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Identifying errors in dust models from data assimilation
Author(s) -
Pope R. J.,
Marsham J. H.,
Knippertz P.,
Brooks M. E.,
Roberts A. J.
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2016gl070621
Subject(s) - data assimilation , mesoscale meteorology , environmental science , mineral dust , meteorology , dust storm , climatology , numerical weather prediction , climate model , moderate resolution imaging spectroradiometer , assimilation (phonology) , aerosol , atmospheric sciences , climate change , geology , geography , satellite , philosophy , linguistics , oceanography , aerospace engineering , engineering
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 characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development.