44 citations · 49 across the 3 of their papers we have counts for
5 papers
Convolutional autoencoders for spatially-informed ensemble post-processing
Sebastian Lerch, Kai L. Polsterer
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks…
From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work
Kai Lars Polsterer, Antonio D'Isanto, Sebastian Lerch
The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides d…
Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
Benedikt Schulz, Mehrez El Ayari, Sebastian Lerch +1
In order to enable the transition towards renewable energy sources, probabilistic energy forecasting is of critical importance for incorporating volatile power sources such as sola…
Desublimation Frosting on Nanoengineered Surfaces
Christopher Walker, Sebastian Lerch, Matthias Reininger +4
Ice nucleation from vapor presents a variety of challenges across a wide range of industries and applications including refrigeration, transportation, and energy generation. Howeve…
Neural networks for post-processing ensemble weather forecasts
Stephan Rasp, Sebastian Lerch
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished w…