An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements

Saito, Masanori; Yang, Ping; Hu, Yongxiang; Liu, Xu; Loeb, Norman G.; Smith, William L.; Minnis, Patrick

doi:10.1029/2018jd029659

Cited by 16 publications

(17 citation statements)

References 44 publications

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“…One promising approach is to synergistically combine observations using active radar, which resolves a greater depth of cloud columns and senses the sixth moment of the SD, with passive measurements at microwave, infrared, or shortwave wavelengths, which are sensitive to lower moments of the SD but generally cannot resolve information vertically (e.g., Leinonen et al, 2016;C. Wang et al, 2019;Saito et al, 2019;Xu et al, 2019).…”

Section: Journal Of Advances In Modeling Earth Systemsmentioning

confidence: 99%

“…We note, however, that such measurements only provide information on microphysical quantities at or near cloud top, whereas vertically resolved information throughout the depth of the whole cloud layer is particularly useful for scheme evaluation. One promising approach is to synergistically combine observations using active radar, which resolves a greater depth of cloud columns and senses the sixth moment of the SD, with passive measurements at microwave, infrared, or shortwave wavelengths, which are sensitive to lower moments of the SD but generally cannot resolve information vertically (e.g., Leinonen et al, 2016; C. Wang et al, 2019; Saito et al, 2019; Xu et al, 2019).…”

Section: Possible Paths Forwardmentioning

confidence: 99%

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Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

Morrison

Lier-Walqui

Fridlind

et al. 2020

J Adv Model Earth Syst

247

125

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In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.

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Section: Journal Of Advances In Modeling Earth Systemsmentioning

confidence: 99%

Section: Possible Paths Forwardmentioning

confidence: 99%

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

Morrison

Lier-Walqui

Fridlind

et al. 2020

J Adv Model Earth Syst

247

125

View full text Add to dashboard Cite

show abstract

“…However, the outgoing solar radiation is influenced by the vertical variation of r eff (Zhang et al, 2010). Using combined MODIS and CloudSat measurements of global marine water clouds, Saito et al (2018) derived the first two leading empirical orthogonal functions (EOFs) of the droplet size profile in a 6-layer normalized τ space. The first two EOFs can explain 94% of the droplet size profile variability (Saito et al, 2018).…”

Section: Appendix A: Details In Setting Up the Ecrad Experimentsmentioning

confidence: 99%

“…Using combined MODIS and CloudSat measurements of global marine water clouds, Saito et al (2018) derived the first two leading empirical orthogonal functions (EOFs) of the droplet size profile in a 6-layer normalized τ space. The first two EOFs can explain 94% of the droplet size profile variability (Saito et al, 2018). Because we have not found reported anvil-like cloud r eff vertical distribution statistics in previous studies, we adopt the two EOFs in Saito et al (2018) to test the sensitivity of the ecRad calculations to the vertical variation of r eff .…”

Section: Appendix A: Details In Setting Up the Ecrad Experimentsmentioning

confidence: 99%

Performance of Cloud 3D Solvers in Ice Cloud Shortwave Radiation Closure Over the Equatorial Western Pacific Ocean

Ren

Yang

Wei

et al. 2022

J Adv Model Earth Syst

Self Cite

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“…This phenomenon could be caused by the onset of water cloud generation when updrafts dominate, causing adiabatic growth, and the reduction of in-cloud depth droplet size increases (Saito et al, 2019). On polluted days, the CER barely changed with a reduction in CTT.…”

Section: Aerosol Effect On Warm Cloud Propertiesmentioning

confidence: 99%

Aerosol impacts on warm-cloud microphysics and drizzle in a moderately polluted environment

Chen¹,

Wang²,

Min³

et al. 2020

Preprint

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Abstract. Climate is critically affected by aerosols, which can alter cloud lifecycles and precipitation distribution through radiative and microphysical effects. In this study, aerosol and cloud properties datasets from MODIS onboard Aqua satellite and surface observations, including aerosol concentrations, raindrop size distribution, and meteorological parameters, were used to statistically quantify the effects of aerosols on low-level warm cloud microphysics and drizzle over northern Taiwan during fall seasons (from October 15 to November 30 of 2005–2017). Results indicated that clouds in northwestern Taiwan, which with active human activity is dominated by low-level clouds (e.g. warm, thin, and broken clouds). The observed effects of aerosols on warm clouds indicated aerosol indirect effects; increasing aerosol loading caused a decrease in cloud effective radius (CER), an increase in cloud optical thickness, an increase in cloud fraction, and a decrease in cloud top temperature under a fixed cloud water path. A quantitative value of aerosol–cloud interactions (ACI = (δ ln⁡ CER)/(δ ln⁡ α), changes in CER depend on changes in aerosols) were calculated to be 0.07 for our research domain. ACI values varied between 0.09 and 0.06 in surrounding clean and heavily polluted areas, respectively, which indicated that aerosol indirect effects were more sensitive in the clean area. Analysis of raindrop size distribution observations during high aerosol loading resulted in a decreased frequency of drizzle events, redistributed cloud water to more numerous and smaller droplets, and reduced collision–coalescence rates. However, in the scenario of light precipitation (&leq; 1 mm h−1), high aerosol concentrations drive raindrops towards smaller droplet sizes and increase the appearance of drizzle drops. This study used long-term surface and satellite data to determine aerosol variations in northern Taiwan, effects on the clouds and precipitations, and applications to observational strategy planning for future research on aerosol–cloud–precipitation interactions.

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An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements

Cited by 16 publications

References 44 publications

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

Performance of Cloud 3D Solvers in Ice Cloud Shortwave Radiation Closure Over the Equatorial Western Pacific Ocean

Aerosol impacts on warm-cloud microphysics and drizzle in a moderately polluted environment

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