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(1) Projected changes of extreme hourly precipitation over Germany determined from a convection permitting multi-model ensemble (2) tbd  (3) tbd (4) tbd

Predicting atmospheric processes in complex terrain remains one of the greatest challenges in modern meteorology. The South Tyrolean Meteorological Service operates a dense observational network in a region defined by extreme orographic complexity. This presentation explores the synergy between our operational mandates and our participation in the international TEAMx program.
By integrating local observations into the TEAMx framework, we aim to improve the validation of kilometric-scale weather models and enhance the understanding convective events and sub-mesoscale flow dynamics in the Alps. This collaboration ensures that cutting-edge atmospheric research directly informs better forecasting tools for mountain regions.
 

(1) tbd (2) Partial Analysis Increments of the ‘Swabian MOSES 2023’ campaign in the ICON-D2 model (3) The representation of equatorial waves in data-driven weather prediction models (4) Assimilating Doppler wind lidar observations from ‘Swabian MOSES 2023’ reveals wind biases in the ICON-D2 model

Earth system modeling has fundamentally contributed to our understanding of past, present and future climate. Regional-scale multidecadal to centennial variability has been identified as a model blind spot, as across general circulation model generations they showed much lower levels of temperature variance than reconstructions, and underpredict regional state-dependency. In this talk I will discuss recent work on closing this gap, what this implies for projections of temperature extremes, and how TERRA aims to improve capacities to project global change impacts.

Deep learning emulates atmospheric reanalyses with high fidelity, enabling increasingly well-calibrated ensemble weather forecasts at progressively longer lead times. To extend these gains to climate-relevant horizons, AI prediction systems must produce credible forced responses to drivers of interest (e.g., greenhouse gases, land-use change). We propose a minimal, testable framework for AI climate modeling: (i) represent external forcings explicitly and restrict them to physically appropriate state tendencies; and (ii) stress-test robustness in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading climate emulators and hybrid physics-AI models, we identify coupling and development challenges and compare scaling with resolution and effective complexity. AI models do not appear intrinsically more efficient than GPU-ported dynamical models once complexity is accounted for, yet they can directly predict target variables at the desired grid without integrating the full high-frequency, multivariate state. Diverse ML downscaling strategies can partially substitute for explicit fine-scale resolution when observations are available, paving the way towards inexpensive, local risk assessment across prediction horizons

(1) tbd (2) tbd (3) Renewable Energy Systems in a Changing Climate(4) tbd

TBD

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(1) Predictability of the 2014 Pentecost storm (2) tbd (3) Added-Value of the Coming Decade ICONXPP Decadal Climate Predictions Ensemble for User-relevant Variables in Europe (4) tbd