Improving Short-Term Weather Predictions Using GANs

Short term weather forecast is very important to different fields like agriculture, disaster management and urban planning. Inherent complexity and non-linearity of atmospheric data presents a challenge for traditional forecasting methods. In a paper written this week, we propose a new way to improve short-term weather predictions using Generative Adversarial Networks (GANs), which are effective at generating high dimensional data. To increase the prediction accuracy, we propose a GAN architecture tailored for time series weather data that utilizes historical weather patterns. In the model are trained on extensive datasets of temperature, humidity, and precipitation and able to approximate the nature of weather phenomena. We show that the GAN based scheme outperforms conventional forecasting techniques in RMSE and MAE metrics. Additionally, visual comparisons show that GAN generated forecasts are closer to observed weather conditions. This work demonstrates the potential for use of GANs in transforming weather forecasting methodologies, the foundation for future exploration of their use in meteorology.