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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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| Publicado no: | Geophys Res Lett |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
John Wiley and Sons Inc.
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5082526/ https://ncbi.nlm.nih.gov/pubmed/27840459 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/2016GL070621 |
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