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  • 1
    Online Resource
    Online Resource
    An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin
    Copernicus GmbH ; 2021
    In:  Atmospheric Chemistry and Physics Vol. 21, No. 3 ( 2021-02-04), p. 1507-1563
    Details
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 21, No. 3 ( 2021-02-04), p. 1507-1563
    Abstract: Abstract. Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ∼350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ∼100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    URL: Article
    URL: Article
    DOI: 10.5194/acp-21-1507-2021
    DOI: 10.5194/acp-21-1507-2021-corrigendum
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
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  • 2
    Online Resource
    Online Resource
    Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street
    American Geophysical Union (AGU) ; 2023
    In:  Reviews of Geophysics Vol. 61, No. 2 ( 2023-06)
    Details
    In: Reviews of Geophysics, American Geophysical Union (AGU), Vol. 61, No. 2 ( 2023-06)
    Abstract: Aerosol climate forcing uncertainty is virtually undiminished despite two decades of advances in many aspects of aerosol‐climate science This review concludes that current and planned aerosol modeling, satellite and ground‐based observation programs remain essential New, systematic aircraft aerosol‐particle and cloud‐process measurements are also needed, along with better model‐measurement integration
    Type of Medium: Online Resource
    ISSN: 8755-1209 , 1944-9208
    URL: Article
    DOI: 10.1029/2022RG000796
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2023
    detail.hit.zdb_id: 2035391-1
    detail.hit.zdb_id: 209852-0
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    SSG: 16,13
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  • 3
    Online Resource
    Online Resource
    Assimilation of SSM/I-Derived Surface Rainfall and Total Precipitable Water for Improving the GEOS Analysis for Climate Studies
    American Meteorological Society ; 2000
    In:  Monthly Weather Review Vol. 128, No. 3 ( 2000-03), p. 509-537
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 128, No. 3 ( 2000-03), p. 509-537
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/1520-0493(2000)128〈0509:AOSIDS〉2.0.CO;2
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2000
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  • 4
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    The GOddard SnoW Impurity Module (GOSWIM) for the NASA GEOS-5 Earth System Model: Preliminary Comparisons with Observations in Sapporo, Japan
    Meteorological Society of Japan ; 2014
    In:  SOLA Vol. 10, No. 0 ( 2014), p. 50-56
    Details
    In: SOLA, Meteorological Society of Japan, Vol. 10, No. 0 ( 2014), p. 50-56
    Type of Medium: Online Resource
    ISSN: 1349-6476
    URL: Article
    DOI: 10.2151/sola.2014-011
    Language: English
    Publisher: Meteorological Society of Japan
    Publication Date: 2014
    detail.hit.zdb_id: 2222926-7
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  • 5
    Online Resource
    Online Resource
    Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS
    Copernicus GmbH ; 2022
    In:  Geoscientific Model Development Vol. 15, No. 1 ( 2022-01-04), p. 1-14
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 15, No. 1 ( 2022-01-04), p. 1-14
    Abstract: 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) were 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. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. 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 were 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∘) 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 an ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with an offset of −0.007 and RMSE of 0.097 at a pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-15-1-2022
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    Monte Carlo Bayesian inference on a statistical model of sub‐gridcolumn moisture variability using high‐resolution cloud observations. Part 2: Sensitivity tests and results
    Wiley ; 2016
    In:  Quarterly Journal of the Royal Meteorological Society Vol. 142, No. 699 ( 2016-07), p. 2528-2540
    Details
    In: Quarterly Journal of the Royal Meteorological Society, Wiley, Vol. 142, No. 699 ( 2016-07), p. 2528-2540
    Abstract: Part 1 of this series presented a Monte Carlo Bayesian method for constraining a complex statistical model of global circulation model (GCM) sub‐gridcolumn moisture variability using high‐resolution Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data, thereby permitting parameter estimation and cloud data assimilation for large‐scale models. This article performs some basic testing of this new approach, verifying that it does indeed reduce mean and standard deviation biases significantly with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud‐top pressure and that it also improves the simulated rotational–Raman scattering cloud optical centroid pressure (OCP) against independent (non‐assimilated) retrievals from the Ozone Monitoring Instrument (OMI). Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows non‐gradient‐based jumps into regions of non‐zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast, where the background state has a clear swath. This article also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost‐effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in passive‐radiometer‐retrieved cloud observables on cloud vertical structure, beyond cloud‐top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow‐dependent correlation modification from Riishojgaard provides some help in this respect, by better honouring inversion structures in the background state.
