Mixed-Phase Clouds: Progress and Challenges

Korolev, Alexei; McFarquhar, Greg M.; Field, Paul R.; Franklin, Charmaine; Lawson, Paul A.; Wang, Z.; Williams, Earle; Abel, Steven J.; Axisa, Duncan; Borrmann, Stephan; Crosier, Jonathan; Fugal, Jacob P.; Krämer, Martina; Lohmann, Ulrike; Schlenczek, Oliver; Schnaiter, M.; Wendisch, Manfred

doi:10.1175/amsmonographs-d-17-0001.1

Cited by 244 publications

(303 citation statements)

References 310 publications

(358 reference statements)

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“…The temperature of glaciation of a cloud depends on different parameters, such as cloud altitudes, surface types, cloud droplet sizes, or pollution concentration (Carro-Calvo et al, 2016;Coopman et al, 2018;Rangno & Hobbs, 2001;Rosenfeld et al, 2011;Zamora et al, 2018). Clouds can therefore be composed of either only cloud droplets-referred to as liquid clouds-or only ice crystals-referred to as ice clouds-or a combination of supercooled liquid droplets and ice crystals-referred to as mixed-phase clouds (Korolev et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…However, the properties driving their development remain poorly understood (Shupe et al, 2008). For example, mixed-phase clouds are not well represented in global climate models (McCoy et al, 2016), their interactions with aerosols, their radiative (Hansen et al, 1997) and dynamical (Boers & Mitchell, 1994) effects, and their role in cloud electrification (Korolev et al, 2017) are still undetermined.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

2019

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Between −37 and 0 °C, clouds are liquid, ice, or mixed phase. Nearly all retrieval algorithms for passive instruments provide binary phase information—ice or liquid—making it difficult to retrieve mixed‐phase cloud properties. Based on measurements from the geostationary space‐based instrument Spinning Enhanced Visible and InfraRed Imager (SEVIRI), we show that the retrieved ice crystal effective radius is smaller than the liquid droplet effective radius for 48% of 230 analyzed cloud thermodynamic phase transitions—phase transition from liquid to ice of rising convective clouds—while ice crystals are expected to be larger than cloud droplets. We simulate mixed‐phase cloud radiances with the numerical model Santa Barbara DISORT Atmospheric Radiative Transfer for which we compare simulated effective radius retrievals with observations. The phase retrieval algorithm from SEVIRI does not represent well mixed‐phase clouds, and categorizing clouds by only ice and liquid is not enough to accurately represent mixed‐phase cloud optical properties. We conclude that the mixed‐phase nature of clouds explains that retrieved cloud droplet radii are larger than ice crystal radii directly before and after the phase transition. However, from a cloud tracking algorithm perspective, the variation of the effective radius enables the detection of mixed‐phase convective clouds from binary phase information.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

2019

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“…The temperature range over which 0 < β < 1 assumes that mixed‐phased clouds exist only at temperatures between 0 and −40 °C (e.g., Korolev et al, ), and the assumed linear variation between these ranges is a simple but common formulation (e.g., Khairoutdinov & Randall, ; Tao et al, ). The squared Brunt‐Väisälä frequency in CM1r19 is calculated as

N^{2} = ({, \begin{matrix} array N_{m}^{2} = β N_{m, w}^{2} + (1 - β) N_{m, i}^{2} & array if q_{n} \geq q_{s} \\ array N_{d}^{2} & array otherwise \end{matrix}),

where

N_{m, i}^{2}

N_{m}^{2}

calculated with respect to ice.…”

Section: Methodsmentioning

confidence: 99%

Key Elements of Turbulence Closures for Simulating Deep Convection at Kilometer‐Scale Resolution

Shi

Chow

Street

et al. 2019

J Adv Model Earth Syst

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Kilometer‐scale grid spacing is increasingly being used in regional numerical weather prediction and climate simulation. This resolution range is in the terra incognita, where energetic eddies are partially resolved and turbulence parameterization is a challenge. The Smagorinsky and turbulence kinetic energy 1.5‐order models are commonly used at this resolution range, but, as traditional eddy‐diffusivity models, they can only represent forward‐scattering turbulence (downgradient fluxes), whereas the dynamic reconstruction model (DRM), based on explicit filtering, permits countergradient fluxes. Here we perform large‐eddy simulation of deep convection with 100‐m horizontal grid spacing and use these results to evaluate the performance of turbulence schemes at 1‐km horizontal resolution. The Smagorinsky and turbulence kinetic energy 1.5 schemes produce large‐amplitude errors at 1‐km resolution, due to excessively large eddy diffusivities attributable to the formulation of the squared moist Brunt‐Väisälä frequency ( Nm2). With this formulation in cloudy regions, eddy diffusivity can be excessively increased in “unstable” regions, which produce downward (downgradient) heat flux in a conditionally unstable environment leading to destabilization and further amplification of eddy diffusivities. A more appropriate criterion based on saturation mixing ratio helps eliminate this problem. However, shallow clouds cannot be simulated well in any case at 1‐km resolution with the traditional models, whereas DRM allows for countergradient heat flux for both shallow and deep convection and predicts the distribution of clouds and fluxes satisfactorily. This is because DRM employs an eddy diffusivity model that is dynamically adjusted and a reconstruction approach that allows countergradient fluxes.

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“…The topic of charge separation and storm electrification is a continued area of research interest (Korolev et al 2017;Stough et al 2017). It is known that when larger ice crystals (>30 µm) fall in stagnant air that is not electrified, they fall with their major axis horizontal (Zikmunda and Vali 1972;Foster and Hallett 2002).…”

Section: Storm Electrification Revealed By Polarimetric Variablesmentioning

confidence: 99%

S-Pol’s Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior)

Hubbert

Wilson

Weckwerth

et al. 2018

Bulletin of the American Meteorological Society

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The National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

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Mixed-Phase Clouds: Progress and Challenges

Cited by 244 publications

References 310 publications

Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

Detection of Mixed‐Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI

Key Elements of Turbulence Closures for Simulating Deep Convection at Kilometer‐Scale Resolution

S-Pol’s Polarimetric Data Reveal Detailed Storm Features (and Insect Behavior)

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