用大模型预测2024年印度夏季雨季将偏多，提前三个月精准预报。
Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 2024
- 用PatchTST大模型融合历史降雨、厄尔尼诺和印度洋偶极子数据进行微调。
- 预测误差仅0.07%，相关性达0.976，比最优神经网络模型高80%准确率。
- 适合气象决策、农业规划和灾害应对等需要长期气候预判的领域。
可靠预测全印度夏季雨季（AISMR）对国家政策制定至关重要，影响数十亿人的生活。然而，由于多尺度因素复杂交互及季风系统固有的变异性，准确模拟AISMR一直是持续挑战。本研究聚焦于适配并微调最新大语言模型PatchTST，以实现三个月提前期的精确预测。该微调后的模型基于历史AISMR数据、Niño3.4指数及分类印度洋偶极子值训练，优于多种主流神经网络与统计模型。其均方根误差百分比低至0.07%，斯皮尔曼相关系数达0.976，相比最佳神经网络模型提升近80%精度。模型预测2024年为高于正常水平的雨季，全年6月至9月全国累计降雨量达921.6毫米。
原文摘要 · Abstract (English)
Reliable prediction of the All India Summer Monsoon Rainfall (AISMR) is pivotal for informed policymaking for the country, impacting the lives of billions of people. However, accurate simulation of AISMR has been a persistent challenge due to the complex interplay of various muti-scale factors and the inherent variability of the monsoon system. This research focuses on adapting and fine-tuning the latest LLM model, PatchTST, to accurately predict AISMR with a lead time of three months. The fine-tuned PatchTST model, trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values, outperforms several popular neural network models and statistical models. This fine-tuned LLM model exhibits an exceptionally low RMSE percentage of 0.07% and a Spearman correlation of 0.976. This is particularly impressive, since it is nearly 80% more accurate than the best-performing NN models. The model predicts an above-normal monsoon for the year 2024, with an accumulated rainfall of 921.6 mm in the month of June-September for the entire country.
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