scholarly journals Voronoi polyhedra probing of hydrated OH radical

RSC Advances ◽  
2014 ◽  
Vol 4 (79) ◽  
pp. 41812-41818 ◽  
Author(s):  
Lukasz Kazmierczak ◽  
Dorota Swiatla-Wojcik
Keyword(s):  
Liquid Water ◽  
3D Visualization ◽  
Probability Distributions ◽  
Oh Radical ◽  
Topological Properties ◽  
Voronoi Polyhedron ◽  
Voronoi Polyhedra ◽  
Polyhedron Method

Voronoi polyhedron method is employed to extract the smallest volume shared by ˙OH radical in liquid water at the biologically important temperature (37 °C). The 3D-visualization and the probability distributions of the metric and topological properties of ˙OH solvation cage are provided.

scholarly journals Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

2020 ◽  
Vol 20 (23) ◽  
pp. 14491-14507
Author(s):  
Hwayoung Jeoung ◽  
Guosheng Liu ◽  
Kwonil Kim ◽  
Gyuwon Lee ◽  
Eun-Kyoung Seo
Keyword(s):  
Liquid Water ◽  
Probability Distributions ◽  
A Priori ◽  
Winter Season ◽  
Retrieval Algorithm ◽  
Clear Correlation ◽  
Liquid Water Path ◽  
Near Surface ◽  
Water Path ◽  
Microphysical Properties

Abstract. Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

3D Visualization of Liquid Water Penetration from MPL to Substrate Layer in PEFC Using in-Situ X-Ray Tomographic Microscopy

2021 ◽  
Vol MA2021-02 (36) ◽  
pp. 1029-1029
Author(s):  
Takaya Sugahara ◽  
Takashi Sasabe ◽  
Hiroshi Naito ◽  
Manabu Kodama ◽  
Shuichiro Hirai
Keyword(s):  
Liquid Water ◽  
3D Visualization ◽  
Water Penetration ◽  
X Ray ◽  
Substrate Layer

scholarly journals Supercooled Liquid Water Detection Capabilities from Ka-Band Doppler Profiling Radars: Moment-Based Algorithm Formulation and Assessment

2021 ◽  
Vol 13 (15) ◽  
pp. 2891
Author(s):  
Petros Kalogeras ◽  
Alessandro Battaglia ◽  
Pavlos Kollias
Keyword(s):  
Liquid Water ◽  
Hydrological Cycle ◽  
Probability Distributions ◽  
Supercooled Liquid ◽  
Spectral Width ◽  
Radiative Properties ◽  
High Spectral Resolution ◽  
Ka Band ◽  
Phase Partition ◽  
Water Detection

The occurrence of supercooled liquid water in mixed-phase cloud (MPC) affects their cloud microphysical and radiative properties. The prevalence of MPCs in the mid- and high latitudes translates these effects to significant contributions to Earth’s radiative balance and hydrological cycle. The current study develops and assesses a radar-only, moment-based phase partition technique for the demarcation of supercooled liquid water volumes in arctic, MPC conditions. The study utilizes observations from the Ka band profiling radar, the collocated high spectral resolution lidar, and ambient temperature profiles from radio sounding deployments following a statistical analysis of 5.5 years of data (January 2014–May 2019) from the Atmospheric Radiation Measurement observatory at the North Slope of Alaska. The ice/liquid phase partition occurs via a per-pixel, neighborhood-dependent algorithm based on the premise that the partitioning can be deduced by examining the mean values of locally sampled probability distributions of radar-based observables and then compare those against the means of climatologically derived, per-phase probability distributions. Analyzed radar observables include linear depolarization ratio (LDR), spectral width, and vertical gradients of reflectivity factor and radial velocity corrected for vertical air motion. Results highlight that the optimal supercooled liquid water detection skill levels are realized for the radar variable combination of spectral width and reflectivity vertical gradient, suggesting that radar-based polarimetry, in the absence of full LDR spectra, is not as critical as Doppler capabilities. The cloud phase masking technique is proven particularly reliable when applied to cloud tops with an Equitable Threat Score (ETS) of 65%; the detection of embedded supercooled layers remains much more uncertain (ETS = 27%).

A Voronoi polyhedra analysis of structures of liquid water

1994 ◽  
Vol 100 (3) ◽  
pp. 2202-2212 ◽  
Author(s):  
Jing‐Ping Shih ◽  
Sheh‐Yi Sheu ◽  
Chung‐Yuan Mou
Keyword(s):  
Liquid Water ◽  
Voronoi Polyhedra

Random Finite Topologies and their Thresholds

2001 ◽  
Vol 10 (3) ◽  
pp. 239-249
Author(s):  
C. F. MACLEAN ◽  
NEIL O'CONNELL
Keyword(s):  
Probability Distributions ◽  
Focus Attention ◽  
Topological Properties ◽  
Integer Variable ◽  
Threshold Functions ◽  
Natural Family

For each integer n, there is a natural family of probability distributions on the set of topologies on a set of n elements, parametrized by an integer variable, m. We will describe how these are constructed and analysed, and find threshold functions (for m in terms of n) for various topological properties; we focus attention on connectivity and the size of the largest component.

scholarly journals Microphysical Properties of Three Types of Snow Clouds: Implication to Satellite Snowfall Retrievals

2020 ◽  
Author(s):  
Hwayoung Jeoung ◽  
Guosheng Liu ◽  
Kwonil Kim ◽  
Gyuwon Lee ◽  
Eun-Kyoung Seo
Keyword(s):  
Liquid Water ◽  
Land Surface ◽  
Probability Distributions ◽  
A Priori ◽  
Retrieval Algorithm ◽  
Clear Correlation ◽  
Liquid Water Path ◽  
Near Surface ◽  
Water Path ◽  
Microphysical Properties

Abstract. Ground-based radar and radiometer data observed during the 2017–18 winter were used to simultaneously estimate both cloud liquid water path and snowfall rate for three types of snowing clouds: near-surface, shallow and deep. Surveying all the observed data, it is found that near-surface cloud is the most frequently observed cloud type with an area fraction of over 60 %, while deep cloud contributes the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with vast majority hardly reaching to 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, liquid water path in the three types of clouds all has substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as a priori database. Under idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.33, 0.48 and 0.74, respectively, for near-surface, shallow and deep snowing clouds over land surface; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increased from 0.33 to 0.54, while virtually no change in skills is found for deep clouds and only marginal improvement is found for shallow clouds. This study provides a general picture of the microphysical characteristics of the different types of snowing clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

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