Articles | Volume 15, issue 2
Model description paper
27 Jan 2022
Model description paper |  | 27 Jan 2022

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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-276', Anonymous Referee #1, 13 Oct 2021
    • AC1: 'Reply on RC1', Julian Quinting, 02 Nov 2021
  • RC2: 'Comment on gmd-2021-276', Anonymous Referee #2, 23 Oct 2021
    • AC2: 'Reply on RC2', Julian Quinting, 02 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Julian Quinting on behalf of the Authors (03 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (08 Dec 2021) by Travis O'Brien
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.