arXiv:2606.15288v1 Announce Type: cross Abstract: Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models. We present a Hybrid NARX-LLM framework that combines a nonlinear autoregressive model with exogenous inputs (NARX) and a large language model (LLM) for residual correction. We further propose a Physics-Informed Prompt (PIP) method that transforms unstructured physical knowledge into structured prompts for zero-shot in-context reasoning. The primary objective is to explore the corrective potential of this framewo
The increasing sophistication of LLMs and the urgent need for better climate models are converging, making hybrid AI approaches for complex systems like iceberg discharge more viable now.
This development indicates a powerful new method for integrating expert knowledge into complex predictive climate models, potentially improving accuracy crucial for policy and infrastructure planning.
Traditional physical models can now be significantly enhanced by the interpretive and pattern-matching capabilities of LLMs, enabling more robust predictions in areas with 'limited observability'.
- · Climate scientists
- · Environmental policy makers
- · AI researchers
- · Arctic navigation industries
- · Traditional modeling approaches
- · Sectors reliant on outdated climate forecasts
More accurate predictions of Greenland iceberg discharge will emerge, improving understanding of sea-level rise mechanisms.
This methodology could be adapted to other complex, data-scarce environmental systems, accelerating climate science advancements.
Enhanced climate foresight could inform long-term infrastructure investments and adaptation strategies, potentially mitigating future economic and social costs of climate change.
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Read at arXiv cs.AI