Empowering weather and climate forecast
This could become very influential:
Classical numerical models of the atmosphere exhibit biases due to incomplete process descriptions and they are computationally highly demanding. Recent AI-based weather forecasting models reduce the computational costs but lack the versatility of conventional models and do not provide probabilistic predictions.
The AtmoRep project asks if one can train one neural network that represents and describes all atmospheric dynamics. The FZJ team around Martin Schulz recently published a paper describing how this novel, task-independent stochastic computer model of atmospheric dynamics provides superior results for a wide range of applications: nowcasting, temporal interpolation, model correction, and counterfactuals.
Congrats to our MAELSTROM team members Bing Gong, Michael Langguth, Martin Schulz, and their FJZ colleagues!
Read the full paper here.
AtmoRep project page
We're approaching two major events before MAELSTROM turns into the home stretch! The registration for both our second Dissemination Workshop and our second Boot Camp are open. Be sure to reserve your place.
MAELSTROM partner and coordinator ECMWF has four (!) machine learning positions available. If you are a machine learning and/or HPC expert and want to walk the last mile towards operational use of ML in numerical weather predictions and climate services, get excited and check here!
Our second hackathon and our second dissemination workshop, which were both originally planned for spring 2023, will be held in the week of November 6
"We need more women in science." "We support women in science." - that's said easily. But when we took a closer look, we discovered lots of open questions and hidden conflicts. We conducted a range of interviews, checked existing initiatives and sources of information - but still have questions. Take a look and tell us what you think!
