Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0
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 combi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-03-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/10/1107/2017/gmd-10-1107-2017.pdf |
| Summary: | 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.
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The present study on mineral dust is a first step towards data assimilation
with a complete aerosol prediction system that includes multiple aerosol
species. |
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| ISSN: | 1991-959X 1991-9603 |