Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts

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SEEK ID: https://publications.h-its.org/publications/1826

Filename: Song et al. (2024).pdf 

Format: PDF document

Size: 3.75 MB

SEEK ID: https://publications.h-its.org/publications/1826

DOI: 10.1007/s00376-023-3184-5

Research Groups: Computational Statistics

Publication type: Journal

Journal: Advances in Atmospheric Sciences

Publisher: Springer Science and Business Media LLC

Citation: Advances in Atmospheric Sciences, 1–21

Date Published: 1st Mar 2024

URL:

Registered Mode: manually

Authors: Mengmeng Song, Dazhi Yang, Sebastian Lerch, Xiang’ao Xia, Gokhan Mert Yagli, Jamie M. Bright, Yanbo Shen, Bai Liu, Xingli Liu, Martin János Mayer

Citation
Song, M., Yang, D., Lerch, S., Xia, X., Yagli, G. M., Bright, J. M., Shen, Y., Liu, B., Liu, X., & Mayer, M. J. (2024). Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts. In Advances in Atmospheric Sciences. Springer Science and Business Media LLC. https://doi.org/10.1007/s00376-023-3184-5
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Created: 26th Mar 2024 at 08:57

Last updated: 8th Apr 2024 at 11:41

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