Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 731–744, 2022

Special issue: Benchmark datasets and machine learning algorithms for Earth...

Geosci. Model Dev., 15, 731–744, 2022
Model evaluation paper
27 Jan 2022
Model evaluation paper | 27 Jan 2022

EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets

Julian F. Quinting et al.

Data sets

ERA Interim, Daily ECMWF

JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data Japan Meteorological Agency

Model code and software

EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models Julian F. Quinting

Short summary
This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.