Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models

• URL: http://arxiv.org/abs/2509.22020v1
• Date: Fri, 26 Sep 2025 07:54:05 GMT
• Title: Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models
• Authors: Shilei Cao, Hehai Lin, Jiashun Cheng, Yang Liu, Guowen Li, Xuehe Wang, Juepeng Zheng, Haoyuan Liang, Meng Jin, Chengwei Qin, Hong Cheng, Haohuan Fu,
• Abstract summary: We introduce WeatherPEFT, a novel PEFT framework for Weather Foundation Models (WFMs)<n>Task-Tu Dynamic Prompting (TTu) injects the embedding weights within the encoder to the input tokens of the backbone of the pre-trained via internal and external pattern extraction.<n>Fisher-Guided Adaptive Selection (SFAS) identifies the most task-critical randomness, preserving in pre-trained knowledge, but also stabilizing the selection.<n>We demonstrate the effectiveness and efficiency of WeatherPEFT on three downstream tasks, where existing PEFT methods show significant gaps versus Full-Tuning, and WeatherPEFT performance parity
• Score: 28.79157031663951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment. Current Parameter-Efficient Fine-Tuning (PEFT) methods, designed for vision or language tasks, fail to address the unique challenges of weather downstream tasks, such as variable heterogeneity, resolution diversity, and spatiotemporal coverage variations, leading to suboptimal performance when applied to WFMs. To bridge this gap, we introduce WeatherPEFT, a novel PEFT framework for WFMs incorporating two synergistic innovations. First, during the forward pass, Task-Adaptive Dynamic Prompting (TADP) dynamically injects the embedding weights within the encoder to the input tokens of the pre-trained backbone via internal and external pattern extraction, enabling context-aware feature recalibration for specific downstream tasks. Furthermore, during backpropagation, Stochastic Fisher-Guided Adaptive Selection (SFAS) not only leverages Fisher information to identify and update the most task-critical parameters, thereby preserving invariant pre-trained knowledge, but also introduces randomness to stabilize the selection. We demonstrate the effectiveness and efficiency of WeatherPEFT on three downstream tasks, where existing PEFT methods show significant gaps versus Full-Tuning, and WeatherPEFT achieves performance parity with Full-Tuning using fewer trainable parameters. The code of this work will be released.

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