Готові списки джерел за темами / Mixed-Phase cloud

Добірка наукової літератури з теми "Mixed-Phase cloud"

Автор: Grafiati
Опубліковано: 7 липня 2024
Оновлено: 7 липня 2024

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Статті в журналах з теми "Mixed-Phase cloud":

1

Zuidema, P., B. Baker, Y. Han, J. Intrieri, J. Key, P. Lawson, S. Matrosov, M. Shupe, R. Stone, and T. Uttal. "An Arctic Springtime Mixed-Phase Cloudy Boundary Layer Observed during SHEBA." Journal of the Atmospheric Sciences 62, no. 1 (January 1, 2005): 160–76. http://dx.doi.org/10.1175/jas-3368.1.

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Abstract The microphysical characteristics, radiative impact, and life cycle of a long-lived, surface-based mixed-layer, mixed-phase cloud with an average temperature of approximately −20°C are presented and discussed. The cloud was observed during the Surface Heat Budget of the Arctic experiment (SHEBA) from 1 to 10 May 1998. Vertically resolved properties of the liquid and ice phases are retrieved using surface-based remote sensors, utilize the adiabatic assumption for the liquid component, and are aided by and validated with aircraft measurements from 4 and 7 May. The cloud radar ice microphysical retrievals, originally developed for all-ice clouds, compare well with aircraft measurements despite the presence of much greater liquid water contents than ice water contents. The retrieved time-mean liquid cloud optical depth of 10.1 ± 7.8 far surpasses the mean ice cloud optical depth of 0.2, so that the liquid phase is primarily responsible for the cloud’s radiative (flux) impact. The ice phase, in turn, regulates the overall cloud optical depth through two mechanisms: sedimentation from a thin upper ice cloud, and a local ice production mechanism with a time scale of a few hours, thought to reflect a preferred freezing of the larger liquid drops. The liquid water paths replenish within half a day or less after their uptake by ice, attesting to strong water vapor fluxes. Deeper boundary layer depths and higher cloud optical depths coincide with large-scale rising motion at 850 hPa, but the synoptic activity is also associated with upper-level ice clouds. Interestingly, the local ice formation mechanism appears to be more active when the large-scale subsidence rate implies increased cloud-top entrainment. Strong cloud-top radiative cooling rates promote cloud longevity when the cloud is optically thick. The radiative impact of the cloud upon the surface is significant: a time-mean positive net cloud forcing of 41 W m−2 with a diurnal amplitude of ∼20 W m−2. This is primarily because a high surface reflectance (0.86) reduces the solar cooling influence. The net cloud forcing is primarily sensitive to cloud optical depth for the low-optical-depth cloudy columns and to the surface reflectance for the high-optical-depth cloudy columns. Any projected increase in the springtime cloud optical depth at this location (76°N, 165°W) is not expected to significantly alter the surface radiation budget, because clouds were almost always present, and almost 60% of the cloudy columns had optical depths >6.
2

Zhu, Shizhen, Ling Qian, Xueqian Ma, Yujun Qiu, Jing Yang, Xin He, Junjun Li, Lei Zhu, Jing Gong, and Chunsong Lu. "Impact of Aerosols on the Macrophysical and Microphysical Characteristics of Ice-Phase and Mixed-Phase Clouds over the Tibetan Plateau." Remote Sensing 16, no. 10 (May 17, 2024): 1781. http://dx.doi.org/10.3390/rs16101781.

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Using CloudSat/CALIPSO satellite data and ERA5 reanalysis data from 2006 to 2010, the effects of aerosols on ice- and mixed-phase, single-layer, non-precipitating clouds over the Tibetan Plateau during nighttime in the MAM (March to May), JJA (June to August), SON (September to November), and DJF (December to February) seasons were examined. The results indicated the following: (1) The macrophysical and microphysical characteristics of ice- and mixed-phase clouds exhibit a nonlinear trend with increasing aerosol optical depth (AOD). When the logarithm of AOD (lnAOD) was ≤−4.0, with increasing AOD during MAM and JJA nights, the cloud thickness and ice particle effective radius of ice-phase clouds and mixed-phase clouds, the ice water path and ice particle number concentration of ice-phase clouds, and the liquid water path and cloud fraction of mixed-phase clouds all decreased; during SON and DJF nights, the cloud thickness of ice-phase clouds, cloud top height, liquid droplet number concentration, and liquid water path of mixed-phase clouds all decreased. When the lnAOD was > −4.0, with increasing AOD during MAM and JJA nights, the cloud top height, cloud base height, cloud fraction, and ice particle number concentration of ice-phase clouds, and the ice water path of mixed-phase clouds all increased; during SON and DJF nights, the cloud fraction of mixed-phase clouds and the ice water path of ice-phase clouds all increased. (2) Under the condition of excluding meteorological factors, including the U-component of wind, V-component of wind, pressure vertical velocity, temperature, and relative humidity at the atmospheric pressure heights near the average cloud top height, within the cloud, and the average cloud base height, as well as precipitable water vapor, convective available potential energy, and surface pressure. During MAM and JJA nights. When the lnAOD was ≤ −4.0, an increase in aerosols may have led to a decrease in the thickness of ice and mixed-phase cloud layers, as well as a reduction in cloud water path values. In contrast, when the lnAOD was > −4.0, an increase in aerosols may contribute to elevated cloud base and cloud top heights for ice-phase clouds. During SON and DJF nights, changes in various cloud characteristics may be influenced by both aerosols and meteorological factors.
3

Solomon, Amy, Gijs de Boer, Jessie M. Creamean, Allison McComiskey, Matthew D. Shupe, Maximilian Maahn, and Christopher Cox. "The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds." Atmospheric Chemistry and Physics 18, no. 23 (December 3, 2018): 17047–59. http://dx.doi.org/10.5194/acp-18-17047-2018.

