Identifying errors in dust models from data assimilation
Abstract 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 unders...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-09-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/2016GL070621 |
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