Extracting information from high-resolution climate data

ACRP project HighResLearn

 

Project overview

Funding: ACRP Project, Climate and Energy Fund
Title: High-resolution machine learning for the climate community in Austria
Keywords: Climate, climate modeling, machine learning, data science, communication
Project lead: University of Vienna
Contact: Lukas Brunner (l.brunner@univie.ac.at)
Project partners: University of Innsbruck, GeoSphere Austria, CCCA Klimadashboard
Project duration: 01.06.2024-31.05.2028

 

Objective

The HighResLearn project develops methods to make the most out of the newest, highly resolved climate simulations.

Executive Summary

The project is structured in two main parts:

The development of data recipes for high-resolution climate data in collaboration with the research community in Austria. These recipes provide processing tools, accompanying documentation, and examples. They are built in coordination with the European Horizon 2020 project NextGEMS which provides the global, high-resolution climate simulations that serve as a starting point for the processing. 
The development of methods for the extraction and analysis of actionable climate information from highly resolved climate data. The data can come from different sources covering different spatial scales such as NextGEMS (global), CORDEX (Europe, Alpine region), and ÖKS (Austria). The methods will build on recent advances in machine learning for climate science and will apply them to regional data for Austria and Europe. 

In addition, HighResLearn will closely coordinate and collaborate with the national and international research community to build bridges between global and regional climate modeling. A special focus is also given to the communication of results to a broader public, for example by working with our local project partner Klimadashboard Austria. 

Relevant publications and additional information