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Abstract. This study investigates the interactions between cloud dynamics and aerosols in idealized large-eddy simulations (LES) of Arctic mixed-phase stratocumulus clouds (AMPS) observed at Oliktok Point, Alaska, in April 2015. This case was chosen because it allows the cloud to form in response to radiative cooling starting from a cloud-free state, rather than requiring the cloud ice and liquid to adjust to an initial cloudy state. Sensitivity studies are used to identify whether there are buffering feedbacks that limit the impact of aerosol perturbations. The results of this study indicate that perturbations in ice nucleating particles (INPs) dominate over cloud condensation nuclei (CCN) perturbations; i.e., an equivalent fractional decrease in CCN and INPs results in an increase in the cloud-top longwave cooling rate, even though the droplet effective radius increases and the cloud emissivity decreases. The dominant effect of ice in the simulated mixed-phase cloud is a thinning rather than a glaciation, causing the mixed-phase clouds to radiate as a grey body and the radiative properties of the cloud to be more sensitive to aerosol perturbations. It is demonstrated that allowing prognostic CCN and INPs causes a layering of the aerosols, with increased concentrations of CCN above cloud top and increased concentrations of INPs at the base of the cloud-driven mixed layer. This layering contributes to the maintenance of the cloud liquid, which drives the dynamics of the cloud system.
4

Shupe, Matthew D., Pavlos Kollias, P. Ola G. Persson, and Greg M. McFarquhar. "Vertical Motions in Arctic Mixed-Phase Stratiform Clouds." Journal of the Atmospheric Sciences 65, no. 4 (April 1, 2008): 1304–22. http://dx.doi.org/10.1175/2007jas2479.1.

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Abstract The characteristics of Arctic mixed-phase stratiform clouds and their relation to vertical air motions are examined using ground-based observations during the Mixed-Phase Arctic Cloud Experiment (MPACE) in Barrow, Alaska, during fall 2004. The cloud macrophysical, microphysical, and dynamical properties are derived from a suite of active and passive remote sensors. Low-level, single-layer, mixed-phase stratiform clouds are typically topped by a 400–700-m-deep liquid water layer from which ice crystals precipitate. These clouds are strongly dominated (85% by mass) by liquid water. On average, an in-cloud updraft of 0.4 m s−1 sustains the clouds, although cloud-scale circulations lead to a variability of up to ±2 m s−1 from the average. Dominant scales-of-variability in both vertical air motions and cloud microphysical properties retrieved by this analysis occur at 0.5–10-km wavelengths. In updrafts, both cloud liquid and ice mass grow, although the net liquid mass growth is usually largest. Between updrafts, nearly all ice falls out and/or sublimates while the cloud liquid diminishes but does not completely evaporate. The persistence of liquid water throughout these cloud cycles suggests that ice-forming nuclei, and thus ice crystal, concentrations must be limited and that water vapor is plentiful. These details are brought together within the context of a conceptual model relating cloud-scale dynamics and microphysics.
5

Carey, Lawrence D., Jianguo Niu, Ping Yang, J. Adam Kankiewicz, Vincent E. Larson, and Thomas H. Vonder Haar. "The Vertical Profile of Liquid and Ice Water Content in Midlatitude Mixed-Phase Altocumulus Clouds." Journal of Applied Meteorology and Climatology 47, no. 9 (September 1, 2008): 2487–95. http://dx.doi.org/10.1175/2008jamc1885.1.

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Abstract The microphysical properties of mixed-phase altocumulus clouds are investigated using in situ airborne measurements acquired during the ninth Cloud Layer Experiment (CLEX-9) over a midlatitude location. Approximately ⅔ of the sampled profiles are supercooled liquid–topped altocumulus clouds characterized by mixed-phase conditions. The coexistence of measurable liquid water droplets and ice crystals begins at or within tens of meters of cloud top and extends down to cloud base. Ice virga is found below cloud base. Peak liquid water contents occur at or near cloud top while peak ice water contents occur in the lower half of the cloud or in virga. The estimation of ice water content from particle size data requires that an assumption be made regarding the particle mass–dimensional relation, resulting in potential error on the order of tens of percent. The highest proportion of liquid is typically found in the coldest (top) part of the cloud profile. This feature of the microphysical structure for the midlatitude mixed-phase altocumulus clouds is similar to that reported for mixed-phase clouds over the Arctic region. The results obtained for limited cases of midlatitude mixed-phase clouds observed during CLEX-9 may have an implication for the study of mixed-phase cloud microphysics, satellite remote sensing applications, and the parameterization of mixed-phase cloud radiative properties in climate models.
6

Turner, D. D. "Arctic Mixed-Phase Cloud Properties from AERI Lidar Observations: Algorithm and Results from SHEBA." Journal of Applied Meteorology 44, no. 4 (April 1, 2005): 427–44. http://dx.doi.org/10.1175/jam2208.1.

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Abstract A new approach to retrieve microphysical properties from mixed-phase Arctic clouds is presented. This mixed-phase cloud property retrieval algorithm (MIXCRA) retrieves cloud optical depth, ice fraction, and the effective radius of the water and ice particles from ground-based, high-resolution infrared radiance and lidar cloud boundary observations. The theoretical basis for this technique is that the absorption coefficient of ice is greater than that of liquid water from 10 to 13 μm, whereas liquid water is more absorbing than ice from 16 to 25 μm. MIXCRA retrievals are only valid for optically thin (τvisible < 6) single-layer clouds when the precipitable water vapor is less than 1 cm. MIXCRA was applied to the Atmospheric Emitted Radiance Interferometer (AERI) data that were collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment from November 1997 to May 1998, where 63% of all of the cloudy scenes above the SHEBA site met this specification. The retrieval determined that approximately 48% of these clouds were mixed phase and that a significant number of clouds (during all 7 months) contained liquid water, even for cloud temperatures as low as 240 K. The retrieved distributions of effective radii for water and ice particles in single-phase clouds are shown to be different than the effective radii in mixed-phase clouds.
7

Cho, Hyoun-Myoung, Shaima L. Nasiri, and Ping Yang. "Application of CALIOP Measurements to the Evaluation of Cloud Phase Derived from MODIS Infrared Channels." Journal of Applied Meteorology and Climatology 48, no. 10 (October 1, 2009): 2169–80. http://dx.doi.org/10.1175/2009jamc2238.1.

