Manuscript Topics

Modelling non-linear atmospheric scenarios using traditional methods is always challenging due to their complexity. On top of that imbalances occurred/occurring in natural systems due to anthropogenic activities brought more complications to the traditional modelling. On the other hand, we are witnessing changing climates which create significant environmental issues. However, the application of machine learning models with explanabilities for forecasting atmospheric variables offers a transformative approach to predicting weather patterns, climate changes, and environmental conditions with unprecedented accuracy. Machine learning approaches with advanced algorithms are capable of handling larger historical datasets and then identifying the complex patterns and correlations that traditional modelling techniques would often miss. This integration of artificial intelligence into meteorology not only enhances predictive capabilities but also holds the promise of improving disaster preparedness, agricultural planning, and overall climate resilience. Therefore, identifying the importance of the application of machine learning models for atmospheric variable forecasting, this special issue is proposed to attract the recent advances and state-of-the-art research work.

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