Deep learning for postprocessing global probabilistic forecasts on subseasonal time scales

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SEEK ID: https://publications.h-its.org/publications/1825

Filename: Horat and Lerch (2024).pdf 

Format: PDF document

Size: 16.7 MB

SEEK ID: https://publications.h-its.org/publications/1825

DOI: 10.1175/MWR-D-23-0150.1

Research Groups: Computational Statistics

Publication type: Journal

Journal: Monthly Weather Review

Publisher: American Meteorological Society

Citation: Monthly Weather Review, 152(3):667–687

Date Published: 1st Mar 2024

URL:

Registered Mode: manually

Authors: Nina Horat, Sebastian Lerch

Citation
Horat, N., & Lerch, S. (2024). Deep Learning for Postprocessing Global Probabilistic Forecasts on Subseasonal Time Scales. In Monthly Weather Review (Vol. 152, Issue 3, pp. 667–687). American Meteorological Society. https://doi.org/10.1175/mwr-d-23-0150.1
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Created: 26th Mar 2024 at 08:51

Last updated: 26th Mar 2024 at 08:52

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