Smart weather predictions for the 21st century through probabilistic AI-models initialized with strom-resolving climate projections - SmartWeather21
since 2024-05-01 - 2027-04-30
The ongoing warming of the earth's climate due to man-made climate change is fundamentally changing our weather. Traditionally, weather forecasts have been made based on numerical models, so-called Numerical Weather Predictions (NWP). Data-driven machine learning models and in particular deep neural networks offer the potential as surrogate models for fast and (energy) efficient emulation of NWP models. As part of the SmartWeather21 project, we want to investigate which DL architecture for NWP is best suited for weather forecasting in a warmer climate, based on the high-resolution climate projections generated with ICON as part of WarmWorld. To incorporate the high resolutions of the WarmWorld climate projections, we will develop data- and model-parallel approaches and architectures for weather forecasting in a warmer climate. Furthermore, we will investigate which (learnable combinations of) variables from the ICON climate projections provide the best, physically plausible forecast accuracy for weather prediction in a warmer climate. With this in mind, we develop dimension reduction techniques for the various input variables that learn a latent, lower-dimensional representation based on the accuracy of the downstream weather forecast as an upstream task. The increased spatial resolution of the ICON simulations also allows conclusions to be drawn about the uncertainties of individual input and output variables at lower resolutions. As part of SmartWeather21, we will develop methods that parameterise these uncertainties using probability distributions and use them as input variables with lower spatial resolution in DL-based weather models. These can be propagated through the model as part of probabilistic forecasts.
ICON-SmART
Contact: Dr. Jörg MeyerFunding: Hans-Ertel-Zentrum für Wetterforschung
“ICON-SmART” addresses the role of aerosols and atmospheric chemistry for the simulation of seasonal to decadal climate variability and change. To this end, the project will enhance the capabilities of the coupled composition, weather and climate modelling system ICON-ART (ICON, icosahedral nonhydrostatic model – developed by DWD, MPI-M and DKRZ with the atmospheric composition module ART, aerosols and reactive trace gases – developed by KIT) for seasonal to decadal predictions and climate projections in seamless global to regional model configurations with ICON-Seamless-ART (ICON-SmART).
Based on previous work, chemistry is a promising candidate for speed-up by machine learning. In addition, the project will explore machine learning approaches for other processes. The ICON-SmART model system will provide scientists, forecasters and policy-makers with a novel tool to investigate atmospheric composition in a changing climate and allows us to answer questions that have been previously out of reach.
Data and services to support marine and freshwater scientists and stakeholders - AquaINFRA
The AquaINFRA project aims to develop a virtual environment equipped with FAIR multi-disciplinary data and services to support marine and freshwater scientists and stakeholders restoring healthy oceans, seas, coastal and inland waters. The AquaINFRA virtual environment will enable the target stakeholders to store, share, access, analyse and process research data and other research digital objects from their own discipline, across research infrastructures, disciplines and national borders leveraging on EOSC and the other existing operational dataspaces. Besides supporting the ongoing development of the EOSC as an overarching research infrastructure, AquaINFRA is addressing the specific need for enabling researchers from the marine and freshwater communities to work and collaborate across those two domains.
