№ 75-1 (том 2): ОБРАЗОВАНИЕ И НАУКА В XXI ВЕКЕ, Июнь, 2026
Научно-образовательные статьи

ONLINE WEATHER FORECAST WEBSITE

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  • Haldurdyyev Wepa ,
  • Jumagylyjov Azady
Haldurdyyev Wepa
Oguz han Engineering and technology university of Turkmenistan
Jumagylyjov Azady
Oguz han Engineering and technology university of Turkmenistan

Опубликован 16.06.2026

Ключевые слова

  • online weather forecast; meteorological data visualization; hyperlocal prediction; geospatial web platform; numerical weather prediction; severe weather alerting; user-centric design; AI in meteorology.

Как цитировать

W. Haldurdyyev, & A. Jumagylyjov. (2026). ONLINE WEATHER FORECAST WEBSITE. ОБРАЗОВАНИЕ И НАУКА В XXI ВЕКЕ, 75-1 (том 2). https://mpcareer-google.ru/index.php/journal/article/view/5814

Аннотация

Accurate, timely, and accessible weather information is essential to virtually every sector of modern society, from agriculture and transportation to disaster management and daily personal planning. While national meteorological agencies have long produced forecast data, the dissemination of this information to end users has historically been constrained by broadcast schedules, geographic generality, and limited interactivity. This paper introduces and conceptualizes the Online Weather Forecast Website (OWFW), an intelligent, data-driven web platform designed to aggregate meteorological data from multiple sources, apply localized forecast refinement, and deliver personalized, interactive weather information to users across devices and contexts. Unlike conventional weather portals that display static forecast tables or simple radar imagery, the OWFW leverages artificial intelligence (AI), geospatial visualization, real-time sensor fusion, and user behavior analytics to provide hyperlocal, temporally granular, and contextually relevant weather experiences. The article examines the theoretical foundations of numerical weather prediction dissemination, geospatial data visualization, and user-centric information design. We propose a four-layer architecture comprising data aggregation, forecast processing, visualization and interaction, and personalization layers. Furthermore, we discuss practical benefits (e.g., hyperlocal precision, real-time severe weather alerting, decision-support integration) and challenges (data latency, forecast uncertainty communication, information overload). Future research directions include AI-generated natural language forecast narratives, crowd-sourced observational data assimilation, and climate-aware long-range planning dashboards.

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Библиографические ссылки

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