Cloud fraction errors for trade wind cumuli from EOS‐Terra instruments

Zhao, Guangyu; Girolamo, Larry Di

doi:10.1029/2006gl027088

Cited by 94 publications

(117 citation statements)

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“…But in some number of cases it is not possible to identify a combination of phase, particle size, and optical thickness consistent with the observed reflectances at multiple wavelengths. This may occur because the pixel has been mis-classified as cloudy, as is common in regions of sun-glint (Zhao and Girolamo, 2006). In the Collection 5 retrievals used here, pixels adjacent to clear sky are also rejected, as these are subject to retrieval biases.…”

Section: Appendix a Measuring Cloud Fractionmentioning

confidence: 99%

On the transitions in marine boundary layer cloudiness

2010

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Abstract. Satellite observations and meteorological reanalysis are used to examine the transition from unbroken sheets of stratocumulus to fields of scattered cumulus, and the processes controlling them, in four subtropical oceans. A Lagrangian analysis suggests that both the transition, defined as the temporal evolution in cloudiness, and the processes driving the transition, are quite similar among the subtropical oceans. The increase in sea surface temperature and the associated decrease in lower tropospheric stability appear to play a far more important role in cloud evolution than other factors including changes in large scale divergence and upper tropospheric humidity. During the summer months, the transitions in marine boundary layer cloudiness appear so systematically that their characteristics obtained by documenting the flow of thousands of individual air masses are well reproduced by the mean (or climatological) fields of the different data sets. This highlights interesting opportunities for future observational and modeling studies of these transitions.

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Section: Appendix a Measuring Cloud Fractionmentioning

confidence: 99%

On the transitions in marine boundary layer cloudiness

2010

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“…As above, large-scale conditions from ERA-40 determine the ISCCP sample of RICO-like conditions during the period of overlap between the data sets (1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001). Because of the method used to define cloud fraction, it is likely the ISCCP data set overestimates cloud fraction in this regime (Wielicki and Parker 1992;Di Girolamo and Davies 1997;Zhao and Di Girolamo 2006). As discussed above, the diagnosed cloud amounts in GCMs can differ from results using an ISCCP simulator, which was not available for this analysis for three of the GCMs, but this regime is not expected to contain much obscuring high-level cloud, so differences should be minimal.…”

Section: Zoom In On Rico Conditionsmentioning

confidence: 99%

Revealing differences in GCM representations of low clouds

2009

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Aquaplanet configurations of four atmospheric general circulation models (GCMs) are compared with standard, Earth-like configurations and observations. The focus here is on tropical, low-level clouds, which have been identified as important for estimates of climate sensitivity. Investigating the distribution of the monthly mean vertical velocity and lower-tropospheric stability, the aquaplanets are seen to capture the core of the distribution of the more Earth-like configurations, whose distributions are, in turn, similar to that of reanalysis data. By conditionally sampling over these distributions, low-cloud regimes are defined, separating shallow cumulus convection from stratocumulus. Within each regime, the GCMs produce similar large-scale environments, yet disparate depictions of the clouds. Aquaplanets lack stratocumulus because of their zonally symmetric boundary conditions, but produce extensive trade-wind regions populated by shallow cumulus clouds just like those in the Earth-like setting. The analysis shows that aquaplanets can be compared with observations, just as well as the Earth-like configuration, with the added ability to focus on particular regimes without complications from geographical or temporal biases.

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“…For this reason, the highest resolution available from ASTER (15 m) is targeted, while the need for an operationally complete and globally validated cloud detection algorithm is not required at this time. Still, the manually tedious effort to produce scene-by-scene cloud masks for a multitude of different resolutions based on a single (or more) threshold approach (e.g., Welch, 1986, Zhao andDi Girolamo, 2006) is replaced in favor of a hybrid approach. Here, individual cases are selected based on the presence of low-level water clouds over the ocean and the absence of high-level cirrus that impacts the cloud F. Werner et al: ASTER cloud property retrievals 5873 property retrieval (Wind et al, 2010).…”

Section: Cloud Detection For Astermentioning

confidence: 99%

“…All other pixels are identified as clear (cloudiness flag "3"). These thresholds, which comprise the first step in the new cloud masking scheme, were set through inspection of 210 ASTER MBL scenes sampled off the coast of California and the tropical western Atlantic (Zhao and Di Girolamo, 2006) between April 2003 and July 2007. These observations have been performed at full ASTER resolution, keeping in mind that we are siding on a cloud-conservative cloud mask.…”

Section: Cloud Detection For Astermentioning

confidence: 99%

“…This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTERspecific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in Zhao and Di Girolamo (2006). To validate and evaluate the cloud optical thickness (τ ) and cloud effective radius (r eff ) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS.…”

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confidence: 99%

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Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

et al. 2016

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Abstract.A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTERspecific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in Zhao and Di Girolamo (2006). To validate and evaluate the cloud optical thickness (τ ) and cloud effective radius (r eff ) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS. Subsequently, τ aA and r eff, aA retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R > 0.970. However, for partially cloudy pixels there are significant differences between r eff, aA and the MODIS results which can exceed 10 µm. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

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Cloud fraction errors for trade wind cumuli from EOS‐Terra instruments

Cited by 94 publications

References 10 publications

On the transitions in marine boundary layer cloudiness

On the transitions in marine boundary layer cloudiness

Revealing differences in GCM representations of low clouds

Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

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