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

Wind, G.; Silva, Arlindo da; Norris, P. M.; Platnick, S.; Mattoo, S.; Levy, Robert C.

doi:10.5194/gmd-9-2377-2016

Cited by 9 publications

(13 citation statements)

References 34 publications

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“…What is lacking are statistical tools that combine the spatial, temporal, and field (or parameter) variability in one diagram. Whilst AERONET sites are well distributed about the globe, there remain many locations without monitored data where it is impossible to determine if the aerosol retrieval has made reasonable choices, either for pixel selection, cloud screening, aerosol model type or surface reflectance assumptions (Wind et al, 2016). If the spatiotemporal variability at monitoring sites is poorly defined this is amplified when aerosol model uncertainty must be included in the assessment of the overall accuracy of the predicted GLCs.…”

Section: Validation/accuracymentioning

confidence: 99%

Working with the enemy? Social work education and men who use intimate partner violence

Jury

Boxall

2018

Social Work Education

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The accurate measurements of natural and anthropogenic aerosol particulate matter (PM) is important in managing both environmental and health risks; however, limited monitoring in regional areas hinders accurate quantification. This article provides an overview of the ability of recently launched geostationary earth orbit (GEO) satellites, such as GOES-R (North America) and HIMAWARI (Asia and Oceania), to provide near real-time ground-level PM concentrations (GLCs). The review examines the literature relating to the spatial and temporal resolution required by air quality studies, the removal of cloud and surface effects, the aerosol inversion problem, and the computation of ground-level concentrations rather than columnar aerosol optical depth (AOD). Determining surface PM concentrations using remote sensing is complicated by differentiating intrinsic aerosol properties (size, shape, composition, and quantity) from extrinsic signal intensities, particularly as the number of unknown intrinsic parameters exceeds the number of known extrinsic measurements. The review confirms that development of GEO satellite products has led to improvements in the use of coupled products such as GEOS-CHEM, aerosol types have consolidated on model species rather than prior descriptive classifications, and forward radiative transfer models have led to a better understanding of predictive spectra interdependencies across different aerosol types, despite fewer wavelength bands. However, it is apparent that the aerosol inversion problem remains challenging because there are limited wavelength bands for characterising localised mineralogy. The review finds that the frequency of GEO satellite data exceeds the temporal resolution required for air quality studies, but the spatial resolution is too coarse for localised air quality studies. Continual monitoring necessitates using the less sensitive thermal infra-red bands, which also reduce surface absorption effects. However, given the challenges of the aerosol inversion problem and difficulties in converting columnar AOD to surface concentrations, the review identifies coupled GEO-neural networks as potentially the most viable option for improving quantification.

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Section: Validation/accuracymentioning

confidence: 99%

Working with the enemy? Social work education and men who use intimate partner violence

Jury

Boxall

2018

Social Work Education

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“…In this study we applied an Observing System Simulation Experiment (OSSE) framework 98 to gain insight on the performance of the MOD06ACAERO algorithm. Rather than using the 99 classic analysis/forecast error metric common in Numerical Weather Prediction OSSE studies (e.g., Hoffman and Atlas 2016) we adopt here a "Retrieval OSSE" perspective where the quality of the retrieval is used as the verification metric (Wind et al 2013(Wind et al , 2016. A radiative transfer code is applied to the model quantities combined with sensor geometry to simulate how a model scene appears to a specific instrument.…”

Section: Introductionmentioning

confidence: 99%

“…The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) is a tool that combines model output with a radiative transfer code in order to simulate radiances that may be measured by a remote sensing instrument if it were passing over the model fields (Wind et al, 2013(Wind et al, , 2016. In this paper, MCARS continues to use the combination of the GEOS-5 model, correlated-k models of atmospheric transmittance due to various gaseous absorbers for https://doi.org/10.5194/gmd-2021-17 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

Wind

Silva

Meyer

et al. 2021

Preprint

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Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE = 0.107. When only near-nadir pixels were considered (view zenith angle within +/−20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset −0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

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“…There has been considerable progress in the last 15 years in characterizing the global column-integrated AOD both from ground-based and space-based remote sensing platforms (e.g., King et al, 1999;Chin et al, 2009). Information on the aerosol vertical profile has also become available in recent years (Welton et al, 2000;Campbell et al, 2003;Winker et al, 2010;McGill et al, 2015), albeit with lesser spatial coverage owing to the active sensor techniques required (i.e., single-beam profiling from ground-based or orbiting lidars). Determination of aerosol-phase function is not generally available from remote sensing platforms, although there is some information possible from multi-angle sensors such as the Multi-angle Imaging Spectroradiometer (MISR; Diner et al, 1998) and the potential for more as multi-angular polarimeters are being developed for future missions (e.g., NASA ACE Science Working Group, 2016, and the European Space Agency 3MI instrument manifested for launch on METOP SG-A in mid-2021).…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Ozone Monitoring Instrument Aerosol Index using the NASA Goddard Earth Observing System Aerosol Reanalysis Products

Colarco¹,

Gassó²,

Ahn³

et al. 2017

Preprint

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Abstract. We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI aerosol retrieval algorithms, and its retrieved AI (OMAERUV AI) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600 hPa and 1013.25 hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.

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

Cited by 9 publications

References 34 publications

Working with the enemy? Social work education and men who use intimate partner violence

Working with the enemy? Social work education and men who use intimate partner violence

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

Simulation of the Ozone Monitoring Instrument Aerosol Index using the NASA Goddard Earth Observing System Aerosol Reanalysis Products

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