Deep learning for post-processing ensemble weather forecasts
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme
weather events. This is typically accomplished with ensemble prediction systems, which …
weather events. This is typically accomplished with ensemble prediction systems, which …
Ensemble methods for neural network‐based weather forecasts
Ensemble weather forecasts enable a measure of uncertainty to be attached to each
forecast, by computing the ensemble's spread. However, generating an ensemble with a …
forecast, by computing the ensemble's spread. However, generating an ensemble with a …
Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks
R Brecht, A Bihlo - Geophysical Research Letters, 2023 - Wiley Online Library
Ensemble prediction systems are an invaluable tool for weather forecasting. Practically,
ensemble predictions are obtained by running several perturbations of the deterministic …
ensemble predictions are obtained by running several perturbations of the deterministic …
FatPaths: Routing in supercomputers and data centers when shortest paths fall short
M Besta, M Schneider, M Konieczny… - … Conference for High …, 2020 - ieeexplore.ieee.org
We introduce FatPaths: a simple, generic, and robust routing architecture that enables state-
of-the-art low-diameter topologies such as Slim Fly to achieve unprecedented performance …
of-the-art low-diameter topologies such as Slim Fly to achieve unprecedented performance …
Benchmark dataset for precipitation forecasting by post-processing the numerical weather prediction
Precipitation forecasting is an important scientific challenge that has wide-reaching impacts
on society. Historically, this challenge has been tackled using numerical weather prediction …
on society. Historically, this challenge has been tackled using numerical weather prediction …
Effects of reliability indicators on usage, acceptance and preference of predictive process management decision support systems
Despite the growing availability of data, simulation technologies, and predictive analytics, it
is not yet clear whether and under which conditions users will trust Decision Support …
is not yet clear whether and under which conditions users will trust Decision Support …
Towards replacing precipitation ensemble predictions systems using machine learning
R Brecht, A Bihlo - arXiv preprint arXiv:2304.10251, 2023 - arxiv.org
Precipitation forecasts are less accurate compared to other meteorological fields because
several key processes affecting precipitation distribution and intensity occur below the …
several key processes affecting precipitation distribution and intensity occur below the …
On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation
Quantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather
prediction and data assimilation systems. Ensemble‐based data assimilation systems …
prediction and data assimilation systems. Ensemble‐based data assimilation systems …
Autoencoder Framework for General Forecasting
Artificial intelligence backed forecasting systems, especially various types of autoencoders,
are frequently used for short-term and medium-term weather forecasting. Sometimes …
are frequently used for short-term and medium-term weather forecasting. Sometimes …
Development of a Random Forest Climate Model Correction Algorithm
T Holthuijsen - 2024 - researchsquare.com
In this paper, a variety of machine learning models for reducing climate model inaccuracy
are developed and critically examined. The most effective model at mitigating climate model …
are developed and critically examined. The most effective model at mitigating climate model …