A global provider of weather modification applications turned to Altoros to create a fully fledged platform for analyzing meteorological data and driving meaningful insights.
As a result of cooperation the customer got a weather forecasting platform capable of analyzing meteorological data and delivering predictions with 97% precision.
Based in Greece, the customer is a worldwide provider of weather modification solutions to the aviation and agricultural industries. Founded in 1976, the company is one of the most recognized organizations in its domain. With 25 years on the market, the firm has developed a range of solutions focused on hail, rain, and snow reinforcement.
The customer had a web-based application for gathering meteorological data. However, the solution was built on top of the outdated technology stack. As a result, analytical capabilities of the system left much to be desired.
Collaborating with Altoros, the company wanted to develop a fully fledged platform—for the agricultural industry—to gather meteorological data, drive meaningful insights, and visualize them in synoptic charts (temperature, rainfall, wind, etc.)
- It was important to ensure scalability, so that the system could sustain high data loads, coming from 6 types of sources: the WRF model, radars, weather stations, radiosondes, satellites, and lightning detectors.
- The solution must provide high-precision temperature calculations at any specified location.
- Instant search through large volumes of unstructured data had to be enabled.
Engineers at Altoros built a weather forecasting platform for data aggregation, processing, and predictive analysis. With a microservices-based architecture, the system ensures elastic scalability and is capable of sustaining high data loads coming from 6 source types. In order to estimate temperature at a specified location, our developers worked out a sophisticated algorithm that achieved 97% of calculation precision. By configuring data parsing, the team at Altoros enabled instant search through massive arrays of unstructured meteorological data. In addition, our engineers established the ETL (extract, transform, load) to efficiently manage the aggregated data. Finally, developers at Altoros made it possible to visualize data in a range of synoptic charts that illustrated different weather conditions (rainfall, wind, etc.)
types of data sources
types of synoptic charts
Partnering with Altoros, the customer developed a weather forecasting solution that aggregates, analyzes, and visualizes meteorological data in a range of synoptic charts. Gathering data from 6 source types (the WRF model, radars, weather stations, radiosondes, satellites, and lightning detectors), the platform features 97% prediction accuracy. Thanks to a microservices-based architecture, the system scales elastically and sustains high data loads.
Amazon Web Services
Frameworks and tools
Spring, Spring MVC, Spring Data JPA, Spring Data JDBC, AWS SDK, Amazon S3, Amazon EC2, Amazon Elastic Beanstalk, Hibernate, JUnit
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