EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model

Julian F. Quinting and Christian M. Grams

Data sets

ERA Interim, Daily ECMWF https://apps.ecmwf.int/datasets/data/interim-full-daily

Model code and software

EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models Julian F. Quinting https://doi.org/10.5281/zenodo.5154980

EuLerian Identification of ascending Air Streams - ELIAS 2.0 Julian Quinting https://git.scc.kit.edu/nk2448/wcbmetric_v2.git

Short summary

Physical processes in weather systems importantly affect the midlatitude large-scale circulation. This study introduces an artificial-intelligence-based framework which allows the identification of an important weather system – the so-called warm conveyor belt (WCB) – at comparably low computational costs and from data at low spatial and temporal resolution. The framework thus newly enables the systematic investigation of WCBs in large data sets such as climate model projections.