Traditional numerical weather prediction methods are computationally intensive and struggle to capture localized weather phenomena

The gap

Evidence profile

Sourced from the future work and future-work section and stated research gap of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 6 representative gaps

• Weather Analysis Using Machine Learning (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  These experiments reveal that machine learning can induce a paradigm shift in weather prediction through the improvement both of its accuracy and efficiency. But there are some challenges to be solved with respect to data quality, model interpretability, as well as the cost in computation. Future research would revolve around hybrid models, real-time data integration, as well as AI-driven automation for improving forecasting techniques. This study thus adds to the emergent research line in meteorology and emphasizes the necessity of continuous improvement in weather prediction through artificial intelligence. Take a sample dataset and analyze the performance of different machine learning models for comparison.

  generalfuture work
  Keywords: machine learning weather prediction improvement well models experiments reveal induce paradigm shift accuracy efficiency there
• Answer to Comments on “Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods” (2026) · Environmental Science and Pollution Research · doi

  Development of hybrid forecasting methodologies that incorporate mechanistic atmospheric knowledge and machine learning. Investigation of the potential of explainable artificial intelligence in environmental governance and policy implementation. Improvement of data quality, harmonization, and interpretability in machine learning techniques.

  generalfuture-work sectionevidence 5/5
  Keywords: development hybrid forecasting methodologies incorporate mechanistic atmospheric knowledge
• Weather Prediction Using Artificial Intelligence Approaches, Challenges, and Future Directions (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  Traditional numerical weather prediction methods are computationally intensive and struggle to capture localized weather phenomena. Early machine learning approaches were limited by computational resources and shallow representational capacity. There is a need for more accurate and reliable weather forecasting methods.

  generalstated research gapevidence 5/5
  Keywords: traditional numerical weather prediction methods computationally intensive struggle
• Weather Prediction Using Artificial Intelligence Approaches, Challenges, and Future Directions (2026) · International Journal of Advanced Research in Science Communication and Technology · doi

  The integration of AI with physical modeling is a promising direction for future research. The use of more advanced AI architectures, such as transformer-based models, may improve the accuracy and reliability of weather forecasting. The development of more comprehensive and representative training datasets is necessary to improve the performance of AI models.

  generalfuture-work sectionevidence 5/5
  Keywords: integration physical modeling promising direction future research use
• Global open-ocean daily turbulent heat flux dataset (1992–2020) from SSM/I via deep learning (2026) · Earth System Science Data · doi

  Future research should focus on improving the accuracy of the DeepFlux dataset. Future research should explore the use of other machine learning algorithms for estimating air-sea turbulent heat fluxes. Future research should evaluate the performance of the DeepFlux dataset in different climate models and weather prediction systems.

  generalfuture-work sectionevidence 5/5
  Keywords: future research focus improving accuracy deepflux dataset explore
• A multi‐scale loss formulation for learning a probabilistic model with proper score optimisation (2026) · Quarterly Journal of the Royal Meteorological Society · doi

  The paper identifies the gap in existing probabilistic machine-learned weather forecasting models, which do not account for the multi-scale nature of atmospheric processes. The gap is addressed through the introduction of a multi-scale loss formulation, which allows the model to target specific scales and improve forecast skill.

  generalstated research gapevidence 5/5
  Keywords: paper identifies gap existing probabilistic machine-learned weather forecasting

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