Information technology for weather forecast based on modern platform solutions

Weather forecasting plays a crucial role in numerous industries and activities, ranging from agriculture and energy to tourism and transportation. In recent years, information technologies have significantly enhanced the capabilities of weather forecasting, providing more accurate and timely data. This article explores innovative information technologies employed in weather forecasting and their impact on modern practices. It highlights the utilization of cloud computing and data storage for managing vast amounts of meteorological data, enabling the use of more precise forecasting models. Additionally, the article discusses the integration of Internet of Things (IoT) and sensor networks, which facilitate the collection of weather data from diverse sources and contribute to localized and real-time weather predictions. Artificial intelligence (AI) and machine learning techniques are also examined for their ability to analyze large datasets, identify patterns, and improve forecast accuracy. Finally, the article emphasizes the importance of advanced data visualization techniques in effectively conveying weather information to end-users. By harnessing these information technologies, weather forecasting continues to advance, empowering various industries and enhancing decision-making processes. 

1. Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47-55.

2. Bhattacharya, B., Chen, Y., & Rasheed, K. (2018). Cloud computing applications in weather forecasting: A review. Journal of Big Data, 5(1), 1-18.

3. Brown, T. B., & Harris, N. L. (2019). Satellite remote sensing of weather and climate: A review. Wiley Interdisciplinary Reviews: Climate Change, 10(5), e593.

4. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., ... & Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553-597.

5. Hacker, J. P., McCollum, J., & Richardson, D. (2018). Using artificial intelligence to improve weather forecasting. Bulletin of the American Meteorological Society, 99(7), 1331-1339.

6. Lavers, D., & Villarini, G. (2019). Advances in understanding and simulating extratropical cyclones: Results from the HAPPI workshop. Bulletin of the American Meteorological Society, 100(8), ES253-ES256.

7. Li, J., Li, Z., & Zhang, X. (2019). Weather forecasting by integrating big data: A survey. Big Data Research, 15, 35-42.

8. Mohanty, S. P., Skoric, B., Collier, C. G., & Teng, H. (2017). Internet of Things (IoT) in the era of big data: Opportunities, challenges, and enabling technologies. Big Data and Cognitive Computing, 1(1), 1-24.

9. Richardson, D., & Fowler, H. J. (2017). Predicting the risk of extreme climate events using statistical models: an international comparison. Weather and Climate Extremes, 15, 10-20.

10. WMO (World Meteorological Organization). (2021). Guidelines on Multi-hazard Impact-based Forecast and Warning Services.