Identifying errors in dust models from data assimilation

Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the...

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Detalles Bibliográficos
Autores principales: Pope, R. J., Marsham, J. H., Knippertz, P., Brooks, M. E., Roberts, A. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Research Letters
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082526/
https://www.ncbi.nlm.nih.gov/pubmed/27840459
http://dx.doi.org/10.1002/2016GL070621

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5082526/
https://www.ncbi.nlm.nih.gov/pubmed/27840459
http://dx.doi.org/10.1002/2016GL070621

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