1 citations · 1 across the 4 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
Jonathan Schmidt, Luca Schmidt, Felix Strnad +2
Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical down…
stat.AP2024
Climate data selection for multi-decadal wind power forecasts
Sofia Morelli, Nina Effenberger, Luca Schmidt +1
Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global Climate Models (GCMs) and Regional Climate Models (RCMs) provide forecasts…
stat.AP2024★ 1 cited
Wind Power Assessment based on Super-Resolution and Downscaling -- A Comparison of Deep Learning Methods
Luca Schmidt, Nicole Ludwig
The efficient placement of wind turbines relies on accurate local wind speed forecasts. Climate projections provide valuable insight into long-term wind speed conditions, yet their…