    Type of Medium: Online Resource
    ISSN: 0035-9009 , 1477-870X
    URL: Issue
    URL: Article
    DOI: 10.1002/qj.2016.142.issue-699
    DOI: 10.1002/qj.2844
    RVK:
    UA 1000
    RVK:
    UA 7650
    Language: English
    Publisher: Wiley
    Publication Date: 2016
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  • 7
    Online Resource
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    Assimilation of Satellite Cloud Data into the GMAO Finite-Volume Data Assimilation System Using a Parameter Estimation Method. Part I: Motivation and Algorithm Description
    American Meteorological Society ; 2007
    In:  Journal of the Atmospheric Sciences Vol. 64, No. 11 ( 2007-11-01), p. 3880-3895
    Details
    In: Journal of the Atmospheric Sciences, American Meteorological Society, Vol. 64, No. 11 ( 2007-11-01), p. 3880-3895
    Abstract: General circulation models are unable to resolve subgrid-scale moisture variability and associated cloudiness and so must parameterize grid-scale cloud properties. This typically involves various empirical assumptions and a failure to capture the full range (synoptic, geographic, diurnal) of the subgrid-scale variability. A variational parameter estimation technique is employed to adjust empirical model cloud parameters in both space and time, in order to better represent assimilated International Satellite Cloud Climatology Project (ISCCP) cloud fraction and optical depth and Special Sensor Microwave Imager (SSM/I) liquid water path. The value of these adjustments is verified by much improved cloud radiative forcing and persistent improvement in cloud fraction forecasts.
    Type of Medium: Online Resource
    ISSN: 1520-0469 , 0022-4928
    URL: Article
    DOI: 10.1175/2006JAS2046.1
    RVK:
    UA 4800
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2007
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  • 8
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    Online Resource
    Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols
    Copernicus GmbH ; 2016
    In:  Geoscientific Model Development Vol. 9, No. 7 ( 2016-07-12), p. 2377-2389
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 9, No. 7 ( 2016-07-12), p. 2377-2389
    Abstract: 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 (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.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 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.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-9-2377-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
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  • 9
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    Online Resource
    Corrigendum: “The GOddard SnoW Impurity Module (GOSWIM) for the NASA GEOS-5 Earth System Model: Preliminary Comparisons with Observations in Sapporo, Japan”
    Meteorological Society of Japan ; 2016
    In:  SOLA Vol. 12, No. 0 ( 2016), p. c1-c1
    Details
    In: SOLA, Meteorological Society of Japan, Vol. 12, No. 0 ( 2016), p. c1-c1
    Type of Medium: Online Resource
    ISSN: 1349-6476
    URL: Article
    DOI: 10.2151/sola.2016-014
    Language: English
    Publisher: Meteorological Society of Japan
    Publication Date: 2016
    detail.hit.zdb_id: 2222926-7
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  • 10
    Online Resource
    Online Resource
    Monte Carlo Bayesian inference on a statistical model of sub‐gridcolumn moisture variability using high‐resolution cloud observations. Part 1: Method
    Wiley ; 2016
    In:  Quarterly Journal of the Royal Meteorological Society Vol. 142, No. 699 ( 2016-07), p. 2505-2527
    Details
    In: Quarterly Journal of the Royal Meteorological Society, Wiley, Vol. 142, No. 699 ( 2016-07), p. 2505-2527
    Abstract: A method is presented to constrain a statistical model of sub‐gridcolumn moisture variability using high‐resolution satellite cloud data. The method can be used for large‐scale model parameter estimation or cloud data assimilation. The gridcolumn model includes assumed probability density function (PDF) intra‐layer horizontal variability and a copula‐based inter‐layer correlation model. The observables used in the current study are Moderate Resolution Imaging Spectroradiometer (MODIS) cloud‐top pressure, brightness temperature and cloud optical thickness, but the method should be extensible to direct cloudy radiance assimilation for a small number of channels. The algorithm is a form of Bayesian inference with a Markov chain Monte Carlo (MCMC) approach to characterizing the posterior distribution. This approach is especially useful in cases where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach is not gradient‐based and allows jumps into regions of non‐zero cloud probability. The current study uses a skewed‐triangle distribution for layer moisture. The article also includes a discussion of the Metropolis and multiple‐try Metropolis versions of MCMC.
    Type of Medium: Online Resource
    ISSN: 0035-9009 , 1477-870X
    URL: Issue
    URL: Article
    DOI: 10.1002/qj.2016.142.issue-699
    DOI: 10.1002/qj.2843
    RVK:
    UA 1000
    RVK:
    UA 7650
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 3142-2
    detail.hit.zdb_id: 2089168-4
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