An Improved AI Weather Prediction System

Weather prediction remains a critical challenge due to the nonlinear and dynamic nature of atmospheric systems, as traditional numerical weather prediction (NWP) models struggle to process large, high-dimensional meteorological data and often lack the adaptability needed for accurate short- and medium-term forecasts, particularly during extreme weather events. This study develops an improved AI-driven weather prediction system that enhances forecasting accuracy for temperature, humidity, wind speed, and atmospheric pressure through a Random Forest predictive model integrated with the Open Weather Map API and geolocation services. Using Agile methodology, data was collected, preprocessed, and trained in Python, while a web-based interface was built with JavaScript/TypeScript and React for visualization. The proposed system achieved approximately 87% short-term forecasting accuracy (87% applies to 1 – 7-day short term forecast), demonstrating improved precision and enhanced early-warning capability for extreme events. Comparative evaluation showed that, whereas the existing edge-based system was constrained by low processing power, maintenance overhead, and security concerns, the proposed AI system outperformed it in accuracy, adaptability, and real-time usability, offering significant benefits for agriculture, disaster management, and urban planning.

Artificial intelligence, weather prediction, Random Forest, machine learning

1. Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., & Gentine, P. (2021). Enforcing analytic constraints in neural networks emulating physical systems. Physical Review Letters, 126(9), 098302. https://doi.org/10.1103/PhysRevLett.126.098302 [Google Scholar] [Crossref]

2. Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., & Tian, Q. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619(7970), 533–538. https://doi.org/10.1038/s41586-023-06185-3 [Google Scholar] [Crossref]

3. Chantry, M., Christensen, H., Dueben, P., & Palmer, T. (2021). Opportunities and challenges for machine learning in weather and climate modelling. Philosophical Transactions of the Royal Society A, 379(2194), 20200083. https://doi.org/10.1098/rsta.2020.0083 [Google Scholar] [Crossref]

4. Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P. (2020). Analog forecasting of extreme-causing weather patterns using deep learning. Journal of Advances in Modeling Earth Systems, 12(2), e2019MS001958. https://doi.org/10.1029/2019MS001958 [Google Scholar] [Crossref]

5. Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., Su, R., Ci, Y., Li, B., Yang, X., & Ouyang, W. (2023). FengWu: Pushing the skillful global medium-range weather forecast beyond 10 days lead. arXiv preprint arXiv:2304.02948. [Google Scholar] [Crossref]

6. Collins, M., Knutti, R., Arblaster, J., Dufresne, J. L., Fichefet, T., Friedlingstein, P., et al. (2013). Long-term climate change: Projections, commitments and irreversibility. In Climate change 2013: The physical science basis (pp. 1029–1136). Cambridge University Press. [Google Scholar] [Crossref]

7. Ham, Y.-G., Kim, J.-H., & Luo, J.-J. (2019). Deep learning for multi-year ENSO forecasts. Nature, 573(7775), 568–572. https://doi.org/10.1038/s41586-019-1559-7 [Google Scholar] [Crossref]

8. Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), 124007. https://doi.org/10.1088/1748-9326/ab4e55 [Google Scholar] [Crossref]

9. Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318–2331. https://doi.org/10.1109/TKDE.2017.2720168 [Google Scholar] [Crossref]

10. Kaur, J. (2025). Edge AI in environmental monitoring: Real-time processing for a sustainable future. XenonStack Blog. https://www.xenonstack.com/blog/environmental-monitoring-with-edge-ai [Google Scholar] [Crossref]

11. McGovern, A., Lagerquist, R., Gagne, D. J., Jergensen, G. E., Elmore, K. L., Homeyer, C. R., & Smith, T. (2017). Using artificial intelligence to improve real-time decision-making for high-impact weather. Bulletin of the American Meteorological Society, 98(10), 2073–2090. https://doi.org/10.1175/BAMS-D-16-0123.1 [Google Scholar] [Crossref]

12. Pasche, O. C., Wider, J., Zhang, Z., Zscheischler, J., & Engelke, S. (2024). Validating deep-learning weather forecast models on extreme events. Weather and Climate Extremes, 46, 100731. [Google Scholar] [Crossref]

13. Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020). WeatherBench: A benchmark dataset for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems, 12(11), e2020MS002203. https://doi.org/10.1029/2020MS002203 [Google Scholar] [Crossref]

14. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1 [Google Scholar] [Crossref]

15. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., et al., & Bengio, Y. (2019). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 1–96. https://doi.org/10.1145/3485128 [Google Scholar] [Crossref]

16. Scher, S. (2018). Toward data-driven weather and climate forecasting: Approximating a simple general circulation model with deep learning. Geophysical Research Letters, 45(22), 12,616–12,623. https://doi.org/10.1029/2018GL080704 [Google Scholar] [Crossref]

17. Smith, L. A., Suckling, E. B., Thompson, E., Maynard, T., & Du, H. (2019). Towards improving the framework for probabilistic forecast evaluation. Climatic Change, 132(1), 31–45. [Google Scholar] [Crossref]

18. Trenberth, K. E., Jones, P. D., Ambenje, P., Bojariu, R., Easterling, D., Klein Tank, A., et al. (2007). Observations: Surface and atmospheric climate change. In Climate change 2007: The physical science basis (pp. 235–336). Cambridge University Press. [Google Scholar] [Crossref]

19. Weyn, J. A., Durran, D. R., & Caruana, R. (2020). Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002109. https://doi.org/10.1029/2020MS002109 [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition