Surface‐based remote sensing of the observed and the Adiabatic liquid water content of stratocumulus clouds

Albrecht, Bruce A.; Fairall, C. W.; Thomson, Dennis W.; White, Allen B.; Snider, J. B.; Schubert, Wayne H.

doi:10.1029/gl017i001p00089

Cited by 156 publications

(163 citation statements)

References 7 publications

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“…Thus the maximum LWC in the retrieved cloud field is 30-40% of the adiabatic value. This fraction is consistent with many of the in-situ and remote sensing observations of marine low-level clouds (Albrecht et al, 1990;Miller et al, 1998). The spatial characteristics of the retrieved clouds around 30, 110, 140 and 160 km are consistent with those in the radar image (Fig.…”

Section: Retrieval Using Only the Psr Datasupporting

confidence: 74%

Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment

2010

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Abstract. Tomographic methods offer great potential for retrieving three-dimensional spatial distributions of cloud liquid water from radiometric observations by passive microwave sensors. Fixed tomographic systems require multiple radiometers, while mobile systems can use just a single radiometer. Part 1 (this paper) examines the results from a limited cloud tomography trial with a single-radiometer airborne system carried out as part of the 2003 AMSR-E validation campaign over Wakasa Bay of the Sea of Japan. During this trial, the Polarimetric Scanning Radiometer (PSR) and Microwave Imaging Radiometer (MIR) aboard the NASA P-3 research aircraft provided a useful dataset for testing the cloud tomography method over a system of low-level clouds. We do tomographic retrievals with a constrained inversion algorithm using three configurations: PSR, MIR, and combined PSR and MIR data. The liquid water paths from the PSR retrieval are consistent with those from the MIR retrieval. The retrieved cloud field based on the combined data appears to be physically plausible and consistent with the cloud image obtained by a cloud radar. We find that some vertically-uniform clouds appear at high altitudes in the retrieved field where the radar shows clear sky. This is likely due to the sub-optimal data collection strategy. This sets the stage for Part 2 of this study that aims to define optimal data collection strategies using observation system simulation experiments.

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Section: Retrieval Using Only the Psr Datasupporting

confidence: 74%

Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment

2010

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“…However, in the VO-CALS dataset we have estimates of both cloud thickness and LWP. Cloud thickness is typically well correlated with cloud LWP for marine stratocumulus, where the assumption of adiabatic LWP is a good approximation (Albrecht et al, 1990;Zuidema et al, 2005Zuidema et al, , 2012. In some areas where there is scud (cumulus humulis under stratocumulus), the assumption of adiabatic LWP breaks down.…”

Section: Susceptibility When Binned By Cloud Liquid Water Pathmentioning

confidence: 99%

“…This assumption is typically valid in marine stratocumulus (Albrecht et al, 1990;Zuidema et al, 2005Zuidema et al, , 2012, and the approximation is especially good for the thinner, non-precipitating clouds for which the WCR is unable to detect a cloud top height (Zuidema et al, 2005). We estimate the cloud thickness h values from the cloud LWP values using the expression…”

Section: Susceptibility With Integrated Cloud Thickness Datamentioning

confidence: 99%

Does precipitation susceptibility vary with increasing cloud thickness in marine stratocumulus?

et al. 2012

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Abstract.The relationship between precipitation rate and accumulation mode aerosol concentration in marine stratocumulus-topped boundary layers is investigated by applying the precipitation susceptibility metric to aircraft data obtained during the VOCALS Regional Experiment. A new method to calculate the precipitation susceptibility that incorporates non-precipitating clouds is introduced. The mean precipitation rate R over a segment of the data is expressed as the product of a drizzle fraction f and a drizzle intensity I (mean rate for drizzling columns). The susceptibility S x is then defined as the fractional decrease in precipitation variable x={R,f ,I } per fractional increase in the concentration of aerosols with dry diameter >0.1 µm, with cloud thickness h held fixed. The precipitation susceptibility S R is calculated using data from both precipitating and nonprecipitating cloudy columns to quantify how aerosol concentrations affect the mean precipitation rate of all clouds of a given h range and not just the mean precipitation of clouds that are precipitating. S R systematically decreases with increasing h, and this is largely because S f decreases with h while S I is approximately independent of h. In a general sense, S f can be thought of as the effect of aerosols on the probability of precipitation, while S I can be thought of as the effect of aerosols on the intensity of precipitation. Since thicker clouds are likely to precipitate regardless of ambient aerosol concentration, we expect S f to decrease with increasing h. The results are broadly insensitive to the choice of horizontal averaging scale. Similar susceptibilities are found for both cloud base and near-surface drizzle rates. The analysis is repeated with cloud liquid water path held fixed instead of cloud thickness. Simple power law relationships relating precipitation rate to aerosol concentration or cloud droplet concentration do not capture this observed behavior.

