Variability of Optical Depth and Effective Radius in Marine Stratocumulus Clouds

Szczodrak, M.; Austin, Philip H.; Krummel, Paul B.

doi:10.1175/1520-0469(2001)058<2912:voodae>2.0.co;2

Cited by 134 publications

(146 citation statements)

References 31 publications

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“…Figure 4 demonstrates the three signal calibrations with a relative root mean square error (RMSE) of 0.155, 0.148, and 0.144 for the red, green, and blue channel respectively. This RMSE is within the range of the radiance variability expected in overcast clouds (Szczodrak et al, 2001). Field calibration to modeled SHDOM data was preferred here as lab calibrations of sky imagers are rarely available.…”

Section: Usi Hardware and Calibration Of The Signal To Radiancementioning

confidence: 52%

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

et al. 2016

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Abstract. A method for retrieving cloud optical depth (τ c ) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red-blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red-blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ 0 ), τ c , solar pixel angle/scattering angle (ϑ s ), and pixel zenith angle/view angle (ϑ z ). The effects of these parameters are described and the functions for radiance, I λ (τ c , θ 0 , ϑ s , ϑ z ), and RBR(τ c , θ 0 , ϑ s , ϑ z ) are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c , where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I meas λ (ϑ s , ϑ z ), in addition to RBR meas (ϑ s , ϑ z ), to obtain a unique solution for τ c . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003)

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Section: Usi Hardware and Calibration Of The Signal To Radiancementioning

confidence: 52%

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

et al. 2016

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“…For a vertically homogeneous cloud, a = 2/3 (Bennartz, 2007), and a = 5/9 when the adiabatic assumption is applied (Szczodrak et al, 2001). A recent study by Miller et al (2016) provides a systematic investigation of the impacts of cloud vertical structure on MODIS LWP retrievals.…”

Section: Lwpmentioning

confidence: 99%

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

et al. 2018

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Abstract. Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the CloudAerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility S POP , precipitation intensity susceptibility S I , and precipitation rate susceptibility S R in warm marine clouds. We find that S POP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a −15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that S POP derived with respect to aerosol index (AI) is about one-third of S POP derived with respect to cloud droplet number concentration (CDNC). Overall, S POP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of S I and S R are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosolcloud-precipitation interactions in observations and models.

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“…A way to circumvent this problem is to estimate the cloud droplet number concentration (N CD ), since it directly links cloud optical and microphysical properties to the aerosol concentration at cloud base. Several methods have been developed for this purpose, each one requiring different assumptions about the sub-adiabatic character of and the mixing that occurs inside clouds (Bennartz, 2007;Boers et al, 2006;Szczodrak et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Estimating seasonal variations in cloud droplet number concentration over the boreal forest from satellite observations

et al. 2011

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Abstract. Seasonal variations in cloud droplet number concentration (N CD ) in low-level stratiform clouds over the boreal forest are estimated from MODIS observations of cloud optical and microphysical properties, using a sub-adiabatic cloud model to interpret vertical profiles of cloud properties. An uncertainty analysis of the cloud model is included to reveal the main sensitivities of the cloud model. We compared the seasonal cycle in N CD , obtained using 9 yr of satellite data, to surface concentrations of potential cloud activating aerosols, measured at the SMEAR II station at Hyytiälä in Finland. The results show that N CD and cloud condensation nuclei (CCN) concentrations have no clear correlation at seasonal time scale. The fraction of aerosols that actually activate as cloud droplet decreases sharply with increasing aerosol concentrations. Furthermore, information on the stability of the atmosphere shows that low N CD is linked to stable atmospheric conditions. Combining these findings leads to the conclusion that cloud droplet activation for the studied clouds over the boreal forest is limited by convection. Our results suggest that it is important to take the strength of convection into account when studying the influence of aerosols from the boreal forest on cloud formation, although they do not rule out the possibility that aerosols from the boreal forest affect other types of clouds with a closer coupling to the surface.

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Variability of Optical Depth and Effective Radius in Marine Stratocumulus Clouds

Cited by 134 publications

References 31 publications

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

Estimating seasonal variations in cloud droplet number concentration over the boreal forest from satellite observations

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