The field of wireless sensing and forecasting is experiencing significant growth, driven by the integration of generative artificial intelligence (GenAI) and advancements in deep learning techniques. Researchers are exploring the application of GenAI to improve wireless sensing systems, including device localization, human activity recognition, and environmental monitoring. Additionally, there is a strong focus on developing innovative forecasting models that can accurately predict solar irradiance, wind fields, and other environmental factors. These models are being designed to leverage diverse data sources, including satellite imagery, ground sensors, and 5G GNSS signals, to provide high-resolution forecasts that can support a wide range of applications, from renewable energy management to aviation safety. Noteworthy papers in this area include: SolarCAST, which presents a causally informed model for predicting future global horizontal irradiance, and MobiGPT, which introduces a foundation model for mobile data forecasting. SolarCrossFormer is also a notable work, which combines satellite images and time series from ground-based meteorological stations to improve the accuracy and resolution of day-ahead irradiance forecasts. G-WindCast is another significant contribution, which leverages 5G GNSS signals and deep learning to retrieve and forecast 3D atmospheric wind fields.