The field of time series forecasting and weather foundation models is rapidly evolving, with a focus on developing more accurate and efficient models. Recent research has highlighted the importance of adapting models to specific downstream tasks and incorporating multimodal inputs to improve performance. The use of transfer learning and fine-tuning has also been shown to be effective in improving model performance. Notably, some studies have demonstrated the potential of weather foundation models in predicting grid-critical variables, such as surface temperature and wind speed, with high accuracy. However, other research has raised concerns about the limitations of time series foundation models, including their susceptibility to catastrophic forgetting and limited generalizability to real-world benchmarks. Overall, the field is moving towards developing more robust and adaptable models that can handle complex and diverse time series data. Some noteworthy papers include: Aurora, which introduces a multimodal time series foundation model that supports zero-shot inference and achieves state-of-the-art performance on several benchmarks. TimeTic, which proposes a transferability estimation framework that predicts the performance of a time series foundation model on a downstream dataset. Fiaingen, which presents a novel generative method for financial time series data that matches real-world data quality and achieves state-of-the-art performance on several criteria.