TianQuan-Climate: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State

Abstract: Subseasonal forecasting serves as an important support for Sustainable Development Goals (SDGs), such as climate challenges, agricultural yield and sustainable energy production. However, subseasonal forecasting is a complex task in meteorology due to dissipating initial conditions and delayed external forces. Although AI models are increasingly pushing the boundaries of this forecasting limit, they face two major challenges: error accumulation and Smoothness. To address these two challenges, we propose Climate Furnace Subseasonal-to-Seasonal (TianQuan-Climate), a novel machine learning model designed to provide global daily mean forecasts up to 45 days, covering five upper-air atmospheric variables at 13 pressure levels and two surface variables. Our proposed TianQuan-Climate has two advantages: 1) it utilizes a multi-model prediction strategy to reduce system error impacts in long-term subseasonal forecasts; 2) it incorporates a Content Fusion Module for climatological integration and extends ViT with uncertainty blocks (UD-ViT) to improve generalization by learning from uncertainty. We demonstrate the effectiveness of TianQuan-Climate on benchmarks for weather forecasting and climate projections within the 15 to 45-day range, where TianQuan-Climate outperforms existing numerical and AI methods.