Relationship between drizzle rate, liquid water path and droplet concentration at the scale of a stratocumulus cloud system

Geoffroy, Olivier; Brenguier, J. L.; Sandu, Irina

doi:10.5194/acp-8-4641-2008

Cited by 64 publications

(58 citation statements)

References 75 publications

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“…Shipway and Abel (2010). Previous aircraft closure studies suggest that the rain rate from stratocumulus shows a dependence on LW P α /N d where α ranges from 1.5 to 2 (Geoffroy et al, 2008). These sets of relations are largely consistent with idealised model simulations of warm rain production in stratocumulus clouds (Wood et al, 2009).…”

Section: Drizzle Efficiencysupporting

confidence: 73%

Evaluation of stratocumulus cloud prediction in the Met Office forecast model during VOCALS-REx

2010

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Abstract.Observations in the subtropical southeast Pacific obtained during the VOCALS-REx field experiment are used to evaluate the representation of stratocumulus cloud in the Met Office forecast model and to identify key areas where model biases exist. Marked variations in the large scale structure of the cloud field were observed during the experiment on both day-to-day and on diurnal timescales. In the remote maritime region the model is shown to have a good representation of synoptically induced variability in both cloud cover and marine boundary layer depth. Satellite observations show a strong diurnal cycle in cloud fraction and liquid water path in the stratocumulus with enhanced clearances of the cloud deck along the Chilean and Peruvian coasts on certain days. The model accurately simulates the phase of the diurnal cycle but is unable to capture the coastal clearing of cloud. Observations along the 20 • S latitude line show a gradual increase in the depth of the boundary layer away from the coast. This trend is well captured by the model (typical low bias of 200 m) although significant errors exist at the coast where the model marine boundary layer is too shallow and moist. Drizzle in the model responds to changes in liquid water path in a manner that is consistent with previous ship-borne observations in the region although the intensity of this drizzle is likely to be too high, particularly in the more polluted coastal region where higher cloud droplet number concentrations are typical. Another mode of variability in the cloud field that the model is unable to capture are regions of pockets of open cellular convection embedded in the overcast stratocumulus deck and an example of such a feature that was sampled during VOCALS-REx is shown.

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Section: Drizzle Efficiencysupporting

confidence: 73%

Evaluation of stratocumulus cloud prediction in the Met Office forecast model during VOCALS-REx

2010

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“…Finally, we note that the mean S R of the four h bins is 1.6, which is in the upper part of the range of β exponents (Eq. 2) found in previous studies (Geoffroy et al, 2008). We are, however, examining the relationship between ambient aerosol concentration and precipitation, while previous studies examined the relationship between the cloud droplet concentration and precipitation.…”

Section: Drizzle Susceptibilitymentioning

confidence: 51%

“…(1). The β values reported in previous field studies range from 1 to 1.75 (Geoffroy et al, 2008). Variations in β with cloud thickness, however, have not been reported in these studies.…”

Section: Introductionmentioning

confidence: 65%

“…Precipitation susceptibility estimates for marine stratocumulus can be obtained from previous field studies. Field studies of precipitating stratocumulus (e.g., Pawlowska and Brenguier, 2003;Comstock et al, 2004;vanZanten et al, 2005) identified that simple power law relationships display some skill in relating the cloud base precipitation rate R CB to cloud thickness h (or, alternatively, LWP) and to cloud droplet number concentration N d (Geoffroy et al, 2008). The relationships take the form…”

Section: Introductionmentioning

confidence: 99%

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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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“…The trees introduced in the simulation domain for the land surface scheme correspond to Atlantic coast broad leaved trees. The model includes a 2-moment bulk warm microphysical scheme (Khairoutdinov and Kogan, 2000;Geoffroy et al, 2008), that considers droplet concentration N c and mixing ratio r c as prognostic variables for the fog. An additional prognostic variable N ccn is used to account for already activated CCN, following the activation scheme of Cohard et al (2000c).…”

Section: Presentation Of the Modelmentioning

confidence: 99%

Large eddy simulation of radiation fog: impact of dynamics on the fog life cycle

et al. 2017

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Abstract. Large eddy simulations (LESs) of a radiation fog event occurring during the ParisFog experiment are studied with a view to analyse the impact of the dynamics of the boundary layer on the fog life cycle. The LES, performed with the Meso-NH model at 5 m resolution horizontally and 1 m vertically, and with a 2-moment microphysical scheme, includes the drag effect of a tree barrier and the deposition of droplets on vegetation. The model shows good agreement with measurements of near-surface dynamic and thermodynamic parameters and liquid water path. The blocking effect of the trees induces elevated fog formation, as actually observed, and horizontal heterogeneities during the formation. It also limits cooling and cloud water production. Deposition is found to exert the most significant impact on fog prediction as it not only erodes the fog near the surface but also modifies the fog life cycle and induces vertical heterogeneities. A comparison with the 2 m horizontal resolution simulation reveals small differences, meaning that grid convergence is achieved. Conversely, increasing numerical diffusion through a wind advection operator of lower order leads to an increase in the liquid water path and has a very similar effect to removing the tree barrier. This study allows us to establish the major dynamical ingredients needed to accurately represent the fog life cycle at very high-resolution.

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Relationship between drizzle rate, liquid water path and droplet concentration at the scale of a stratocumulus cloud system

Cited by 64 publications

References 75 publications

Evaluation of stratocumulus cloud prediction in the Met Office forecast model during VOCALS-REx

Evaluation of stratocumulus cloud prediction in the Met Office forecast model during VOCALS-REx

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

Large eddy simulation of radiation fog: impact of dynamics on the fog life cycle

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