Articles | Volume 10, issue 3
Geosci. Model Dev., 10, 1107–1129, 2017
https://doi.org/10.5194/gmd-10-1107-2017
Geosci. Model Dev., 10, 1107–1129, 2017
https://doi.org/10.5194/gmd-10-1107-2017

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 et al.

Viewed

Total article views: 2,414 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,419 885 110 2,414 137 132
  • HTML: 1,419
  • PDF: 885
  • XML: 110
  • Total: 2,414
  • BibTeX: 137
  • EndNote: 132
Views and downloads (calculated since 21 Sep 2016)
Cumulative views and downloads (calculated since 21 Sep 2016)

Viewed (geographical distribution)

Total article views: 2,280 (including HTML, PDF, and XML) Thereof 2,256 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 28 Nov 2021
Download
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.