Realizing Specific Weather Forecast through Machine Learning Enabled Prediction Model

To general people, it is more convenient to know weather condition at a specific location and particular time. However, current weather forecasting services offered by meteorological observation organizations only provide a wide-range or coarse-grained forecast. This research work tried to utilize historical weather observation data and machine learning (ML) techniques to build models enabling specific weather forecast. Different settings of models were applied and the corresponding results were compared and analyzed in terms of training cost and prediction quality. The preliminary results indicate that the ML-enabled forecast model can serve as a supplementary source for people who need to know finer-grained whether condition. To improve the quality of the ML forecasting models, besides more fine-tuning and algorithms renovation, large volume of long-term historical weather data are critical since climate changes to a large extent, possess subtle periodical characteristics.