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liquid to total water (the so-called supercooled liquid fraction or SLF) in mixed-phase clouds decreases the SW negative feedback, and thus increases climate sensitivity. These results have been attributed to models with higher T5050 having more susceptible ice ( McCoy et al. 2018 ), which is hypothesized to control the feedback strength (as in Tan et al. 2018 ). Improvements in understanding the governing mechanisms are especially important as some modeling studies with observationally based
liquid to total water (the so-called supercooled liquid fraction or SLF) in mixed-phase clouds decreases the SW negative feedback, and thus increases climate sensitivity. These results have been attributed to models with higher T5050 having more susceptible ice ( McCoy et al. 2018 ), which is hypothesized to control the feedback strength (as in Tan et al. 2018 ). Improvements in understanding the governing mechanisms are especially important as some modeling studies with observationally based
, concentration, and refractive index ( Sun and Shine 1994 ). The generalized properties of mixed-phase clouds in which liquid and ice coexist are relatively unknown and remain an active area of research. Recent studies indicate that 40%–60% of clouds in the temperature range between 0° and −30°C are mixed phase and 30%–60% are supercooled liquid water clouds ( Korolev et al. 2003 ; Mazin 2006 ; Shupe et al. 2006 ; Zhang et al. 2010 ). The longevity and areal extent of these supercooled-liquid and mixed-phase
, concentration, and refractive index ( Sun and Shine 1994 ). The generalized properties of mixed-phase clouds in which liquid and ice coexist are relatively unknown and remain an active area of research. Recent studies indicate that 40%–60% of clouds in the temperature range between 0° and −30°C are mixed phase and 30%–60% are supercooled liquid water clouds ( Korolev et al. 2003 ; Mazin 2006 ; Shupe et al. 2006 ; Zhang et al. 2010 ). The longevity and areal extent of these supercooled-liquid and mixed-phase
shortwave cloud radiative forcing (SWCRF) over the Southern Ocean ( Bodas-Salcedo et al. 2012 ; Williams et al. 2013 ). Bodas-Salcedo confirmed that the SWCRF bias mainly originated from underestimation of supercooled liquid water in low-level mixed-phase clouds. Large intermodal spread in supercooled liquid water over the mid- to high-latitude regions results in large uncertainties in cloud feedbacks (e.g., McCoy et al. 2015 ). The SWCRF bias mainly originates from poor representation of cloud
shortwave cloud radiative forcing (SWCRF) over the Southern Ocean ( Bodas-Salcedo et al. 2012 ; Williams et al. 2013 ). Bodas-Salcedo confirmed that the SWCRF bias mainly originated from underestimation of supercooled liquid water in low-level mixed-phase clouds. Large intermodal spread in supercooled liquid water over the mid- to high-latitude regions results in large uncertainties in cloud feedbacks (e.g., McCoy et al. 2015 ). The SWCRF bias mainly originates from poor representation of cloud
1. Introduction Cloud thermodynamic phase is a highly uncertain observable from satellite observations that indicates whether a cloud is composed of liquid water droplets, ice crystals, or a mixture of the two phases of hydrometeors (i.e., mixed phase). The determination of cloud phase is an important step in the satellite-based retrieval of several cloud properties, such as cloud particle size, optical thickness, and water path. Cloud phase can significantly impact the planetary radiation
1. Introduction Cloud thermodynamic phase is a highly uncertain observable from satellite observations that indicates whether a cloud is composed of liquid water droplets, ice crystals, or a mixture of the two phases of hydrometeors (i.e., mixed phase). The determination of cloud phase is an important step in the satellite-based retrieval of several cloud properties, such as cloud particle size, optical thickness, and water path. Cloud phase can significantly impact the planetary radiation
objective, unique observations of cloud water thermodynamic phases are analyzed using the measurements made with both the Doppler radar Radar System Airborne (RASTA) and the lidar Leandre New Generation (LNG) ( Delanoë et al. 2013 ) on board the French SAFIRE (Service des Avions Français Instrumentés pour la Recherche en Environnement) Falcon 20. To reach the second objective, numerical simulations of the MesoNH model ( Lac et al. 2018 ) with two different representations for MPCs have been performed
objective, unique observations of cloud water thermodynamic phases are analyzed using the measurements made with both the Doppler radar Radar System Airborne (RASTA) and the lidar Leandre New Generation (LNG) ( Delanoë et al. 2013 ) on board the French SAFIRE (Service des Avions Français Instrumentés pour la Recherche en Environnement) Falcon 20. To reach the second objective, numerical simulations of the MesoNH model ( Lac et al. 2018 ) with two different representations for MPCs have been performed
