Articles | Volume 10, issue 3
https://doi.org/10.5194/gmd-10-1107-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-1107-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
10 Mar 2017
Development and technical paper |
| 10 Mar 2017
Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0
Enza Di Tomaso
CORRESPONDING AUTHOR
Earth Sciences Department, Barcelona Supercomputing Center, Spain
Nick A. J. Schutgens
Atmospheric, Oceanic and Planetary Physics, University of Oxford,
UK
now at: Faculty of Life & Earth Sciences, Vrije Universiteit, Amsterdam, the
Netherlands
Oriol Jorba
Earth Sciences Department, Barcelona Supercomputing Center, Spain
Carlos Pérez García-Pando
NASA Goddard Institute for Space Studies, New York, USA
Department of Applied Physics and Applied Math, Columbia
University, New York, USA
now at: Earth Sciences Department, Barcelona Supercomputing Center,
Spain
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Saved (final revised paper)
Latest update: 25 Apr 2024
Short summary
A data assimilation capability has been built for a chemical weather prediction system, with a focus on mineral dust. Before this work, dust was produced uniquely from model estimated emissions. As emissions are recognized as a major factor limiting the accuracy of dust modelling, satellite observations have been used to improve the description of the atmospheric dust load, with a significant impact on dust forecast from assimilating observations particularly relevant for dust applications.
A data assimilation capability has been built for a chemical weather prediction system, with a...
