## 1 Introduction
In the field of meteorological missing value imputation, this method has three basic forms of application. The first form is to use machine-learning algorithms directly. Some researchers have confirmed the validity of some machine-learning imputation methods. For instance, random forest (RF) [34] and multi-layer perceptron neural networks (MLP) [35] have the ability to accurately reconstruct meteorological variables, particularly temperature. In the research by [PERSON] [36], two statistical imputation methods (linear and spline) and three machine-learning methods (multivariate linear regression (MLR), RF, and MLP) interpolated the missing values of seven meteorological variables, respectively. The findings indicate that machine-learning imputation methods are more applicable than statistical imputation methods for handling missing data in both the short and long term. Moreover, MLP exhibited the highest level of accuracy across all experiments. The second form is based on the improvement of the existing algorithm. [PERSON] [37] designed an auto-encoder architecture with a convolution layer to reconstruct missing wind speed data. Compared with the six traditional data reconstruction methods, the designed network had the lowest reconstruction error. The third version combines various machine-learning techniques. For example, [PERSON] [38] used long short-term memory networks (LSTM) and support vector regression (SVR) to interpolate missing wind pressure data. [PERSON]'s model [39] is composed of temporal convolutional networks (TCN) and convolutional neural networks (CNN), which are used to estimate the missing value of PM2.5. The experimental results indicated that the combined model has a higher imputation accuracy than the single model. Multi-model combination can also be used to interpolate some meteorological parameters that are difficult to recover, such as precipitation. Precipitation data are a special challenge for the recovery of missing data, because these data are random and highly unbalanced in the duration of rainfall and non-rainfall. [PERSON] [40] proposed an imputation model of rainfall that was classified and then predicted. Gradient tree boosting, K-nearest neighbors (KNN), RF,support vector machines (SVM), and neural networks were tested via classification and prediction algorithms, respectively. The test results showed that a combination of RF and neural networks is superior to surface fitting technology in recovering missing precipitation data at a 30 min resolution. By reviewing the existing literature, it can be seen that the imputation method based on machine learning has advantages in the field of reconstruction of ground weather station missing values, but there are also some problems. First, much of the literature tends to focus on the imputation of a certain meteorological element, but when multiple parameters (such as temperature, humidity, wind speed, etc.) in the dataset have random missing values at the same time, using the existing data in the dataset to fill these missing values is a problem that is rarely discussed in the literature. Second, there are many machine-learning methods, but their verification in the field of meteorological data imputation is insufficient. Most machine-learning method types are verified in [39], but only eight methods are verified overall. Third, in addition to [PERSON]'s extensive tests on imputation algorithms of 134 weather stations [34], a limited number of studies have assessed the performance of machine-learning meteorological imputation methods in multiple locations or examined the effectiveness of different algorithms in various climatic regions. Finally, some comparisons are unfair, such as comparing a combined model with a single model, comparing a fine-tuned model with an untuned model, and running the algorithm with carefully selected and processed datasets.