2020 Weather Indicator Time Series Dataset

Currently, the meteorological industry faces the challenge of accurately predicting extreme weather and climate change. Existing meteorological datasets often lack sufficient time series data and precision, making them inadequate for dealing with variable weather patterns and complex environmental factors. Therefore, this dataset aims to provide high-density and high-precision meteorological indicators to support the development of more advanced predictive models and decision-making tools. Data collection is done through automated meteorological station equipment distributed across several key geographical locations, providing indicators such as temperature, humidity, air pressure, and wind speed. After data collection, it undergoes multiple rounds of consistency checks and expert reviews by a professional team to ensure accuracy and consistency. The data is stored in CSV format after standardized preprocessing, including missing value filling and noise filtering, which facilitates analysis and modeling. The team consists of 30 members with expertise in meteorology and data science, ensuring the scientific validity and practicability of the data. The core advantage of this dataset is its high-quality data, with annotation accuracy reaching 99%, and extremely high temporal integrity. Technically, new data cleaning and data enhancement techniques have been introduced to improve the predictive performance of models. Compared to traditional meteorological datasets, this dataset is more detailed and comprehensive, adapting to noisy weather environments and increasing predictive accuracy by at least 15%. The dataset is characterized by detailed weather changes recorded at small time intervals, suitable for the training and improvement of various meteorological models and particularly valuable in studies of extreme climate change. Additionally, its scalable structure allows for the addition of new meteorological parameters or geographical location records, possessing high generalizability and applicability.