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“…c is the rate of increase of liquid water content (q L ) with height (dq L /dz, with units kgm −4 ) and is referred to as the "condensation rate" in B07, or the "water content lapse rate" in Painemal and Zuidema (2011, hereafter PZ11). Albrecht et al (1990) and Ahmad et al (2013) give two alternative derivations of this quantity. c depends more strongly on the temperature (T ) than on the pressure (P ).…”

Section: Atmosmentioning

confidence: 99%

The effect of solar zenith angle on MODIS cloud optical and microphysical retrievals within marine liquid water clouds

2014

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Abstract. In this paper we use a novel observational approach to investigate MODIS satellite retrieval biases of τ and r e (using three different MODIS bands: 1.6, 2.1 and 3.7 µm, denoted as r e1.6 , r e2.1 and r e3.7 , respectively) that occur at high solar zenith angles (θ 0 ) and how they affect retrievals of cloud droplet concentration (N d ). Utilizing the large number of overpasses for polar regions and the diurnal variation of θ 0 we estimate biases in the above quantities for an open ocean region that is dominated by low level stratiform clouds.We find that the mean τ is fairly constant between θ 0 = 50 • and ∼65-70 • , but then increases rapidly with an increase of over 70 % between the lowest and highest θ 0 . The r e2.1 and r e3.7 decrease with θ 0 , with effects also starting at around θ 0 =65-70 • . At low θ 0 , the r e values from the three different MODIS bands agree to within around 0.2 µm, whereas at high θ 0 the spread is closer to 1 µm. The percentage changes of r e with θ 0 are considerably lower than those for τ , being around 5 % and 7 % for r e2.1 and r e3.7 . For r e1.6 there was very little change with θ 0 . Evidence is provided that these changes are unlikely to be due to any physical diurnal cycle.The increase in τ and decrease in r e both contribute to an overall increase in N d of 40-70 % between low and high θ 0 . Whilst the overall r e changes are quite small, they are not insignificant for the calculation of N d ; we find that the contributions to N d biases from the τ and r e biases were roughly comparable for r e3.7 , although for the other r e bands the τ changes were considerably more important. Also, when considering only the clouds with the more heterogeneous tops, the importance of the r e biases was considerably enhanced for both r e2.1 and r e3.7 .When using the variability of 1 km resolution τ data (γ τ ) as a heterogeneity parameter we obtained the expected result of increasing differences in τ between high and low θ 0 as heterogeneity increased, which was not the case when using the variability of 5 km resolution cloud top temperature (σ CTT ), suggesting that γ τ is a better predictor of τ biases at high θ 0 than σ CTT . For a given θ 0 , large decreases in r e were observed as the cloud top heterogeneity changed from low to high values, although it is possible that physical changes to the clouds associated with cloud heterogeneity variation may account for some of this. However, for a given cloud top heterogeneity we find that the value of θ 0 affects the sign and magnitude of the relative differences between r e1.6 , r e2.1 and r e3.7 , which has implications for attempts to retrieve vertical cloud information using the different MODIS bands. The relatively larger decrease in r e3.7 and the lack of change of r e1.6 with both θ 0 and cloud top heterogeneity suggest that r e3.7 is more prone to retrieval biases due to high θ 0 than the other bands. We discuss some possible reasons for this.Our results have important implications for individual MODIS swaths at high θ 0...

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Surface‐based remote sensing of the observed and the Adiabatic liquid water content of stratocumulus clouds

Cited by 156 publications

References 7 publications

Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment

Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment

Does precipitation susceptibility vary with increasing cloud thickness in marine stratocumulus?

The effect of solar zenith angle on MODIS cloud optical and microphysical retrievals within marine liquid water clouds

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