Multi-sensor cloud retrieval simulator and remote sensing from model parameters – Part 1: Synthetic sensor radiance formulation

Wind, G.; Silva, A. M. da; Norris, P. M.; Platnick, S.

doi:10.5194/gmd-6-2049-2013

Cited by 16 publications

(46 citation statements)

References 41 publications

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“…For example, the cloudonly mode corresponds to a clean atmosphere with no aerosols; this mode was the only one considered in Wind et al (2013). In the current paper we consider additional options afforded by the implementation of the aerosol effect.…”

Section: Discussionmentioning

confidence: 99%

“…We produced the simulation input data in accordance with the methods outlined in Wind et al (2013). The GEOS-5 model output is split into 1 km subcolumns using the independent column approximation (ICA) method as described in detail in Wind et al (2013).…”

Section: The Mcars Softwarementioning

confidence: 99%

“…The GEOS-5 model output is split into 1 km subcolumns using the independent column approximation (ICA) method as described in detail in Wind et al (2013). Here we give a brief summary of the model data preparation methodology.…”

Section: The Mcars Softwarementioning

confidence: 99%

“…The Multi-Sensor Cloud and Aerosol Retrieval Simulator (MCARS; Wind et al, 2013) is a modular, flexible tool, in which model output is coupled with a radiative transfer code in order to simulate top-of-atmosphere (TOA) radiances that may be measured by a remote-sensing instrument if it were passing over the model fields. In principle, MCARS can be applied to any model/visible-IR radiometer combination.…”

Section: Introductionmentioning

confidence: 99%

“…However, in this paper, the MCARS continues to use the combination of GEOS-5 model and discrete ordinate radiative transfer (DISORT) code (Stamnes et al, 1988) to simulate MODIS radiances. In Wind et al (2013), the MCARS simulated only clouds; here we add microphysical properties of aerosols present in scenes we examine.…”

Section: Introductionmentioning

confidence: 99%

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Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

et al. 2016

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Abstract. 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.This new MCRS code is now known as MCARS (Multisensor 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.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.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.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 k...

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Section: Discussionmentioning

confidence: 99%

Section: The Mcars Softwarementioning

confidence: 99%

Section: The Mcars Softwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

et al. 2016

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Global observations of aerosol-cloud-precipitation-climate interactions

et al. 2014

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Cloud drop condensation nuclei (CCN) and ice nuclei (IN) particles determine to a large extent cloud microstructure and, consequently, cloud albedo and the dynamic response of clouds to aerosol-induced changes to precipitation. This can modify the reflected solar radiation and the thermal radiation emitted to space. Measurements of tropospheric CCN and IN over large areas have not been possible and can be only roughly approximated from satellite-sensor-based estimates of optical properties of aerosols. Our lack of ability to measure both CCN and cloud updrafts precludes disentangling the effects of meteorology from those of aerosols and represents the largest component in our uncertainty in anthropogenic climate forcing. Ways to improve the retrieval accuracy include multiangle and multipolarimetric passive measurements of the optical signal and multispectral lidar polarimetric measurements. Indirect methods include proxies of trace gases, as retrieved by hyperspectral sensors. Perhaps the most promising emerging direction is retrieving the CCN properties by simultaneously retrieving convective cloud drop number concentrations and updraft speeds, which amounts to using clouds as natural CCN chambers. These satellite observations have to be constrained by in situ observations of aerosol-cloud-precipitation-climate (ACPC) interactions, which in turn constrain a hierarchy of model simulations of ACPC. Since the essence of a general circulation model is an accurate quantification of the energy and mass fluxes in all forms between the surface, atmosphere and outer space, a route to progress is proposed here in the form of a series of box flux closure experiments in the various climate regimes. A roadmap is provided for quantifying the ACPC interactions and thereby reducing the uncertainty in anthropogenic climate forcing.

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Cloud type frequency over Poland (2003–2021) revealed by independent satellite‐based (MODIS) and surface‐based (SYNOP) observations

Wojciechowska

Kotarba

Żmudzka

2023

Intl Journal of Climatology

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Recent studies have identified several statistically significant trends in cloud genera frequency over Poland. Notably, there has been an increase in high and convective clouds, along with a decrease in Stratus, Altostratus and Nimbostratus. These earlier studies were, however, only based on traditional, surface data. As the ability to observe mid and high‐level clouds from the ground is limited, due to overlapping, we address the question of whether these trends are confirmed by other sources of cloud data, such as satellite records. In this paper, we analyse cloud data from two fully independent sources to characterize the cloud type climatology over Poland for the period 2003–2021: cloud optical thickness (COT) and cloud top pressure (CTP) parameters retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface (SYNOP) observations of cloud genera from the country's 27 ground‐based stations. The International Satellite Cloud Climatology Project (ISCCP) COT–CTP classification was used to define MODIS cloud types. We explore the hypothesis that MODIS data, classified using the ISCCP philosophy, confirms the changes in cloud type frequencies found in SYNOP observations. Our analysis is based on the application of the Spearman rank correlation test to mean monthly SYNOP and MODIS cloud type frequencies, and we compare per decade trends for two time series. Our hypothesis was confirmed, but only for Cirrus, Altostratus + Nimbostratus and Cumulus. We therefore conclude that the two analysed data sources are not fully consistent, and cannot substitute for each other in cloud type climatologies. Hence, at least for Poland, they should be treated as independent rather than complementary. We also demonstrate that the increase in high‐level cloud frequency over Poland, which has been observed by other authors who based their research on synoptic data, is not due to a decrease in the frequency of low‐ and mid‐level clouds. Our findings are confirmed by satellite records.

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Multi-sensor cloud retrieval simulator and remote sensing from model parameters – Part 1: Synthetic sensor radiance formulation

Cited by 16 publications

References 41 publications

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Global observations of aerosol-cloud-precipitation-climate interactions

Cloud type frequency over Poland (2003–2021) revealed by independent satellite‐based (MODIS) and surface‐based (SYNOP) observations

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