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

• URL: http://arxiv.org/abs/2504.09940v3
• Date: Tue, 22 Apr 2025 02:29:49 GMT
• Title: TianQuan-Climate: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
• Authors: Guowen Li, Xintong Liu, Shilei Cao, Haoyuan Liang, Mengxuan Chen, Lixian Zhang, Jinxiao Zhang, Jiuke Wang, Meng Jin, Juepeng Zheng, Haohuan Fu,
• Abstract summary: 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.<n>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.<n>We demonstrate the effectiveness of TianQuan-Climate on benchmarks for weather forecasting and climate projections within the 15 to 45-day range.
• Score: 5.8241701310554355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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