## 1 Introduction  
Ground meteorological observation data (GMOD) include multiple elements such as temperature, humidity, wind speed, wind direction, air pressure, precipitation, cloud cover, visibility, etc. These elements reflect meteorological conditions and changes on the earth's surface and are the basis of research on earth-related disciplines. For example, GMOD are inevitably used in the process of weather forecasting [1, 2] or climate change analysis [3, 4, 5]. Additionally, they are an important reference for societal production and life and can be used for disaster warning [6, 7, 8], agriculture [9, 10], forestry [11, 12, 13], tourism [14, 15], marine fisheries [16, 17], water conservancy [18, 19], transportation [20, 21], and other fields. Effective use of GMOD may help stakeholders, including the government, to avoid environmental problems and damages, providing a wide range of social benefits. To meet different service requirements and functions, weather stations can use various sensors to monitor different environmental factors and transmit data within minutes or even seconds. However, there are inevitable problems such as operational errors, sensor failures, network transmission failures, and storage failures that can cause missing values within the meteorological observation dataset. Consequently, adopting rational techniques to handle missing data during the data preprocessing phase is essential for ensuring the integrity and dependability of the data. The most direct method for missing value processing is deletion [22], which means deleting records with missing values or outliers. The advantage of this method is that it is simple to operate, but it may cause information loss, lead to data deviation [23], destroy the continuity of time series, and cause bias or even errors in the analysis results [24]. Imputing missing values is an effective way to make data complete and to avoid analysis errors or the inability to perform analysis due to missing values. Of course, imputing missing values also has certain limitations and risks, such as introducing some biases or errors, resulting in inaccurate or unreliable data analysis results. Therefore, when choosing the method of imputing missing values, it is necessary to choose the appropriate method according to the type, distribution, missing mechanism, and other factors of the data. The simplest imputation methods include the mean imputation [25] (replacing missing values with the average of non-missing values in the attribute), mode imputation [26] (replacing missing values with the most frequent value in the attribute), and median imputation [27] (replacing missing values with the middle value in the sorted data). However, these methods tend to reduce the overall variance of the imputed dataset and cause a large amount of data homogenization. Another common and simple imputation method is hot-deck imputation, which replaces missing data with values from existing data that meet a set of rules. The advantage of this method is that it uses the relationship between data to estimate missing values, but the disadvantage is that the rule setting is more subjective. The above two types of imputation methods are often used as benchmark methods for comparison with other imputation methods [28; 29]. Another frequently used technique for estimating missing values is multiple imputation. It has a better imputation effect than traditional methods such as mean imputation and median imputation [30; 31], but the imputation process may fail. [PERSON] [32] summarized the causes of imputation failure, including perfect prediction and collinearity. The imputation method based on machine learning can adapt to any missing patterns and has good robustness and small deviation. It also interpolates complex high-dimensional data better than mean, hot-deck, and multiple imputation methods [33]. This method learns the rules in the data through relevant algorithms and uses them to interpolate the data.