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Abstract In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) infrared-based cloud thermodynamic phase retrievals are evaluated using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals for the 6 months from January to June of 2008. The CALIOP 5-km cloud-layer product provides information on cloud opacity, cloud-top height, midlayer cloud temperature, and cloud thermodynamic phase. Comparisons are made between MODIS IR phase and CALIOP observations for single-layer clouds (54% of the cloudy CALIOP scenes) and for the top layer of the CALIOP scenes. Both CALIOP and MODIS retrieve larger fractions of water clouds in the single-layer cases than in the top-layer cases, demonstrating that focusing on only single-layer clouds may introduce a water-cloud bias. Of the single-layer clouds, 60% are transparent and 40% are opaque (defined by the lack of a CALIOP ground return). MODIS tends to classify single-layer clouds with midlayer temperatures below −40°C as ice; around −30°C nearly equally as ice, mixed, and unknown; between −28° and −15°C as mixed; and above 0°C as water. Ninety-five percent of the single-layer CALIOP clouds not detected by MODIS are transparent. Approximately ⅓ of transparent single-layer clouds with temperatures below −30°C are not detected by MODIS and close to another ⅓ are classified as ice, with the rest assigned as water, mixed, or unknown. CALIOP classes nearly all of these transparent cold clouds as ice.
8

Wang, Jing, Bida Jian, Guoyin Wang, Yuxin Zhao, Yarong Li, Husi Letu, Min Zhang, and Jiming Li. "Climatology of Cloud Phase, Cloud Radiative Effects and Precipitation Properties over the Tibetan Plateau." Remote Sensing 13, no. 3 (January 21, 2021): 363. http://dx.doi.org/10.3390/rs13030363.

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Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January 2007–December 2010) cloud phase (2B-CLDCLASS-LIDAR), radiative fluxes (2B-FLXHR-LIDAR), and precipitation (2C-PRECIP-COLUMN) products from CloudSat, this study systematically quantifies the radiative contribution of cloud phases and precipitation over the TP. Statistical results indicate that the ice cloud frequently occurs during the cold season, while mixed-phase cloud fraction is more frequent during the warm season. In addition, liquid clouds exhibit a weak seasonal variation, and the relative cloud fraction is very low, but supercooled water cloud has a larger cloud distribution (the value reaches about 0.24) than those of warm water clouds in the eastern part of the TP during the warm season. Within the atmosphere, the ice cloud has the largest radiative contribution during the cold season, the mixed-phase cloud is the second most important cloud phase for the cloud radiative contribution during the warm season, and supercooled water clouds’ contribution is particularly important during the cold season. In particular, the precipitation frequency over the TP is mainly dominated by the ice and mixed-phase clouds and is larger over the southeastern part of the TP during the warm season.
9

Shupe, Matthew D., Sergey Y. Matrosov, and Taneil Uttal. "Arctic Mixed-Phase Cloud Properties Derived from Surface-Based Sensors at SHEBA." Journal of the Atmospheric Sciences 63, no. 2 (February 1, 2006): 697–711. http://dx.doi.org/10.1175/jas3659.1.

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Abstract Arctic mixed-phase cloud macro- and microphysical properties are derived from a year of radar, lidar, microwave radiometer, and radiosonde observations made as part of the Surface Heat Budget of the Arctic Ocean (SHEBA) Program in the Beaufort Sea in 1997–98. Mixed-phase clouds occurred 41% of the time and were most frequent in the spring and fall transition seasons. These clouds often consisted of a shallow, cloud-top liquid layer from which ice particles formed and fell, although deep, multilayered mixed-phase cloud scenes were also observed. On average, individual cloud layers persisted for 12 h, while some mixed-phase cloud systems lasted for many days. Ninety percent of the observed mixed-phase clouds were 0.5–3 km thick, had a cloud base of 0–2 km, and resided at a temperature of −25° to −5°C. Under the assumption that the relatively large ice crystals dominate the radar signal, ice properties were retrieved from these clouds using radar reflectivity measurements. The annual average ice particle mean diameter, ice water content, and ice water path were 93 μm, 0.027 g m−3, and 42 g m−2, respectively. These values are all larger than those found in single-phase ice clouds at SHEBA. Vertically resolved cloud liquid properties were not retrieved; however, the annual average, microwave radiometer–derived liquid water path (LWP) in mixed-phase clouds was 61 g m−2. This value is larger than the average LWP observed in single-phase liquid clouds because the liquid water layers in the mixed-phase clouds tended to be thicker than those in all-liquid clouds. Although mixed-phase clouds were observed down to temperatures of about −40°C, the liquid fraction (ratio of LWP to total condensed water path) increased on average from zero at −24°C to one at −14°C. The observations show a range of ∼25°C at any given liquid fraction and a phase transition relationship that may change moderately with season.
10

Luo, Qing, Bingqi Yi, and Lei Bi. "Sensitivity of Mixed-Phase Cloud Optical Properties to Cloud Particle Model and Microphysical Factors at Wavelengths from 0.2 to 100 µm." Remote Sensing 13, no. 12 (June 14, 2021): 2330. http://dx.doi.org/10.3390/rs13122330.

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The representation of mixed-phase cloud optical properties in models is a critical problem in cloud modeling studies. Ice and liquid water co-existing in a cloud layer result in significantly different cloud optical properties from those of liquid water and ice clouds. However, it is not clear as to how mixed-phase cloud optical properties are affected by various microphysical factors, including the effective particle size, ice volume fraction, and ice particle shape. In this paper, the optical properties (extinction efficiency, scattering efficiency, single scattering albedo, and asymmetry factor) of mixed-phase cloud were calculated assuming externally and internally mixed cloud particle models in a broad spectral range of 0.2–100 μm at various effective particle diameters and ice volume fraction conditions. The influences of various microphysical factors on optical properties were comprehensively examined. For the externally mixed cloud particles, the shapes of ice crystals were found to become more important as the ice volume fraction increases. Compared with the mixed-phase cloud with larger effective diameter, the shape of ice crystals has a greater impact on the optical properties of the mixed-phase cloud with a smaller effective diameter (<20 μm). The optical properties calculated by internally and externally mixed models are similar in the longwave spectrum, while the optical properties of the externally mixed model are more sensitive to variations in ice volume fraction in the solar spectral region. The bulk scattering phase functions were also examined and compared. The results indicate that more in-depth analysis is needed to explore the radiative properties and impacts of mixed-phase clouds.
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Статті в журналах Дисертації

Дисертації з теми "Mixed-Phase cloud":

1

Fallas, German Vidaurre. "Characterization of mixed-phase clouds." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3275833.

