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arXiv · 2411.19031

Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature

Akshay Sunil·B Deepthi·Gaurav Ganjir·Muhammed Rashid·Rahul Sreedhar·Adarsh S
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Abstract

The growing adoption of machine learning (ML) in modelling atmospheric and oceanic processes offers a promising alternative to traditional numerical methods. It is essential to benchmark the performance of both ML and physics-informed ML (PINN) models to evaluate their predictive skill, particularly for short- to medium-term forecasting. In this study, we utilize gridded sea surface temperature (SST) data and six atmospheric predictors (cloud cover, relative humidity, solar radiation, surface pressure, u-component of velocity, and v-component of velocity) to capture both spatial and temporal patterns in SST predictions.

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Akshay Sunil, B Deepthi, Gaurav Ganjir, Muhammed Rashid, Rahul Sreedhar, Adarsh S. 2024-11-28. Assessing the potential of state-of-the-art machine learning and physics-informed machine learning in predicting sea surface temperature. https://arxiv.org/abs/2411.19031

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