Hybrid Approaches in Weather Forecasting: From Numerical Models to Deep Learning

Yuding Wu

doi:10.54254/2753-8818/2025.DL27256

Theoretical and Natural ScienceOpen access

Hybrid Approaches in Weather Forecasting: From Numerical Models to Deep Learning

Research Article

Open Access

Hybrid Approaches in Weather Forecasting: From Numerical Models to Deep Learning

Yuding Wu ^1*

¹ University of Toronto

^*Corresponding author: yudingwu1@gmail.com

Published on 2 October 2025

TNS Vol.132

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-305-5

ISBN (Online): 978-1-80590-306-2

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Abstract

This survey reviews the fundamental principles and major approaches used in modern forecasting, with a focus on numerical weather prediction (NWP) and deep learning. NWP remains the cornerstone of operational forecasting, utilizing mathematical equations of atmospheric dynamics to produce high-resolution predictions. The computational demands and strengths of physics-based methods are exemplified by representative models such as the Global Seasonal Forecast System (GloSea5) and the Weather Research and Forecasting (WRF) system. However, deep learning techniques like as convolutional architectures and distribution-based neural networks each have advantages that make them helpful for examining huge meteorological datasets with nonlinear correlations. Validation, uncertainty quantification, model interpretability, and accurate forecasting of extreme weather events remain challenges despite advancements. It is anticipated that future advancements in high-performance computing, multi-source data assimilation, and hybrid methodologies that integrate machine learning and physical modeling will increase the utility and dependability of predictions. This study summarizes recent advancements, points out unresolved issues, and suggests exciting avenues for further weather forecasting research.

Keywords:

Weather forecasting, Numerical weather prediction, Deep learning

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References

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References

[2]. Coiffier, J. (2011). Fundamentals of numerical weather prediction (C. Sutcliffe, Tran.; 1st ed.). Cambridge University Press.

[3]. Dodla, V. B. R. (2023). Numerical weather prediction. CRC Press.

[10]. Teague, K. A., & Gallicchio, N. (2017). The evolution of meteorology: a look into the past, present, and future of weather forecasting. Wiley Blackwell.

Cite this article

Wu,Y. (2025). Hybrid Approaches in Weather Forecasting: From Numerical Models to Deep Learning. Theoretical and Natural Science,132,55-59.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Simulation and Theory of Differential-Integral Equation in Applied Physics

ISBN: 978-1-80590-305-5(Print) / 978-1-80590-306-2(Online)

Editor: Marwan Omar, Shuxia Zhao

Conference website: https://www.confapmm.org/dalian.html

Conference date: 27 September 2025

Series: Theoretical and Natural Science

Volume number: Vol.132

ISSN: 2753-8818(Print) / 2753-8826(Online)

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