Data for the manuscript "DEUCE v1.0: A neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties" by Harnist et al. (2023)

Kuvaus

# File descriptions: ## `verif_inputs.zip` and `verif_case_inputs.zip` Contain folders of raw PGM composites, containing needed input data for verification experiments, that is for the creation of baseline nowcast as well as observation HDF5 files. ## `deuce_inputs.zip` Contains HDF5 archives of the composite crops used for the training of DEUCE, derived from raw PGM composites using the `create_hdf5_dataset.py` script given. ## `deuce_model_checkpoints.py` Contains the two intermediate pytorch lightning checkpoints of DEUCE, as the final model checkpoint used for making predictions. ## `metrics.zip` Contains the netcdf files of verification metric values computed from DEUCE and baseline model predictions compared to observations.
Näytä enemmän

Julkaisuvuosi

2023

Aineiston tyyppi

Tekijät

Ilmatieteen laitos - Julkaisija

Harnist, Bent - Tekijä

Mäkinen, Terhi - Tekijä

Pulkkinen, Seppo - Tekijä

Projekti

Muut tiedot

Tieteenalat

Geotieteet

Kieli

englanti

Saatavuus

Avoin

Lisenssi

Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)

Avainsanat

precipitation, INSPIRE theme: climatologyMeteorologyAtmosphere, weather radar, Bayesian, convolutional, neural network, nowcasting, probabilistic, uncertainty quantification

Asiasanat

Ajallinen kattavuus

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