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2

Farrington, Robert. "Testing mixed phase cloud parametrizations through confronting models with in-situ observations." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/testing-mixed-phase-cloud-parametrizations-through-confronting-models-with-insitu-observations(e2b7e31b-fa4a-4501-9f30-2ca2452c58fa).html.

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Accurate representations of clouds are required in large-scale weather and climate models to make detailed and precise predictions of the Earth's weather and climate. Representations of clouds within these models are limited by the present understanding of the role of aerosols in the microphysical processes responsible for cloud formation and development. As part of a NERC funded CASE studentship with the Met Office, this thesis aims to test new aerosol-dependent mixed-phase cloud parametrizations by obtaining extensive cloud microphysical measurements in-situ and comparing and contrasting them with model simulations. Cloud particle concentrations were measured during the Ice NUcleation Process Investigation And Quantification (INUPIAQ) field campaign at Jungfraujoch in Switzerland. A new probe was used to separate droplet and small ice concentrations by using depolarisation ratio and size thresholds. Whilst the new small ice crystal and droplet number concentrations compared favourably with other instruments, the size and depolarisation ratio thresholds were found to be subjective, and suggested to vary from cloud to cloud. An upwind site was chosen to measure out-of-cloud aerosol particle concentrations during INUPIAQ. During periods where the site was out-of-cloud and upwind of Jungfraujoch, several large-scale model simulations were run using the aerosol concentrations in an aerosol-dependent ice nucleation parametrization. The inclusion of the parametrization failed to increase the simulated ice crystal number concentrations, which were several orders of magnitude below those observed in-situ at Jungfraujoch. Several possible explanations for the high observed ice crystal number concentrations at Jungfraujoch are tested using further model simulations. Further primary ice nucleation was ruled out, as the inclusion of additional ice nucleating particles in the model simulations suppressed the liquid water content, preventing the simulation of the mixed-phase clouds observed during INUPIAQ. The addition of ice crystals produced via the Hallett-Mossop process upwind of Jungfraujoch into the model only infrequently provided enough ice crystals to match the observed concentrations. The inclusion of a simple surface flux of hoar crystals into the model simulations was found to produce ice crystal number concentrations of a similar magnitude to those observed at Jungfraujoch, without depleting the simulated liquid water content. By confronting models with in-situ observations of cloud microphysical process, this thesis highlights interactions between surface ice crystals and mixed-phase clouds, and their potential impact on large-scale models.
3

Atkinson, James David. "Freezing of droplets under mixed-phase cloud conditions." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/5858/.

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Mixed-phase clouds contain both liquid and ice particles. They have important roles in weather and climate and such clouds are thought to be responsible for a large proportion of precipitation. Their lifetime and precipitation rates are sensitive to the concentration of ice. This project focuses upon the formation of ice within clouds containing liquid droplets colder than 273 K. A new bench-top instrument has been developed to study ice nucleation in liquid droplets. Pure water droplets of sizes relevant to clouds in the lower atmosphere do not freeze homogeneously until temperatures below ~237 K are reached. However, literature measurements of nucleation rates are scattered over two kelvin and there is uncertainty over the actual mechanism of ice formation in small droplets. The freezing of droplets with diameters equivalent to ~4 – 17 μm has been observed. It was found that ice nucleation rates in the smallest droplets of this size range were consistent with nucleation due to the droplet surface, but that surface nucleation does not occur at fast enough rates to be significant in the majority of tropospheric clouds. Water droplets can be frozen at higher temperatures than relevant for homogeneous freezing due to the presence of a class of aerosol particles called ice nuclei. Field observations of ice crystal residues have shown that mineral dust particles are an important group of ice nuclei, and the ice nucleating ability of seven of the most common minerals found in atmospheric dust has been described. In comparison to the other minerals, it was found that the mineral K-feldspar is much more efficient at nucleating ice. To relate this result to the atmosphere, a global chemical and aerosol transport modelling study was performed. This study concluded that dust containing feldspar emitted from desert regions reaches all locations around the globe. At temperatures below ~255 K, the modelled concentration of feldspar is sufficient to explain field observations of ice nuclei concentrations.
4

Kripchak, Kristopher J. "Cloud phase and the surface energy balance of the arctic an investigation of mixed-phase clouds." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FKripchak.pdf.

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Thesis (M.S. in Meterology)--Naval Postgraduate School, March 2008.
Thesis Advisor(s): Guest, Peter. "March 2008." Description based on title screen as viewed on May 1, 2008. Includes bibliographical references (p. 59-61). Also available in print.
5

Williams, Robyn D. "Studies of Mixed-Phase Cloud Microphysics Using An In-Situ Unmanned Aerial Vehicle (UAV) Platform." Thesis, Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7252.

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Cirrus clouds cover between 20% - 50% of the globe and are an essential component in the climate. The improved understanding of ice cloud microphysical properties is contingent on acquiring and analyzing in-situ and remote sensing data from cirrus clouds. In ??u observations of microphysical properties of ice and mixed-phase clouds using the mini-Video Ice Particle Sizer (mini-VIPS) aboard robotic unmanned aerial vehicles (UAVs) provide a promising and powerful platform for obtaining valuable data in a cost-effective, safe, and long-term manner. The purpose of this study is to better understand cirrus microphysical properties by analyzing the effectiveness of the mini-VIPS/UAV in-situ platform. The specific goals include: (1) To validate the mini-VIPS performance by comparing the mini-VIPS data retrieved during an Artic UAV mission with data retrieved from the millimeterwavelength cloud radar (MMCR) at the Barrow ARM/CART site. (2) To analyze mini-VIPS data to survey the properties of high latitude mixedphase clouds The intercomparison between in-situ and remote sensing measurements was carried out by comparing reflectivity values calculated from in-situ measurements with observations from the MMCR facility. Good agreement between observations and measurements is obtained during the time frame where the sampled volume was saturated with respect to ice. We also have 1 2 shown that the degree of closure between calculated and observed reflectivity strongly correlates with the assumption of ice crystal geometry observed in the mini-VIPS images. The good correlation increases the confidence in mini-VIPS and MMCR measurements. Finally, the size distribution and ice crystal geometry obtained from the data analysis is consistent with published literature for similar conditions of temperature and ice supersaturation.
6

Costa, Anja [Verfasser]. "Mixed-phase and ice cloud observations with NIXE-CAPS / Anja Costa." Wuppertal : Universitätsbibliothek Wuppertal, 2018. http://d-nb.info/1156625181/34.

