Development of a Scalable Cloud-Native Meteorological Data Analysis Platform

The organization relies on an outdated technology stack that impairs performance and maintainability when analyzing data collected from multiple sources such as weather stations, satellites, radiosondes, and lightning detectors. As the company plans to expand to new markets and incorporate additional data sources, there is a pressing need for a robust, scalable, and high-precision system capable of aggregating unstructured meteorological data and delivering accurate weather predictions.

A mid-sized organization providing weather forecasting and meteorological data services aimed at supporting agricultural and environmental decision-making across regions.

• Develop a cloud-native, scalable platform capable of integrating multiple meteorological data sources, including weather stations, satellites, radiosondes, and lightning detectors.
• Implement an ETL pipeline to transform unstructured meteorological data into standardized formats suitable for analysis and visualization.
• Automate data synchronization with existing data repositories at configurable intervals to ensure up-to-date information.
• Create predictive algorithms with a target accuracy of at least 97% for temperature and other weather-related variables at specified locations.
• Design visualization tools for displaying weather data on various synoptic charts representing rainfall, wind, pressure, and other meteorological conditions.
• Ensure ease of maintenance and scalability through modular architecture and cloud infrastructure.

• Data ingestion pipelines capable of processing data from multiple structured and unstructured sources including weather stations, satellites, and sensors.
• Data transformation modules to convert diverse data formats into a unified JSON format for analysis.
• Scheduler for automated, periodic synchronization with external data sources and repositories.
• Predictive algorithms and models with a minimum 97% accuracy level for temperature estimation and other weather parameters.
• Interactive visualization dashboards presenting data on various types of synoptic charts for different weather conditions.
• Monitoring and logging features to support system maintenance, performance tracking, and troubleshooting.

• Meteorological data repositories and data aggregation servers
• Weather stations, satellites, radiosondes, lightning detectors, and other sensor networks
• Visualization and analytics modules or dashboards

• Scalability to accommodate expansion from 6 to numerous data sources with increasing data volume
• High system availability and reliability with automated data refresh cycles
• Performance benchmarks ensuring real-time data processing and visualization
• Security measures for sensitive meteorological data and user access

The implementation of this platform is expected to improve data integration efficiency and prediction accuracy to at least 97%. It will enable the organization to support timely weather forecasting, improve operational decision-making, and facilitate expansion to new markets with enhanced scalability and maintainability of its meteorological data analysis capabilities.