Advancing Weather Forecasting Using Incremental Learning Techniques

Weather change has intensified extreme events, increasing risks to agriculture, public health, and disaster management. Conventional forecasting models face challenges from exponential data growth, non-stationary weather patterns (concept drift), and the need for real-time adaptability. Incremental Learning (IL)—a class of algorithms that continuously update models with streaming data without full retraining—offers adaptive, scalable, and drift-resilient forecasting capabilities. This study investigates the role of IL in advancing weather forecasting, examining methodologies, realworld applications, domain-specific challenges, and evaluation strategies. It reviews techniques including online ensemble models, continuous deep learning frameworks, and hybrid adaptive systems capable of learning from evolving weather data streams. The study discusses applications in temperature and rainfall forecasting, as well as extreme weather event detection. Key challenges include high spatiotemporal variability and incomplete or noisy satellite observations. The study concludes that IL is pivotal for next-generation adaptive weather forecasting and supports timely, reliable decision making in dynamic weather conditions