CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

Minnis, Patrick; Sun-Mack, S.; Chen, Yan; Khaiyer, Mandana M.; Yi, Yaling; Ayers, J. Kirk; Brown, R. R.; Dong, Xiquan; Gibson, Sharon; Heck, Patrick W.; Lin, Bing; Nordeen, Michele L.; Nguyen, Louis; Palikonda, Rabindra; Smith, William L.; Spangenberg, Douglas A.; Trepte, Qing Z.; Xi, Baike

doi:10.1109/tgrs.2011.2144602

Cited by 130 publications

(138 citation statements)

References 62 publications

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“…If CloudSat's columnar classification (Sassen and Wang, 2008) is cirrus, altostratus, altocumulus or deep convection, those cells in the column identified as cloud with temperatures < 273 K are classed as ice and r eff is set uniformly to the value inferred from MODIS data (Minnis et al, 2008(Minnis et al, , 2010a(Minnis et al, , 2010b. Warmer cells in the column are set to liquid with r eff = 10 µm.…”

Section: Merged A-train Datamentioning

confidence: 99%

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Barker

Jerg

Wehr

et al. 2011

Quart J Royal Meteoro Soc

144

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† The contribution of H. W. Barker to this article was prepared as part of his official duties as a Canadian government employee This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active-passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 .

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Section: Merged A-train Datamentioning

confidence: 99%

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Barker

Jerg

Wehr

et al. 2011

Quart J Royal Meteoro Soc

144

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“…Radiances from the Tenth Geostationary Operational Environmental Satellite (GOES-10) were used to retrieve cloud properties at 4-km resolution with the methods of Minnis et al (2011b) properties were also retrieved from 1-km Terra MODerateresolution Imaging Spectroradiometer (MODIS) data using the same algorithms applied to the GOES-10 data (Minnis et al, 2011a). In addition, the Quick Scatterometer (QuikSCAT) L3 data provides 0.25 • × 0.25 • resolution surface wind twice per day.…”

Section: Reanalysis Data Satellite Measurements and Radiosonde Datamentioning

confidence: 99%

Observations of the boundary layer, cloud, and aerosol variability in the southeast Pacific near-coastal marine stratocumulus during VOCALS-REx

et al. 2011

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Abstract. Aircraft observations made off the coast of northern Chile in the Southeastern Pacific (20 • S, 72 • W; named Point Alpha) from 16 October to 13 November 2008 during the VAMOS Ocean-Cloud-Atmosphere-Land StudyRegional Experiment (VOCALS-REx), combined with meteorological reanalysis, satellite measurements, and radiosonde data, are used to investigate the boundary layer (BL) and aerosol-cloud-drizzle variations in this region. On days without predominately synoptic and meso-scale influences, the BL at Point Alpha was typical of a non-drizzling stratocumulus-topped BL. Entrainment rates calculated from the near cloud-top fluxes and turbulence in the BL at Point Alpha appeared to be weaker than those in the BL over the open ocean west of Point Alpha and the BL near the coast of the northeast Pacific. The cloud liquid water path (LWP) varied between 15 g m −2 and 160 g m −2 . The BL had a depth of 1140 ± 120 m, was generally well-mixed and capped by a sharp inversion without predominately synoptic and mesoscale influences. The wind direction generally switched from southerly within the BL to northerly above the inversion. On days when a synoptic system and related mesoscale costal circulations affected conditions at Point Alpha (29 OctoberCorrespondence to: X. Zheng (xzheng@rsmas.miami.edu) 4 November), a moist layer above the inversion moved over Point Alpha, and the total-water mixing ratio above the inversion was larger than that within the BL.The accumulation mode aerosol varied from 250 to 700 cm −3 within the BL, and CCN at 0.2 % supersaturation within the BL ranged between 150 and 550 cm −3 . The main aerosol source at Point Alpha was horizontal advection within the BL from south. The average cloud droplet number concentration ranged between 80 and 400 cm −3 . While the mean LWP retrieved from GOES was in good agreement with the in situ measurements, the GOES-derived cloud droplet effective radius tended to be larger than that from the aircraft in situ observations near cloud top. The aerosol and cloud LWP relationship reveals that during the typical wellmixed BL days the cloud LWP increased with the CCN concentrations. On the other hand, meteorological factors and the decoupling processes have large influences on the cloud LWP variation as well.

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“…But because retrieval algorithms based on these sensors include radiative-equivalent assumptions particular to the infrared part of the spectrum, the retrieved cloud properties do not always agree with results obtained from other types of sensors: they are most sensitive to optically thin clouds near the instrument (i.e., high clouds for satellite-based sensors and low clouds for surface-based sensors), typically are layer-averaged through all cloud columns, and generally are more sensitive to liquid than ice, primarily due to how differences in the geometries of ice and liquid hydrometeors affect their infrared radiance (e.g., Garrett and Zhao, 2006). Numerous intercomparison studies report systematic differences between cloud properties retrieved from infrared sensors and results from other sensors (e.g., Shupe et al, 2008;Dong et al, 2008;Karlsson and Dybbroe, 2010;Liu et al, 2010;Minnis et al, 2011;Vogelmann et al, 2012;Zhao et al, 2012;Chan and Comiso, 2013;Jin and Nasiri, 2014).…”

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confidence: 99%

“…Thus, alternative evaluation approaches (e.g., Bugliaro et al, 2011;Pincus et al, 2012) are needed to complement conventional techniques to better constrain biases, facilitate algorithm development, and advance interpretation of results. Simulated data sets, though idealized, reduce the number of sources of uncertainty, thereby permitting a more detailed evaluation of many aspects of individual methodologies and measurement sensitivities.…”

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confidence: 99%

A synthetic data set of high-spectral-resolution infrared spectra for the Arctic atmosphere

Cox

Rowe

Neshyba

et al. 2016

Earth Syst. Sci. Data

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Abstract. Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectralresolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface-and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325 • N, 156.615 • W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm −1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of ∼ 0.01 cm −1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).

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CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part II: Examples of Average Results and Comparisons With Other Data

Cited by 130 publications

References 62 publications

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Observations of the boundary layer, cloud, and aerosol variability in the southeast Pacific near-coastal marine stratocumulus during VOCALS-REx

A synthetic data set of high-spectral-resolution infrared spectra for the Arctic atmosphere

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