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

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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

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Alexander I. Jordan

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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

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

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