The recent advancements in the field of meteorology and weather forecasting are significantly shifting towards the integration of AI and machine learning techniques. There is a notable trend towards developing generalist models capable of handling a wide array of weather understanding tasks within a unified framework, addressing the limitations of models that focus on single tasks. These models leverage in-context learning and visual prompting question-answering paradigms to enhance their versatility and performance across diverse tasks such as weather forecasting, super-resolution, and image translation. Additionally, there is a growing emphasis on adaptive and degradation-aware models for image restoration, which are designed to handle various weather conditions more effectively by integrating contrastive language-image pre-training and diffusion models. The field is also witnessing the democratization of advanced AI-based atmospheric modeling through platforms that offer flexible, scalable, and user-friendly environments for training and deploying these models. These platforms aim to address the limitations of traditional physics-based systems by providing end-to-end pipelines for data preprocessing, model training, and evaluation. Furthermore, there is a surge in the development of hybrid models that combine the strengths of convolutional neural networks and transformers to tackle complex image restoration tasks, enhancing both performance and computational efficiency. These developments collectively signify a transformative shift towards more integrated, adaptive, and efficient solutions in meteorology and weather forecasting.