Imme Ebert-Uphoff

Publications

Corresponding Articles: 2

Imme Ebert Uphoff authored and/or contributed to the following articles/publications.

Estimating full longwave and shortwave radiative transfer with neural networks of varying complexity.

Radiative transfer (RT) is a crucial but computationally expensive process in numerical weather/climate prediction. We develop neural networks (NN) to emulate a common RT parameterization called the Rapid Radiative Transfer Model (RRTM), with the goal of creating a faster parameterization for the Global Forecast System (GFS) v16. In previous wor...

Institution National Oceanic and Atmospheric Administration - NOAA

Machine-learned uncertainty quantification is not magic: Lessons learned from emulating radiative transfer with ML

Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks. Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model. However, because ML-UQ is a new tool, its limitations are not yet fully appreciated. For example, some ...

Institution National Oceanic and Atmospheric Administration - NOAA