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7

Talik, Anja [Verfasser]. "Mixed-phase and ice cloud observations with NIXE-CAPS / Anja Costa." Wuppertal : Universitätsbibliothek Wuppertal, 2018. http://nbn-resolving.de/urn:nbn:de:hbz:468-20180326-082805-8.

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8

Young, Gillian. "Understanding the nucleation of ice particles in polar clouds." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/understanding-the-nucleation-of-ice-particles-in-polar-clouds(4f80f81b-ed06-480a-944b-6e3594ba8471).html.

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Arctic clouds are poorly represented in numerical models due to the complex, small-scale interactions which occur within them. Modelled cloud fractions are often significantly less than observed in this region; therefore, the radiative budget is not accurately simulated and forecasts of the melting cryosphere are fraught with uncertainty. Our ability to accurately model Arctic clouds can be improved through observational studies. Recent in situ airborne measurements from the springtime Aerosol-Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) campaign are presented in this thesis to improve our understanding of the cloud microphysical interactions unique to this region. Aerosol-cloud interactions - where aerosol particles act as ice nucleating particles (INPs) or cloud condensation nuclei (CCN) - are integral to the understanding of clouds on a global scale. In the Arctic, uncertainties caused by our poor understanding of these interactions are enhanced by strong feedbacks between clouds, the boundary layer, and the sea ice. In the Arctic spring, aerosol-cloud interactions are affected by the Arctic haze, where a stable boundary layer allows aerosol particles to remain in the atmosphere for long periods of time. This leads to a heightened state of mixing in the aerosol population, which affects the ability of particles to act as INPs or CCN. Aerosol particle compositional data are presented to indicate which particles are present during the ACCACIA campaign, and infer how they may participate in aerosol-cloud interactions. Mineral dusts (known INPs) are identified in all flights considered, and the dominating particle classes in each case vary with changing air mass history. Mixed particles, and an enhanced aerosol loading, are identified in the final case. Evidence is presented which suggests these characteristics may be attributed to biomass burning activities in Siberia and Scandinavia. Additionally, in situ airborne observations are presented to investigate the relationship between the Arctic atmosphere and the mixed-phase clouds - containing both liquid cloud droplets and ice crystals - common to this region. Cloud microphysical structure responds strongly to changing surface conditions, as strong heat and moisture fluxes from the comparatively-warm ocean promote more turbulent motion in the boundary layer than the minimal heat fluxes from the frozen sea ice. Observations over the transition from sea ice to ocean show that the cloud liquid water content increases four-fold, whilst ice crystal number concentrations, N_ice, remain consistent at ~0.5/L. Following from this study, large eddy simulations are used to illustrate the sensitivity of cloud structure, evolution, and lifetime to N_ice. To accurately model mixed-phase conditions over sea ice, marginal ice, and ocean, ice nucleation must occur under water-saturated conditions. Ocean-based clouds are found to be particularly sensitive to N_ice, as small decreases in N_ice allow glaciating clouds to be sustained, with mixed-phase conditions, for longer. Modelled N_ice also influences precipitation development over the ocean, with either snow or rain depleting the liquid phase of the simulated cloud.
9

Myagkov, Alexander. "Shape-temperature relationship of ice crystals in mixed-phase clouds based on observations with polarimetric cloud radar." Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-216598.

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This thesis is devoted to the experimental quantitative characterization of the shape and orientation distribution of ice particles in clouds. The characterization is based on measured and modeled elevation dependencies of the polarimetric parameters differential reflectivity and correlation coefficient. The polarimetric data is obtained using a newly developed 35-GHz cloud radar MIRA-35 with hybrid polarimetric configuration and scanning capabilities. The full procedure chain of the technical implementation and the realization of the setup of the hybrid-mode cloud radar for the shape determination are presented. This includes the description of phase adjustments in the transmitting paths, the introduction of the general data processing scheme, correction of the data for the differences of amplifications and electrical path lengths in the transmitting and receiving channels, the rotation of the polarization basis by 45°, the correction of antenna effects on polarimetric measurements, the determination of spectral polarimetric variables, and the formulation of a scheme to increase the signal-to-noise ratio. Modeling of the polarimetric variables is based on existing backscattering models assuming the spheroidal representation of cloud scatterers. The parameters retrieved from the model are polarizability ratio and degree of orientation, which can be assigned to certain particle orientations and shapes. In the thesis the first quantitative estimations of ice particle shape at the top of liquid-topped clouds are presented. Analyzed ice particles were formed in the presence of supercooled water and in the temperature range from -20 °C to -3 °C. The estimation is based on polarizability ratios of ice particles measured by the MIRA-35 with hybrid polarimetric configuration, manufactured by METEK GmbH. For the study, 22 cases observed during the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) field campaign were used. Polarizability ratios retrieved for cloud layers with cloud-top temperatures of about -5, -8, -15, and -20 °C were 1.6, 0.9, 0.6, and 0.9, respectively. Such values correspond to prolate, quasi-isotropic, oblate, and quasi-isotropic particles, respectively. Data from a free-fall chamber were used for the comparison. A good agreement of detected shapes with well-known shape{temperature dependencies observed in laboratories was found.
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Myagkov, Alexander [Verfasser], Andreas [Akademischer Betreuer] Macke, and Herman [Gutachter] Russchenberg. "Shape-temperature relationship of ice crystals in mixed-phase clouds based on observations with polarimetric cloud radar : Shape-temperature relationship of ice crystals in mixed-phase cloudsbased on observations with polarimetric cloud radar / Alexander Myagkov ; Gutachter: Herman Russchenberg ; Betreuer: Andreas Macke." Leipzig : Universitätsbibliothek Leipzig, 2017. http://d-nb.info/1240696310/34.

