Building the Backbone for Efficient Climate Data Analysis
More and larger climate datasets than ever before are being produced, thanks to substantial investments in Earth observations, new climate models at groundbreaking resolutions, and increased global interest in understanding climate change. However, with this surge in data comes the challenge of how to efficiently analyse and use it. Theme 4 of the EXPECT project tackles this issue head-on by developing the infrastructure necessary for the efficient and flexible analysis of large and diverse climate datasets.
Why Does It Matter?
In the digital age, data is a valuable asset to underpin knowledge and decision-making, but only if it can be easily accessed, shared, and analysed. Climate research relies on vast amounts of data collected from various sources around the world, including satellite observations, weather stations, and climate models. The challenge is that this data is often stored in different locations, making it difficult and time-consuming for researchers to access and use it effectively. By creating a distributed system that allows data to be analysed where it is stored, rather than moving large datasets around, Theme 4 aims to streamline climate research and make it more efficient.
Creating a Flexible and Efficient Data Infrastructure
The EXPECT project is building on existing data repositories and platforms to create a system where data can be analysed without needing to be moved to different locations. This approach not only saves time and resources but also minimises the risk of data duplication and loss. The project focuses on making climate data FAIR—Findable, Accessible, Interoperable, and Reusable. This ensures that the data is not only easy to find and use but also compatible with various tools and systems used by researchers worldwide.
To achieve this, EXPECT is developing new tools and workspaces that allow for distributed data analysis. These tools will enable researchers to perform complex analyses on data stored in different locations, making it easier to combine and compare datasets. Additionally, the project is committed to open science by providing benchmarks, standardised datasets, and open-source code that make it easier for others to reproduce and build on EXPECT’s work.