Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 1107-1129, 2017
https://doi.org/10.5194/gmd-10-1107-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Tomaso1, Nick A. J. Schutgens2,a, Oriol Jorba1, and Carlos Pérez García-Pando3,4,b 1Earth Sciences Department, Barcelona Supercomputing Center, Spain
2Atmospheric, Oceanic and Planetary Physics, University of Oxford, UK
3NASA Goddard Institute for Space Studies, New York, USA
4Department of Applied Physics and Applied Math, Columbia University, New York, USA
anow at: Faculty of Life & Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands
bnow at: Earth Sciences Department, Barcelona Supercomputing Center, Spain
Abstract. A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets.

The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.


Citation: Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C.: Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129, https://doi.org/10.5194/gmd-10-1107-2017, 2017.
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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...
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