Development of a Scalable Cloud-Native Meteorological Data Analysis and Visualization Platform

The client operates an outdated web-based meteorological data analysis application that aggregates data from multiple sources, including hundreds of weather stations, radars, radiosondes, satellites, and lightning detectors. The existing technology infrastructure impairs system performance, scalability, and maintainability. With plans to expand into broader markets and incorporate additional data sources, there is an urgent need for a robust, scalable, and high-precision system that can unify unstructured meteorological data for analysis and visualization.

A mid-sized enterprise specializing in weather modification solutions for agriculture, requiring precise data analysis and forecasting capabilities.

• Create a cloud-native, scalable data processing platform capable of handling increasing volumes of meteorological data from multiple sources.
• Develop an ETL pipeline to transform unstructured data into a unified format suitable for analysis and visualization.
• Implement regular automated data extraction and updates from diverse aggregation servers.
• Develop algorithms to accurately estimate key meteorological parameters, such as temperature, with a target prediction accuracy of at least 97%.
• Design an interactive visualization system featuring multiple types of synoptic weather charts (e.g., precipitation, wind, pressure).
• Ensure system maintainability and ease of deployment using microservices architecture and cloud platform tools.

• An ETL pipeline capable of processing unstructured data formats such as meteorological maps and transforming them into JSON for analysis.
• A data scheduler to automate data collection and synchronization at predefined intervals.
• Algorithms for temperature estimation and other meteorological parameters with high accuracy (target 97%).
• A visualization module that displays weather conditions through various synoptic charts, including rainfall, wind, and pressure maps.
• Microservices-based architecture supporting modular development and easy scalability.
• Automated deployment and management using cloud-native tooling.

• Meteorological data sources including weather stations, radars, radiosondes, satellites, lightning detectors
• Customer's data aggregation servers for automated data retrieval

• Scalability to accommodate increased data sources and volume
• High system availability and reliability
• Data processing latency within acceptable timeframes to support real-time visualization
• Data security and compliance with relevant standards
• Maintainability and ease of updates via microservices architecture

The new meteorological data analysis platform aims to deliver high-precision weather predictions with at least 97% accuracy, significantly improving the client’s forecasting capabilities. The scalable, cloud-native architecture ensures seamless expansion to new data sources and markets, enhancing operational flexibility, reducing maintenance burden, and enabling quicker deployment of new features. As a result, the client can strengthen its market position in weather modification solutions and offer more reliable forecasting tools to the agricultural and environmental sectors.