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.

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

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