1. Introduction In passive remote sensing, cloud thermodynamic phase (water or ice) information typically comes from the spectral absorption difference between visible (VIS; 0.65 μ m) and shortwave infrared (SWIR; 1.5–1.6 and 3–4 μ m) wavelengths. Neither ice clouds nor water clouds absorb much visible light. However, absorption by both ice and water increases at near-infrared wavelengths, and multiple scattering enhances the absorption. At SWIR wavelengths, ice clouds absorb significantly
1. Introduction In passive remote sensing, cloud thermodynamic phase (water or ice) information typically comes from the spectral absorption difference between visible (VIS; 0.65 μ m) and shortwave infrared (SWIR; 1.5–1.6 and 3–4 μ m) wavelengths. Neither ice clouds nor water clouds absorb much visible light. However, absorption by both ice and water increases at near-infrared wavelengths, and multiple scattering enhances the absorption. At SWIR wavelengths, ice clouds absorb significantly
1. Introduction There are several reasons that one may wish to know the fraction of liquid water in thin, mixed-phase layer clouds. One is aircraft icing. Between 1975 and 1988, there were 803 aviation accidents in the continental United States in which icing was a cause or a factor ( Bragg et al. 1998 ). Cober et al. (2001) discuss several well-documented cases of severe icing suffered by research aircraft and also mention the crash of an ATR-72 commuter aircraft near Roselawn, Indiana, in
1. Introduction There are several reasons that one may wish to know the fraction of liquid water in thin, mixed-phase layer clouds. One is aircraft icing. Between 1975 and 1988, there were 803 aviation accidents in the continental United States in which icing was a cause or a factor ( Bragg et al. 1998 ). Cober et al. (2001) discuss several well-documented cases of severe icing suffered by research aircraft and also mention the crash of an ATR-72 commuter aircraft near Roselawn, Indiana, in
1. Introduction Cloud phase and ice crystal orientation are among the major factors that determine the radiative effect of clouds. Even at the same wavelength, the complex refraction index differs between water and ice. The phase function of a cloud particle, which determines its scattering characteristics, varies depending on the shape of the particle ( Sassen and Liou 1979 ). Phases and shapes of clouds are also important in retrieving the microphysical properties, such as particle size
1. Introduction Cloud phase and ice crystal orientation are among the major factors that determine the radiative effect of clouds. Even at the same wavelength, the complex refraction index differs between water and ice. The phase function of a cloud particle, which determines its scattering characteristics, varies depending on the shape of the particle ( Sassen and Liou 1979 ). Phases and shapes of clouds are also important in retrieving the microphysical properties, such as particle size
algorithm combines measurements from radiosondes, lidar, radar, microwave radiometer, and infrared radiometer within a framework that consists of multiple ground-based remote-sensor retrieval methods to produce estimates of cloud water content and hydrometeor size for both liquid and ice phases. The strength of the algorithm lies in the instrument and retrieval synergy. For example, while infrared measurements are accurate at determining cloud properties for optically thin clouds, they provide little
algorithm combines measurements from radiosondes, lidar, radar, microwave radiometer, and infrared radiometer within a framework that consists of multiple ground-based remote-sensor retrieval methods to produce estimates of cloud water content and hydrometeor size for both liquid and ice phases. The strength of the algorithm lies in the instrument and retrieval synergy. For example, while infrared measurements are accurate at determining cloud properties for optically thin clouds, they provide little
, seawater, and bacterial samples were also studied, often motivated by collections of fog water and precipitation (e.g., Schnell and Vali 1976 ; Vali et al. 1976 ; Schnell 1977 ). 3) IRs in mixed-phase clouds There exist two ground-based CVI inlet systems for sampling IRs of ice crystals contained in MPCs. These are the Ice-CVI ( Mertes et al. 2007 ) and Ice Selective Inlet (ISI; Kupiszewski et al. 2015 ), both of which have been deployed at the high-altitude research station Jungfraujoch in the
, seawater, and bacterial samples were also studied, often motivated by collections of fog water and precipitation (e.g., Schnell and Vali 1976 ; Vali et al. 1976 ; Schnell 1977 ). 3) IRs in mixed-phase clouds There exist two ground-based CVI inlet systems for sampling IRs of ice crystals contained in MPCs. These are the Ice-CVI ( Mertes et al. 2007 ) and Ice Selective Inlet (ISI; Kupiszewski et al. 2015 ), both of which have been deployed at the high-altitude research station Jungfraujoch in the
