用低秩适配技术提升中东地区天气预报精度与效率
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region
- 采用低秩适配方法微调神经天气模型，聚焦特定区域
- 在中东地区实现更高精度且节省计算资源的预报结果
- 适合需要高效本地化气象建模的研究与应用
准确的天气与气候建模对科学进步及应对环境风险至关重要。传统数值天气预报（NWP）模型依赖物理方程模拟地球系统能量与物质流动，但计算开销大、效率低，难以满足实际需求。基于神经网络的方法作为数据驱动的替代方案应运而生。本文聚焦有限区域建模，针对中东（MENA）地区特定下游任务训练模型。该区域气候复杂，精准本地化预报对水资源管理、农业和极端天气应对尤为关键。研究验证参数高效微调（PEFT）方法，特别是低秩适配（LoRA）及其变体，在提升预报精度、训练速度、计算资源利用率和内存效率方面的有效性。
原文摘要 · Abstract (English)
Accurate weather and climate modeling is critical for both scientific advancement and safeguarding communities against environmental risks. Traditional approaches rely heavily on Numerical Weather Prediction (NWP) models, which simulate energy and matter flow across Earth's systems. However, heavy computational requirements and low efficiency restrict the suitability of NWP, leading to a pressing need for enhanced modeling techniques. Neural network-based models have emerged as promising alternatives, leveraging data-driven approaches to forecast atmospheric variables. In this work, we focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. As a case study, we consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. This targeted approach allows us to tailor the model's capabilities to the unique conditions of the region of interest. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
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