于其他方法。对于时间间隔为 30 天或 60 天的测试集，均方根误差（RMSE）保持稳定，表明模型对不同缺失值间隔具有出色的泛化能力。虽然该模型仅应用于温度数据插补，但它也有可能应用于其他气象数据集填补场景。
