Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols
The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/9/2377/2016/gmd-9-2377-2016.pdf |
| Summary: | The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated
radiance” product from any high-resolution general circulation model with
interactive aerosol as if a specific sensor such as the Moderate Resolution
Imaging Spectroradiometer (MODIS) were viewing a combination of the
atmospheric column and land–ocean surface at a specific location. Previously
the MCRS code only included contributions from atmosphere and clouds in its
radiance calculations and did not incorporate properties of aerosols. In
this paper we added a new aerosol properties module to the MCRS code that
allows users to insert a mixture of up to 15 different aerosol species in any
of 36 vertical layers.<br><br>This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol
Retrieval Simulator). Inclusion of an aerosol module into MCARS not only
allows for extensive, tightly controlled testing of various aspects of
satellite operational cloud and aerosol properties retrieval algorithms, but
also provides a platform for comparing cloud and aerosol models against
satellite measurements. This kind of two-way platform can improve the
efficacy of model parameterizations of measured satellite radiances,
allowing the assessment of model skill consistently with the retrieval algorithm.
The MCARS code provides dynamic controls for appearance of cloud and aerosol
layers. Thereby detailed quantitative studies of the impacts of various
atmospheric components can be controlled.<br><br>In this paper we illustrate the operation of MCARS by deriving simulated
radiances from various data field output by the Goddard Earth Observing
System version 5 (GEOS-5) model. The model aerosol fields are prepared for
translation to simulated radiance using the same model subgrid variability
parameterizations as are used for cloud and atmospheric properties profiles,
namely the ICA technique. After MCARS
computes modeled sensor radiances equivalent to their observed counterparts,
these radiances are presented as input to operational remote-sensing
algorithms.<br><br>Specifically, the MCARS-computed radiances are input into the processing
chain used to produce the MODIS Data Collection 6 aerosol product
(M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from
M{O/Y}D021KM MODIS Level-1B radiance product
directly acquired by the MODIS instrument. MCARS matches the format and
metadata of a M{O/Y}D021KM product. The
resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive
Processing System) as input to various operational atmospheric retrieval
algorithms. Thus the operational algorithms can be tested directly without
needing to make any software changes to accommodate an alternative input
source.<br><br>We show direct application of this synthetic product in analysis of the
performance of the MOD04 operational algorithm. We use biomass-burning case
studies over Amazonia employed in a recent Working Group on Numerical
Experimentation (WGNE)-sponsored study of aerosol impacts on numerical
weather prediction (Freitas et al., 2015). We demonstrate that a known low
bias in retrieved MODIS aerosol optical depth appears to be due to a
disconnect between actual column relative humidity and the value assumed by
the MODIS aerosol product. |
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| ISSN: | 1991-959X 1991-9603 |