Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 2024

TL;DR

This paper demonstrates that fine-tuning a large language model, PatchTST, can significantly improve the accuracy of monsoon rainfall predictions for India three months in advance, outperforming traditional models.

Contribution

The study adapts and fine-tunes the latest LLM, PatchTST, for accurate monsoon rainfall prediction, achieving unprecedented precision and outperforming existing neural and statistical models.

Findings

RMSE percentage of 0.07% indicating high accuracy

Spearman correlation of 0.976 showing strong predictive correlation

Predicted above-normal monsoon rainfall for 2024 with 921.6 mm in June-September

Abstract

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 Ni\~no3.4 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.