Statistical characteristics of cloud variability. Part 1: Retrieved cloud liquid water path at three ARM sites

Huang, Dong; Campos, Edwin; Liu, Yangang

doi:10.1002/2014jd022001

Cited by 9 publications

(12 citation statements)

References 41 publications

(63 reference statements)

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“…Among the three sites, the mean standard deviation at the TWP site is largest and that at the NSA site is smallest. Similar behaviors have been found for LWP in Part I [Huang et al, 2014]. Figure 2c reveals that the mean relative dispersion rises from a close to zero value (<0.1) to about 1.2 at the TWP site, 1.1 at the SGP site, and 0.8 at the NSA site when the window size increases from a few minutes to 2 days.…”

Section: Variability Of Retrieved Cloud Lwcsupporting

confidence: 79%

“…The companion paper has demonstrated the strong dependence of LWP subgrid variability on the averaging window size [ Huang et al , ]. This section examines the scale dependence of the subgrid microphysical and radiative effects.…”

Section: Scale Dependence Of the Subgrid Effectsmentioning

confidence: 99%

“…Among the three sites, the mean standard deviation at the TWP site is largest and that at the NSA site is smallest. Similar behaviors have been found for LWP in Part I [ Huang et al , ].…”

Section: Variability Of Retrieved Cloud Lwcmentioning

confidence: 99%

“…Part I of this paper examines the statistical characteristics of cloud Liquid Water Path (LWP) variability and its scale dependence using long‐term ground‐based observations [ Huang et al , ]. It is revealed that the lognormal and Gamma distributions are able to reasonably describe the observed relationship between statistical moments of cloud LWP.…”

Section: Introductionmentioning

confidence: 99%

“…Others also proposed to use a multivariate lognormal distribution to describe spatial subgrid variation of clouds [Golaz et al, 2002;Larson and Griffin, 2012]. Part I of this paper examines the statistical characteristics of cloud Liquid Water Path (LWP) variability and its scale dependence using long-term ground-based observations [Huang et al, 2014]. It is revealed that the lognormal and Gamma distributions are able to reasonably describe the observed relationship between statistical moments of cloud LWP.…”

Section: Introductionmentioning

confidence: 99%

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Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models

Huang

Liu

2014

J. Geophys. Res. Atmos.

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The effects of subgrid cloud variability on grid-average microphysical rates and radiative fluxes are examined by use of long-term retrieval products at the Tropical West Pacific, Southern Great Plains, and North Slope of Alaska sites of the Department of Energy's Atmospheric Radiation Measurement program. Four commonly used distribution functions, the truncated Gaussian, Gamma, lognormal, and Weibull distributions, are constrained to have the same mean and standard deviation as observed cloud liquid water content. The probability density functions are then used to upscale relevant physical processes to obtain grid-average process rates. It is found that the truncated Gaussian representation results in up to 30% mean bias in autoconversion rate, whereas the mean bias for the lognormal representation is about 10%. The Gamma and Weibull distribution function performs the best for the grid-average autoconversion rate with the mean relative bias less than 5%. For radiative fluxes, the lognormal and truncated Gaussian representations perform better than the Gamma and Weibull representations. The results show that the optimal choice of subgrid cloud distribution function depends on the nonlinearity of the process of interest, and thus, there is no single distribution function that works best for all parameterizations. Examination of the scale (window size) dependence of the mean bias indicates that the bias in grid-average process rates monotonically increases with increasing window sizes, suggesting the increasing importance of subgrid variability with increasing grid sizes.

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Section: Variability Of Retrieved Cloud Lwcsupporting

confidence: 79%

Section: Scale Dependence Of the Subgrid Effectsmentioning

confidence: 99%

“…Among the three sites, the mean standard deviation at the TWP site is largest and that at the NSA site is smallest. Similar behaviors have been found for LWP in Part I [ Huang et al , ].…”

Section: Variability Of Retrieved Cloud Lwcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models

Huang

Liu

2014

J. Geophys. Res. Atmos.

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Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Xie

Zhang

2015

JGR Atmospheres

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Using long-term radar-based ground measurements from the Atmospheric Radiation Measurement Program, we derive the inhomogeneity of cloud liquid water as represented by the shape parameter of a gamma distribution. The relationship between the inhomogeneity and the model grid size as well as atmospheric condition is presented. A larger grid scale and more unstable atmosphere are associated with larger inhomogeneity that is described by a smaller shape parameter. This relationship is implemented as a scale-aware parameterization of the liquid cloud inhomogeneity in the Community Earth System Model (CESM) in which the shape parameter impacts the cloud microphysical processes. When used in the default CESM1 with the finite-volume dynamic core where a constant liquid inhomogeneity parameter was assumed, it reduces the cloud inhomogeneity in high latitudes and increases it in low latitudes. This is due to both the smaller (larger) grid size in high (low) latitudes in the longitude-latitude grid setting of CESM and the more stable (unstable) atmosphere. The single-column model and general circulation model sensitivity experiments show that the new parameterization increases the cloud liquid water path in polar regions and decreases it in low latitudes. An advantage of the parameterization is that it can recognize the spatial resolutions of the CESM without special tuning of the cloud water inhomogeneity parameter.

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Application and Evaluation of an Explicit Prognostic Cloud‐Cover Scheme in GRAPES Global Forecast System

Liu

Zhao

et al. 2018

J Adv Model Earth Syst

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An explicit prognostic cloud‐cover scheme (PROGCS) is implemented into the Global/Regional Assimilation and Prediction System (GRAPES) for global middle‐range numerical weather predication system (GRAPES_GFS) to improve the model performance in simulating cloud cover and radiation. Unlike the previous diagnostic cloud‐cover scheme (DIAGCS), PROGCS considers the formation and dissipation of cloud cover by physically connecting it to the cumulus convection and large‐scale stratiform condensation processes. Our simulation results show that clouds in mid‐high latitudes arise mainly from large‐scale stratiform condensation processes, while cumulus convection and large‐scale condensation processes jointly determine cloud cover in low latitudes. Compared with DIAGCS, PROGCS captures more consistent vertical distributions of cloud cover with the observations from Atmospheric Radiation Measurements (ARM) program at the Southern Great Plains (SGP) site and simulates more realistic diurnal cycle of marine stratocumulus with the ERA‐Interim reanalysis data. The low, high, and total cloud covers that are determined via PROGCS appear to be more realistic than those simulated via DIAGCS when both are compared with satellite retrievals though the former maintains slight negative biases. In addition, the simulations of outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) from PROGCS runs have been considerably improved as well, resulting in less biases in radiative heating rates at heights below 850 hPa and above 400 hPa of GRAPES_GFS. Our results indicate that a prognostic method of cloud‐cover calculation has significant advantage over the conventional diagnostic one, and it should be adopted in both weather and climate simulation and forecast.

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Statistical characteristics of cloud variability. Part 1: Retrieved cloud liquid water path at three ARM sites

Cited by 9 publications

References 41 publications

Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models

Statistical characteristics of cloud variability. Part 2: Implication for parameterizations of microphysical and radiative transfer processes in climate models

Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Application and Evaluation of an Explicit Prognostic Cloud‐Cover Scheme in GRAPES Global Forecast System

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