EuLerian Identification of ascending AirStreams - ELIAS 2.0

Features

• ELIAS 2.0 is suitable to identify the 2-dimensional footprints of rapidly ascending air streams in extratropical cyclones -- so-called warm conveyor belts (WCBs) -- from data at relative coarse spatio-temporal resolution using a Unet-type CNN
• the trained models can be directly downloaded from the repository
• ELIAS 2.0 is trained on ERA-Interim data at a global latitude-longitude grid of 1.0° grid spacing. The input data need to be provided on the same grid. Input data are needed at the following pressure levels: 1000, 850, 700, 500, 300, 200 hPa. These variables are required: temperature (T), specific humidity (qv), geopotential (phi), zonal wind (u), and meridional wind (v)
• the output is conditional probabilities for three stages of WCBs namely inflow, ascent and outflow. Thresholds to convert these conditional probabilities to binary footprints are provided.

Getting started

• ELIAS 2.0 is available from Zenodo or the Gitlab repository

Further information

These are the main references for citing ELIAS 2.0 in scientific publications:

• Quinting, J. F. and Grams, C. M.: EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model, Geosci. Model Dev., 15, 715–730, https://doi.org/10.5194/gmd-15-715-2022, 2022.
• Quinting, J. F., Grams, C. M., Oertel, A., and Pickl, M.: EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets, Geosci. Model Dev., 15, 731–744, https://doi.org/10.5194/gmd-15-731-2022, 2022.