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Книги з теми "Mixed-Phase cloud":

1

Morris, D. S. The role of the Hallett-Mossop process on the mixed phase of layer clouds in general circulation models. Manchester: UMIST, 1996.

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2

Andronache, Constantin. Mixed-Phase Clouds: Observations and Modeling. Elsevier Science & Technology Books, 2017.

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3

Andronache, Constantin. Mixed-Phase Clouds: Observations and Modeling. Elsevier, 2017.

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4

Mixed-Phase Clouds. Elsevier, 2018. http://dx.doi.org/10.1016/c2016-0-00556-9.

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5

Observed Microphysical and Radiative Structure of Mid-Level, Mixed-Phase Clouds. Storming Media, 2001.

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6

Loewe, Katharina. Arctic Mixed-phase Clouds: Macro- and Microphysical Insights With a Numerical Model. Saint Philip Street Press, 2020.

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7

Lynch, David K., Kenneth Sassen, David O'C Starr, and Graeme Stephens, eds. Cirrus. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195130720.001.0001.

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Cirrus clouds are high, thin, tropospheric clouds composed predominately of ice. In the last ten years, considerable work has shown that cirrus is widespread--more common than previously believed--and has a significant impact on climate and global change. As the next generation weather satellites are being designed, the impact of cirrus on remote sensing and the global energy budget must be recognized and accommodated. This book, the first to be devoted entirely to cirrus clouds, captures the state of knowledge of cirrus and serves as a practical handbook as well. Each chapter is based on an invited review talk presented at Cirrus, a meeting hosted by the Optical Society of America and co-sponsored by the American Geophysical Union and the American Meteorological Society. All aspects of cirrus clouds are covered, an approach that reaches into diverse fields. Topics include: the definition of cirrus, cirrus climatologies, nucleation, evolution and dissipation, mixed-phase thermodynamics, crystallinity, orientation mechanisms, dynamics, scattering, radiative transfer, in situ sampling, processes that produce or influence cirrus (and vice versa), contrails, and the influence of cirrus on climate.

Частини книг з теми "Mixed-Phase cloud":

1

Avgoustoglou, E., and T. Tzeferi. "The Application of a Mixed-Phase Statistical Cloud-Cover Scheme to the Local Numerical Weather Prediction Model COSMO.GR." In Advances in Meteorology, Climatology and Atmospheric Physics, 889–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29172-2_124.

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2

Pietikäinen, Joni-Pekka, J. Hienola, Harri Kokkola, S. Romakkaniemi, Kari E. J. Lehtinen, Markku Kulmala, and Ari Laaksonen. "Model Studies of Nitric Acid Condensation in Mixed-phase Clouds." In Nucleation and Atmospheric Aerosols, 596–600. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6475-3_118.

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3

Laj, P., A. I. Flossmann, W. Wobrock, S. Fuzzi, G. Orsi, L. Ricci, H. ten Brink, et al. "Subproject PROCLOUD Phase Partitioning of Chemical Species in Mixed Clouds during CIME." In Transport and Chemical Transformation in the Troposphere, 90–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56722-3_16.

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4

Cozic, J., B. Verheggen, Ernest Weingartner, Urs Baltensperger, S. Mertes, K. N. Bower, I. Crawford, et al. "Partitioning of Aerosol Particles in Mixed-phase Clouds at a High Alpine Site." In Nucleation and Atmospheric Aerosols, 565–69. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6475-3_112.

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5

Garnier, Anne, Jacques Pelon, David Winker, Melody Avery, Mark Vaughan, and Yongxiang Hu. "Identification of Mixed-Phase Clouds Using Combined CALIPSO Lidar and Imaging Infrared Radiometer Observations." In Proceedings of the 30th International Laser Radar Conference, 817–23. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37818-8_105.

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6

McCoy, Daniel T., Dennis L. Hartmann, and Mark D. Zelinka. "Mixed-Phase Cloud Feedbacks." In Mixed-Phase Clouds, 215–36. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-810549-8.00009-x.

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7

Emanuel, Kerry A. "Stratocumulus And Trade-Cumulus Boundary Layers." In Atmospheric Convection, 421–62. Oxford University PressNew York, NY, 1994. http://dx.doi.org/10.1093/oso/9780195066302.003.0013.

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Abstract In Chapter 3 we explored the properties of dry convective boundary layers that form when there is a positive buoyancy flux from the surface. Under a variety of conditions, such boundary layers become deep and/or moist enough that clouds form within them. When this happens, the properties of the boundary layer are strongly altered by the latent heat released and absorbed when water changes phase and by the very strong effect of clouds on radiative transfer. In this chapter we explore the properties of boundary layers strongly influenced by clouds that are shallow enough that little precipitation is formed. Boundary layers with clouds may be classified into four groups, illustrated in Figure 13.1: foggy layers, cloud-topped mixed layers, trade­ cumulus layers, and elevated stratocumulus/mixed trade cumulus-strato­cumulus layers. These are discussed in the following sections. Fog is defined as cloud in contact with the surface. For the present purposes, we exclude from this definition cloud in contact with hills or mountains. Over land, fog may form at night as a consequence of radiative cooling of the surface. Heat is lost from the air radiatively and diffused downward into the surface by wind-induced turbulence. If the air near the ground is cooled to its dew-point temperature, condensation occurs, first near the ground and later at higher altitudes. Initially, the fog is thickest near the surface, with the cloud-water content falling off rapidly with altitude. If it does not achieve a thickness greater than a few meters by sunrise, it will remain in this state until absorption of sunlight by the surface and by the cloud itself raises the temperature enough to evaporate the cloud. Thin fog of this kind is not convective, because the cooling occurs from below.
8

Jakob, Christian. "Ice Clouds in Numerical Weather Prediction Models: Progress, Problems, and Prospects." In Cirrus. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195130720.003.0020.

