Circle Case study

Space weather – Enhanced weather forecasting systems for the Met Office

How CACI enhanced the existing Met Office space weather forecast systems & the services it delivers to it's customers

Space weather describes conditions in space that can interact with the Earth’s upper atmosphere and magnetic field that disrupts modern technology, radio communications and satellite operations including GPS and power grids.

The Met Office owns the UK space weather risk on behalf of the Department for Business, Energy and Industrial Strategy (BEIS). As part of the National Risk Assessment mitigation strategy, the Met Office delivers space weather services to government, critical national infrastructure (CNI) providers and the public.


CACI Information Intelligence Group were asked to undertake this project to enhance the existing Met Office space weather forecast systems and the services it delivers to customers. These enhancements include implementing new scientific forecasting models, incorporating new data sources and preparatory work ahead of transferring the system into the AWS cloud.

Space Weather is on the UK’s national risk registry as a high impact, high likelihood event and as such this system needs to be secure and have high availability 24/7/365.


The team came up with the following solutions:

  • A large microservice application built primarily with Java, Spring and Apache Camel using a combination of JMS and REST.
  • This was deployed as a hybrid of cloud (AWS) and on premises.
  • External data sources – Multiple data formats (jpg, png, video, json, csv, text, NetCDF) ingested into a Mongo database from:
  • External sources – ESWDS, NASA, ESA (European Space Agency)
  • Internal sources – Complex scientific models, supercomputer, forecaster analysis
  • Javascript front end clients using Dojo to present a data to:
  • Internal expert users (forecasters)
  • External expert users


This system is in constant development with an increasingly wide range of products being introduced for existing and new clients. The team has successfully completed the deployment of the first model to the AWS cloud, integrated with the current on-premises system.


If you have any questions or want to learn more, get in touch today.