Evaluation of machine learning techniques for forecast uncertainty quantification-Reference-Cited by-同舟云学术

Evaluation of machine learning techniques for forecast uncertainty quantification

Published:2022-10 Issue:749 Volume:148 Page:3470-3490
ISSN:0035-9009
Container-title:Quarterly Journal of the Royal Meteorological Society
language:en
Short-container-title:Quart J Royal Meteoro Soc

Author:

Sacco Maximiliano A.¹²^ORCID,Ruiz Juan J.²³⁴^ORCID,Pulido Manuel⁴⁵^ORCID,Tandeo Pierre⁶^ORCID

Affiliation:

1. Servicio Meteorológico Nacional Buenos Aires Argentina

2. Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina

3. Centro de Investigaciones del Mar y la Atmósfera (CIMA) CONICET – Universidad de Buenos Aires Buenos Aires Argentina

4. CNRS – IRD – CONICET – UBA, Instituto Franco‐Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI) Buenos Aires Argentina

5. Departamento de Física, Facultad Ciencias Exactas y Naturales y Agrimensura Universidad Nacional del Nordeste Corrientes Argentina

6. Lab‐STICC UMR CNRS 6285, IMT Atlantique Plouzané France

Funder

Fondo para la Investigación Científica y Tecnológica

Universidad de Buenos Aires

Publisher

Wiley

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4362

Reference48 articles.

1. Improving the Analog Ensemble Wind Speed Forecasts for Rare Events

2. Controlled Abstention Neural Networks for Identifying Skillful Predictions for Regression Problems

3. Machine Learning for Model Error Inference and Correction

4. Ensemble Postprocessing Using Quantile Function Regression Based on Neural Networks and Bernstein Polynomials

5. Camporeale E.(2018)Accuracy‐reliability cost function for empirical variance estimation. arXiv preprint arXiv:1803.04475.

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