- 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.
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.