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<p>Accurate weather forecasting stands as a crucial necessity for India. The government of India must therefore pursue the goal of weather forecasting with utmost accuracy as a guiding imperative from the monsoon to disastrous tropical cyclones. Traditional Numerical Weather Prediction (NWP) is suffering from fundamental limitations owing to computational requirements and difficulties with precise localized prediction of severe events. In this paper, the progress made in the area of Artificial Intelligence (AI) and Machine Learning (ML) is focused on as it marks a new chapter in the future of meteorology in India. We review the range of models from basic algorithms to deep learning models boosting ConvLSTMS, Transformers, and Graph Neural Networks in speeding up and improving prediction accuracy. The review looks at how agriculture, disaster management, and energy have improved significantly, it also review the adoption barriers such as the concerns over data shortage, computation facilities, and model interpretability, and discusses the technologys use in India.</p>

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Advancements in AI-based weather forecasting: A review of models, applications and challenges in the Indian context

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Tanmoy Dey,
  • Tanmoy Datta,
  • Anal Mandal,
  • Aniruddha Chatterjee,
  • Akash Kumar Pradhan,
  • Haraprasad Mondal,
  • Himanshu Ranjan Das,
  • Sandip Swarnakar,
  • Mohammad Soroosh,
  • N. K. Kaphungkui

摘要

Accurate weather forecasting stands as a crucial necessity for India. The government of India must therefore pursue the goal of weather forecasting with utmost accuracy as a guiding imperative from the monsoon to disastrous tropical cyclones. Traditional Numerical Weather Prediction (NWP) is suffering from fundamental limitations owing to computational requirements and difficulties with precise localized prediction of severe events. In this paper, the progress made in the area of Artificial Intelligence (AI) and Machine Learning (ML) is focused on as it marks a new chapter in the future of meteorology in India. We review the range of models from basic algorithms to deep learning models boosting ConvLSTMS, Transformers, and Graph Neural Networks in speeding up and improving prediction accuracy. The review looks at how agriculture, disaster management, and energy have improved significantly, it also review the adoption barriers such as the concerns over data shortage, computation facilities, and model interpretability, and discusses the technologys use in India.