Cargando…
A Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning
The initial field has a crucial influence on numerical weather prediction (NWP). Data assimilation (DA) is a reliable method to obtain the initial field of the forecast model. At the same time, data are the carriers of information. Observational data are a concrete representation of information. DA...
Autores principales: | Dong, Renze, Leng, Hongze, Zhao, Juan, Song, Junqiang, Liang, Shutian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871406/ https://www.ncbi.nlm.nih.gov/pubmed/35205558 http://dx.doi.org/10.3390/e24020264 |
Ejemplares similares
-
A Hybrid Method Using HAVOK Analysis and Machine Learning for Predicting Chaotic Time Series
por: Yang, Jinhui, et al.
Publicado: (2022) -
Couple of the Variational Iteration Method and Fractional-Order Legendre Functions Method for Fractional Differential Equations
por: Yin, Fukang, et al.
Publicado: (2014) -
The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework
por: Abbaszadeh, Peyman, et al.
Publicado: (2019) -
Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application
por: Zang, Zengliang, et al.
Publicado: (2022) -
Correction to ‘Learning earth system models from observations: machine learning or data assimilation?’
por: Geer, A. J.
Publicado: (2022)