Repositorio Institucional
Repositorio Institucional
CONICET Digital
  • Inicio
  • EXPLORAR
    • AUTORES
    • DISCIPLINAS
    • COMUNIDADES
  • Estadísticas
  • Novedades
    • Noticias
    • Boletines
  • Ayuda
    • General
    • Datos de investigación
  • Acerca de
    • CONICET Digital
    • Equipo
    • Red Federal
  • Contacto
JavaScript is disabled for your browser. Some features of this site may not work without it.
  • INFORMACIÓN GENERAL
  • RESUMEN
  • ESTADISTICAS
 
Artículo

On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation

Sacco, Maximiliano Antonio; Pulido, Manuel ArturoIcon ; Ruiz, Juan JoseIcon ; Tandeo, Pierre
Fecha de publicación: 05/2024
Editorial: John Wiley & Sons Ltd
Revista: Quarterly Journal of the Royal Meteorological Society
ISSN: 0035-9009
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Meteorología y Ciencias Atmosféricas

Resumen

Quantifying forecast uncertainty is a key aspect of state-of-the-art numerical weather prediction and data assimilation systems. Ensemble-based data assimilation systems incorporate state-dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work, a machine-learning method is presented based on convolutional neural networks that estimates the state-dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heteroscedastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman-like analysis update and the machine-learning-based estimation of a state-dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz´96 model as a proof-of-concept. The promising results show that the machine-learning method is able to predict precise values of the forecast covariance matrix in relatively high-dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter, outperforming it when the ensembles are relatively small.
Palabras clave: Machine learning , Data assimilation , Ensemble forecasting , Uncertainty quantification
Ver el registro completo
 
Archivos asociados
Tamaño: 7.982Mb
Formato: PDF
.
Solicitar
Licencia
info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/261077
URL: https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4743
DOI: http://dx.doi.org/10.1002/qj.4743
Colecciones
Articulos(CIMA)
Articulos de CENTRO DE INVESTIGACIONES DEL MAR Y LA ATMOSFERA
Articulos(IMIT)
Articulos de INST.DE MODELADO E INNOVACION TECNOLOGICA
Citación
Sacco, Maximiliano Antonio; Pulido, Manuel Arturo; Ruiz, Juan Jose; Tandeo, Pierre; On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation; John Wiley & Sons Ltd; Quarterly Journal of the Royal Meteorological Society; 150; 762; 5-2024; 2937-2954
Compartir
Altmétricas
 

Enviar por e-mail
Separar cada destinatario (hasta 5) con punto y coma.
  • Facebook
  • X Conicet Digital
  • Instagram
  • YouTube
  • Sound Cloud
  • LinkedIn

Los contenidos del CONICET están licenciados bajo Creative Commons Reconocimiento 2.5 Argentina License

https://www.conicet.gov.ar/ - CONICET

Inicio

Explorar

  • Autores
  • Disciplinas
  • Comunidades

Estadísticas

Novedades

  • Noticias
  • Boletines

Ayuda

Acerca de

  • CONICET Digital
  • Equipo
  • Red Federal

Contacto

Godoy Cruz 2290 (C1425FQB) CABA – República Argentina – Tel: +5411 4899-5400 repositorio@conicet.gov.ar
TÉRMINOS Y CONDICIONES