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The properties of cirrus, as well as the role ice clouds play in the atmosphere, have been extensively described in the previous chapters. To represent the effects of cirrus in atmospheric models, several intimately linked processes need to be described. These processes include the generation and dissipation of ice clouds as well as their interaction with the radiative fluxes throughout the atmosphere. In this chapter the cloud parameterization aspects of this problem (i.e., the treatment of the generation and dissipation of ice clouds), are discussed in the context of global numerical weather prediction (NWP) models. Aspects of the radiative transfer in ice clouds can be found in chapter 13. The main focus of the current chapter is on the cloud parameterization used in the global forecast model of the European Centre for Medium-Range Weather Forecasts (ECMWF). This parameterization will serve as an example in highlighting the progress made, the problems encountered, and the prospects for improving the representation of ice clouds in atmospheric models. The principles of representing clouds in global NWP models are identical to those in general circulation models (GCMs) used for climate research (see chapter 15). Although ice clouds are the focus of this book, a substantial part of this chapter will be concerned with the overall treatment of clouds in numerical models of the atmosphere. In fact, many models used in NWP today distinguish ice clouds from mixed-phase and water clouds only as a function of temperature. Cloud parameterizations in GCMs have evolved rapidly over the last few years. Section 16.2 is a general overview of the progress made. Section 16.3 will describe the cloud parameterization that is currently used in the ECMWF forecast model as a specific example for a state-of-the-art cloud parameterization in NWP. General aspects of the simulation of ice clouds with this model will be presented. GCM simulations of the atmosphere are very sensitive to the treatment of clouds in general (Senior and Mitchell 1993; Rasch and Kristjansson 1998) and to assumptions about cloud ice in particular (Fowler et al. 1996; Jakob and Morcrette 1995). Section 16.4 gives an example of those sensitivities and the model design problems that can arise when model sensitivities exist in combination with a lack of observations, as noted for cloud ice by Stephens et al. (1998).
9

Andronache, Constantin. "Introduction." In Mixed-Phase Clouds, 1–9. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-810549-8.00001-5.

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10

Whale, Thomas F. "Ice Nucleation in Mixed-Phase Clouds." In Mixed-Phase Clouds, 13–41. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-810549-8.00002-7.

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Тези доповідей конференцій з теми "Mixed-Phase cloud":

1

Oshchepkov, Sergey, and Harumi Isaka. "Studies of an Inverse Scattering Problem Solution for Mixed-Phase and Cirrus Clouds." In The European Conference on Lasers and Electro-Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/cleo_europe.1996.ctuk11.

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The determination of cloud microphysicai parameters by light scattering methods can be referred to one of the most priority procedures of the modem atmospheric optics. Such an information including phase cloud structure is interesting as itself at studying the formation of mixed phase clouds, nucleation processes, precipitation and is a primary factor to calculate radiative transfer through clouds and to design radiative models of cloud cover.
2

Coutris, Pierre, Guy Febvre, Louis Jaffeux, Alfons Schwarzenboeck, Fabien Dezitter, Anne-Claire Billault-Roux, Jacopo Grazioli, Alexis Berne, Susana Jorquera, and Julien Delanoe. "Assessing Mixed-Phase Conditions during the ICE GENESIS Snow Measurement Campaign." In International Conference on Icing of Aircraft, Engines, and Structures. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-1494.

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<div class="section abstract"><div class="htmlview paragraph">In the framework of the European ICE GENESIS project (https://www.ice-genesis.eu/), a field experiment was conducted in the Swiss Jura in January 2021 in order to characterize snow microphysical properties and document snow conditions for aviation industry purposes. Complementary to companion papers reporting on snow properties, this study presents an investigation on mixed-phase conditions sampled during the ICE GENESIS field campaign. Using in situ measurement of the liquid and total water content, the ice mass fraction is calculated and serves as a criteria to identify mixed-phase conditions. In the end, mixed phase conditions were identified in almost 30 % of the 3800 km long cloud samples included in the ICE GENESIS dataset. The data suggests that the occurrence of mixed-phase does not clearly depend on temperature in the 0 to -10 °C range, but varies significantly from one cloud system to another. The distribution of mixed phase and liquid only spatial scales cascades from 100 m (instrumental resolution limit) to 12 km, existing most of the time as pockets of few hundreds of meters embedded in larger cloudy areas.</div></div>
3

Yan, Sihong, and Jose Palacios. "Experimental Measurement of Ice Accretion Rate in Mixed-phase Icing Cloud." In 2018 Atmospheric and Space Environments Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-4222.

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4

Molkoselka, Eero, Ville Kaikkonen, and Anssi Makynen. "Instrument and Method for Measuring Ice Accretion in Mixed-Phase Cloud Conditions." In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2020. http://dx.doi.org/10.1109/i2mtc43012.2020.9129604.

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5

Bartkus, Tadas P., Peter M. Struk, Jen-Ching Tsao, and Judith F. Van Zante. "Numerical Analysis of Mixed-Phase Icing Cloud Simulations in the NASA Propulsion Systems Laboratory." In 8th AIAA Atmospheric and Space Environments Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3739.

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6

Bartkus, Tadas P., Peter M. Struk, and Jen-Ching Tsao. "Comparisons of Mixed-Phase Icing Cloud Simulations with Experiments Conducted at the NASA Propulsion Systems Laboratory." In 9th AIAA Atmospheric and Space Environments Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-4243.

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7

Goriachev, Aleksei, Vadim Zhulin, Pavel Goriachev, Sergei Grebenkov, and Vladimir Savenkov. "Experimental Processing of Methodical Questions of Modeling the Atmospheric Cloud Containing Ice Crystals and Mixed Phase." In International Conference on Icing of Aircraft, Engines, and Structures. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2019. http://dx.doi.org/10.4271/2019-01-1922.

