# Study of Precipitation Forecast Based on Deep Belief Networks
## 1 Introduction  
Whether it is sailing, navigation, agriculture, or travel, accurate weather forecasts are always needed. In recent years, science and technology have developed rapidly, the methods of weather data collection have become more diverse, and more and more meteorological data can be collected. It is always a challenge to improve the accuracy of traditional weather forecasting with large amounts of collected meteorological data. Information and computer technology have driven the development of massive data analysis capabilities, stimulated the interest of many research groups in using machine learning techniques for big data research, and pushed them to explore hidden correlations of weather forecasting with big data sets. Our mission is to build a powerful weather forecasting model that uses a large amount of weather data to reveal hidden data associations in the data and ultimately improve the accuracy of weather forecasts.  
Precipitation forecasting is the core of the meteorological forecasting system. Improving the accuracy of precipitation prediction results is crucial to improving the forecast results of the entire meteorological forecasting system. Precipitation prediction is a complicated systematic project. The establishment of a meteorological forecasting system involves not only the collection and storage of data, such as climate, geography, and environment, but also accurate predictions based on the obtained data. This has always been a hot issue in the field of meteorological forecasting. Currently, precipitation data is collected mainly in the following three ways: Measurement of rain ganges, satellite-derived rainfall data, and radar rainfall estimation [1]. The three acquisition methods havetheir own advantages and disadvantages. Although the precipitation data obtained by rain ganges is accurate, it only reflects the precipitation in a small area, with poor spatial representativeness. Precipitation data from satellites and radars have a high coverage area, but the data accuracy is not very satisfactory. Therefore, the precipitation data collected by the automatic weather station is the most reliable data among the precipitation observation data. However, due to the limitation of the geographical environment and funds, the automatic weather station cannot be evenly distributed, so the observation data inevitably appear unevenly distributed in time and space. Although the accuracy of precipitation data from rain ganges is great, the data lacks continuity in time and space, and it is difficult to reflect the overall trend of regional climate change. Therefore, the existing ground weather station cannot meet the increasingly demanding accuracy requirements of today's precipitation products, and there is an urgent need for research breakthroughs.  
Nowadays, how to improve the accuracy of forecasts is a hot and difficult topic in the field of forecasting. In the era of big data, how to use the large amount of weather data collected to improve the accuracy of the traditional weather forecast rate has always been a challenge of weather forecasting. Our task is to create a powerful weather forecasting model that uses a large amount of weather data to reveal hidden data associations in the weather data and eventually improve the accuracy of the weather forecast.