Cargando…
Purely satellite data–driven deep learning forecast of complicated tropical instability waves
Forecasting fields of oceanic phenomena has long been dependent on physical equation–based numerical models. The challenge is that many natural processes need to be considered for understanding complicated phenomena. In contrast, rules of the processes are already embedded in the time-series observa...
Autores principales: | Zheng, Gang, Li, Xiaofeng, Zhang, Rong-Hua, Liu, Bin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439441/ https://www.ncbi.nlm.nih.gov/pubmed/32832620 http://dx.doi.org/10.1126/sciadv.aba1482 |
Ejemplares similares
-
Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data
por: Giffard-Roisin, Sophie, et al.
Publicado: (2020) -
Seasonality of Tropical Instability Waves and Its Feedback to the Seasonal Cycle in the Tropical Eastern Pacific
por: Im, Seul-Hee, et al.
Publicado: (2012) -
Tropical instability wave modulation of chlorophyll-a in the Equatorial Pacific
por: Shi, Wei, et al.
Publicado: (2021) -
Deep Learning for satellite imagery
por: Boget, Yoann
Publicado: (2019) -
Data-Driven Forecasting of Agitation for Persons with Dementia: A Deep Learning-Based Approach
por: HekmatiAthar, SeyyedPooya, et al.
Publicado: (2021)