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8

Lucke, Johannes Reinhard, Tina Jurkat, Darrel Baumgardner, Frank Kalinka, Manuel Moser, Elena De La Torre Castro, and Christiane Voigt. "Characterization of Atmospheric Icing Conditions during the HALO-(AC) <sup>3</sup> Campaign with the Nevzorov Probe and the Backscatter Cloud Probe with Polarization Detection." In International Conference on Icing of Aircraft, Engines, and Structures. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-1485.

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<div class="section abstract"><div class="htmlview paragraph">The measurement and in-flight characterization of atmospheric icing conditions remains a challenging task. This is due to the large variability of microphysical properties of icing conditions. Icing may occur in pure supercooled liquid clouds of various droplet sizes, it may contain freezing drizzle or freezing rain drops and it also takes place in various types of mixed-phase conditions. A sensor or a combination of sensors to discriminate these icing environments would therefore be beneficial. Especially the phase classification of small cloud particles is still difficult to assess. Within the SENS4ICE project, the German Aerospace Center (DLR) suggests the use of the Nevzorov probe and the Backscatter Cloud Probe with Polarization Detection (BCPD) for the detection and differentiation of icing conditions during research missions that lack standard underwing probes. The first research flights with this instrument combination were conducted in March and April 2022 out of Longyearbyen, Svalbard in the scope of the HALO-(AC)<sup>3</sup> campaign. The Polar 6 aircraft of the Alfred-Wegener-Institut was equipped with the two sensors and other established microphysical cloud probes for validation. Here, we demonstrate our evaluation strategy of the two instruments and show how their data can be used to assess microphysical cloud conditions. We test this evaluation strategy on the basis of one research flight during which a large variety of icing conditions occurred. Furthermore, we also show a comparison of our results to the predictions of the icing warning system ADWICE of the German Weather Service.</div></div>
9

Ray, Mark, Kaare Anderson, and Kent Ramthun. "Optical Ice Detector Lite: Initial Flight Test Results." In International Conference on Icing of Aircraft, Engines, and Structures. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-1427.

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<div class="section abstract"><div class="htmlview paragraph">In response to safety regulations regarding aircraft icing, Collins Aerospace has developed and tested a new generation of optical ice detectors (OID Lite) intended to discriminate among icing conditions described by Appendix C and Appendix O of 14 CFR Part 25 and Appendix D of Part 33. The OID Lite is a flush-mounted, short-range, polarimetric optical sensor that samples the airstream up to two meters beyond the skin of the aircraft. The intensity and polarization of the backscatter light correlate with bulk properties of the cloud, such as cloud density and phase. Drizzle-sized droplets, mixed within a small droplet cloud, appear as scintillation spikes in the lidar signal when it is processed pulse-by-pulse. Scintillation in the backscatter (in combination with the outside air temperature monitored by another probe) signals the presence of supercooled large droplets (SLD) within the cloud—a capability incorporated into the OID Lite to meet the requirements of Appendix O. Recent laboratory and flight tests have demonstrated the ability of the OID Lite to discriminate among the various icing conditions. In addition, the OID Lite has discriminated mixed phase in a flight test aboard the Weather Modification International Cessna Citation-II research aircraft. The threshold for detection as defined by SAE AS5498B is 0.5 g/m<sup>3</sup> IWC, while that for liquid water cloud detection is 0.05 g/m<sup>3</sup> LWC [<span class="xref">1</span>].</div></div>
10

Anderson, Kaare, Mark Ray, and Darren Jackson. "Optical Ice Detector: Measurement Comparison to Research Probes." In International Conference on Icing of Aircraft, Engines, and Structures. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-1428.

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<div class="section abstract"><div class="htmlview paragraph">The Collins Aerospace Optical Ice Detector is a short-range polarimetric cloud lidar designed to detect and discriminate among all types of icing conditions with the use of a single sensor. Recent flight tests of the Optical Ice Detector (OID) aboard a fully instrumented atmospheric research aircraft have allowed comparisons of measurements made by the OID with those of standard cloud research probes. The tests included some icing conditions appropriate to the most recent updates to the icing regulations. Cloud detection, discrimination of mixed phase, and quantification of cloud liquid water content for a cloud within the realm of Appendix C were all demonstrated. The duration of the tests (eight hours total) has allowed the compilation of data from the OID and cloud probes for a more comprehensive comparison. The OID measurements and those of the research probes agree favorably given the uncertainties inherent in these instruments.</div></div>

Звіти організацій з теми "Mixed-Phase cloud":

1

Xie, S., J. Boyle, S. Klein, X. Liu, and S. Ghan. Evaluation of Mixed-Phase Cloud Parameterizations in Short-Range Weather Forecasts with CAM3 and AM2 for Mixed-Phase Arctic Cloud Experiment. Office of Scientific and Technical Information (OSTI), June 2007. http://dx.doi.org/10.2172/1021003.

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2

Shupe, Matthew. Final Report: Investigations of Mixed-Phase Cloud Microphysical, Radiative, and Dynamical Processes. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1298132.

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3

Wang, Zhien. Improving Mixed-phase Cloud Parameterization in Climate Model with the ACRF Measurements. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1335565.

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4

Liu, X., SJ Ghan, and S. Xie. Evaluation of Mixed-Phase Cloud Microphysics Parameterizations with the NCAR Single Column Climate Model (SCAM) and ARM Observations. Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/1021005.

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5

Liu, X., SJ Ghan, S. Xie, J. Boyle, and SA Klein. Evaluation of A New Mixed-Phase Cloud Microphysics Parameterization with the NCAR Climate Atmospheric Model (CAM3) and ARM Observations Fourth Quarter 2007 ARM Metric Report. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/948097.

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6

Tjernstrom, Michael. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada572578.

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7

Tjernstrom, Michael. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601216.

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8

Tjernstroem, Michael. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada616540.

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9

Sulia, Kara, Zachary Lebo, Carl Schmitt, and Vanessa Przybylo. Investigating the Evolution of Ice Particle Distributions in Mixed-Phase Clouds. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1773361.

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10

Turner, David D. Microphysical Properties of Single and Mixed-Phase Arctic Clouds Derived from AERI Observations. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/1000181.

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