Introduction Ice Fog, Ice Clouds, and Remote Sensing

Gültepe, Ismail; Heymsfield, Andrew J.

doi:10.1007/s00024-016-1380-2

Cited by 13 publications

(19 citation statements)

References 37 publications

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“…The average O ic in April and June is about 20 %, whereas in winter (December-February) it is no more than 10 %. The average O ic in the 4 years is 14 %, which is lower than the ice cloud coverage of 24 % reported by Hahn and Warren (2007) based on satellite measurements over North China. This might be associated with the observation location and the field of view (FOV) of the KPDR.…”

Section: Monthly and Hourly Occurrence Frequencycontrasting

confidence: 57%

“…Cirrus clouds consist solely of ice crystals. Their occurrence frequency exhibits latitudinal variability ranging from 50 % in the equatorial regions of Africa to 7 % in the polar regions (Stubenrauch et al, 2006;Hahn and Warren, 2007;Sassen et al, 2008Sassen et al, , 2009. Ice clouds cover over 50 % of the globe's surface (Hong and Liu, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017

Huo

Tian

et al. 2020

Atmos. Chem. Phys.

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Abstract. The physical properties and radiative role of ice clouds remain one of the uncertainties in the Earth–atmosphere system. In this study, we present a detailed analysis of ice cloud properties based on 4 years of surface millimeter-wavelength radar measurements in Beijing, China, where the summer monsoon from the ocean and the winter monsoon from the continent prevail alternately, resulting in various ice clouds. More than 6300 ice cloud clusters were studied to quantify the properties of ice clouds, such as the height, optical depth and horizontal extent, which can serve as a reference for parameterization and characterization in global climate models. In addition, comparison between ice cloud clusters formed under the summer monsoon and the winter monsoon indicates the different formation and evolution mechanisms of cirrus clouds. Statistically, temperatures of more than 95 % of ice radar bins are below −15 ∘C and more than 80 % of ice clouds are above 7 km. The dependence of the radar reflectivity of ice particles on height and temperature was also observed in this study, indicating that the reflectivity of ice bins increases (decreases) as the temperature (height) increases. In addition, it is found that there is a strong linear relationship between the mean reflectivity and the ice cloud depth. Due to various synoptic circumstances, the ice clouds in summer are warmer, higher and thicker, with larger reflectivity than that in winter; in particular, the mean cloud-top height of ice clouds in summer is 2.2 km higher than that in winter. Our analysis indicates that in spring, in situ-origin cirrus clouds are more common than liquid-origin cirrus clouds, while in summer liquid-origin cirrus clouds are more frequent; in autumn and winter, most cirrus clouds are of in situ origin.

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Section: Monthly and Hourly Occurrence Frequencycontrasting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017

Huo

Tian

et al. 2020

Atmos. Chem. Phys.

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“…In the CLOUD submodel, a single updraught velocity (w) is used for the whole grid cell, although w can vary strongly in reality within the cell horizontal dimension (e.g. Guo et al, 2008). This is a simplification which is commonly used by GCMs; nevertheless, important progress has been recently achieved on this front to describe the subgridscale variability in updraught velocity using high-resolution simulations (Barahona et al, 2017).…”

Section: Default Ice Nucleation In Emacmentioning

confidence: 99%

Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53)

et al. 2018

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A comprehensive ice nucleation parameterization has been implemented in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations (ICNCs). The parameterization of Barahona and Nenes (2009, hereafter BN09) allows for the treatment of ice nucleation taking into account the competition for water vapour between homogeneous and heterogeneous nucleation in cirrus clouds. Furthermore, the influence of chemically heterogeneous, polydisperse aerosols is considered by applying one of the multiple ice nucleating particle parameterizations which are included in BN09 to compute the heterogeneously formed ice crystals. BN09 has been modified in order to consider the pre-existing ice crystal effect and implemented to operate both in the cirrus and in the mixedphase regimes. Compared to the standard EMAC parameterizations, BN09 produces fewer ice crystals in the upper troposphere but higher ICNCs in the middle troposphere, especially in the Northern Hemisphere where ice nucleating mineral dust particles are relatively abundant. Overall, IC-NCs agree well with the observations, especially in cold cirrus clouds (at temperatures below 205 K), although they are underestimated between 200 and 220 K. As BN09 takes into account processes which were previously neglected by the standard version of the model, it is recommended for future EMAC simulations.

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“…According to Gultepe et al (2014), ice fog is also related to frost formation, which could lead to deposition of ice on aircraft, runways, and infrastructure such as roads and power lines. Gultepe et al (2019) and Gultepe and Heymsfield (2016) suggest that accidents related to weather and low-visibility conditions over the Arctic regions could increase tenfold because of increasing air traffic. In addition, the large number and characteristics of ice particles during ice fog events and their impact on the surface energy balance is not well understood.…”

Section: Figures Andmentioning

confidence: 99%

“…The challenge, given the remaining need to fully understand extreme cold weather events and their impact on civilian and military activities, is in part due to limited surface in situ and remote-sensing observations (Gultepe et al 2015;Gultepe and Heymsfield 2016). Specifically, a need exists to have high-resolution spatiotemporal observations of environmental factors associated with ice fog.…”

Section: Figures Andmentioning

confidence: 99%

Ice fog monitoring near Fairbanks, AK

Vas¹,

Peckham²,

Schmitt³

et al. 2021

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Ice fog events, which occur during the Arctic winter, result in greatly decreased visibility and can lead to an increase of ice on roadways, aircraft, and airfields. The Fairbanks area is known for ice fog conditions, and previous studies have shown these events to be associated with moisture released from local power generation. Despite the identified originating mechanism of ice fog, there remains a need to quantify the environmental conditions controlling its origination, intensity, and spatial extent. This investigation focused on developing innovative methods of identifying and characterizing the environmental conditions that lead to ice fog formation near Fort Wainwright, Alaska. Preliminary data collected from December 2019 to March 2020 suggest that ice fog events occurred with temperatures below −34°C, up to 74% of the time ice fog emanated from the power generation facility, and at least 95% of ice particles during ice fog events were solid droxtals with diameters ranging from 7 to 50 μm. This report documents the need for frequent and detailed observations of the meteorological conditions in combination with photographic and ice particle observations. Datasets from these observations capture the environmental complexity and the impacts from energy generation in extremely cold weather conditions.

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Introduction Ice Fog, Ice Clouds, and Remote Sensing

Cited by 13 publications

References 37 publications

Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017

Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017

Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53)

Ice fog monitoring near Fairbanks, AK

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