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returns, the range-resolved extinction coefficient and effective particle diameter. From these primary solutions and the assumption that the form of the droplet size distribution is known or given, for example, a modified gamma function for cloud droplets, other important microphysical parameters such as the liquid water content (LWC) of water clouds can be calculated at the same high temporal and spatial resolutions as those of the original lidar measurements. The subject of this paper is not the
returns, the range-resolved extinction coefficient and effective particle diameter. From these primary solutions and the assumption that the form of the droplet size distribution is known or given, for example, a modified gamma function for cloud droplets, other important microphysical parameters such as the liquid water content (LWC) of water clouds can be calculated at the same high temporal and spatial resolutions as those of the original lidar measurements. The subject of this paper is not the
1. Introduction Early condensation schemes of cloud models contained the assumption that a computational grid box volume element was either entirely saturated or entirely unsaturated. To improve upon this crude approximation, Sommeria and Deardorff (1977) introduced the concept of statistical distribution for variables such as total water specific humidity q w and liquid water potential temperature θ l , allowing the computation of partial cloudiness R and liquid water specific humidity
1. Introduction Early condensation schemes of cloud models contained the assumption that a computational grid box volume element was either entirely saturated or entirely unsaturated. To improve upon this crude approximation, Sommeria and Deardorff (1977) introduced the concept of statistical distribution for variables such as total water specific humidity q w and liquid water potential temperature θ l , allowing the computation of partial cloudiness R and liquid water specific humidity
1. Introduction A complicating factor in simulating and understanding the climatic roles of water vapor (WV) and clouds is their tight coupling with circulation, posing a major bottleneck in narrowing the uncertainty of cloud feedback ( Bony et al. 2015 ). This motivates us to construct a model of passive WV and clouds, meaning that both are advected as tracers that do not feed back on circulation either through latent heat release or through cloud radiative effects (CRE). Such a model can be
1. Introduction A complicating factor in simulating and understanding the climatic roles of water vapor (WV) and clouds is their tight coupling with circulation, posing a major bottleneck in narrowing the uncertainty of cloud feedback ( Bony et al. 2015 ). This motivates us to construct a model of passive WV and clouds, meaning that both are advected as tracers that do not feed back on circulation either through latent heat release or through cloud radiative effects (CRE). Such a model can be
1. Introduction At temperatures between 0° and −40°C, liquid water droplets and ice crystals may coexist in a single-layered cloud. Although studies have indicated that the parameterization of so-called mixed-phase clouds is critical for understanding the radiative characteristics of clouds in climate models and satellite remote sensing applications (e.g., Li and Le Treut 1992 ; Sun and Shine 1995 ; Gregory and Morris 1996 ; Hogan et al. 2002 ; Yang et al. 2003 ; McFarquhar and Cober 2004
1. Introduction At temperatures between 0° and −40°C, liquid water droplets and ice crystals may coexist in a single-layered cloud. Although studies have indicated that the parameterization of so-called mixed-phase clouds is critical for understanding the radiative characteristics of clouds in climate models and satellite remote sensing applications (e.g., Li and Le Treut 1992 ; Sun and Shine 1995 ; Gregory and Morris 1996 ; Hogan et al. 2002 ; Yang et al. 2003 ; McFarquhar and Cober 2004
1. Introduction It has long been recognized that clouds play a dominant role in the earth’s climate and its changes. Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level boundary layer clouds have the most significant influence on cloud radiative forcing because of their areal extent and frequency ( Harrison et al. 1990 ; Hartmann et al. 1992 ). Radiation absorbed by boundary layer clouds also plays an important role in the
1. Introduction It has long been recognized that clouds play a dominant role in the earth’s climate and its changes. Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level boundary layer clouds have the most significant influence on cloud radiative forcing because of their areal extent and frequency ( Harrison et al. 1990 ; Hartmann et al. 1992 ). Radiation absorbed by boundary layer clouds also plays an important role in the
Accurate Absolute Measurements of Liquid Water Content (LWC) and Ice Water Content (IWC) of Clouds and Precipitation with Spectrometric Water Raman Lidar1. Introduction Water content and phase of clouds are key to a better understanding of weather and climate processes ( Baker 1997 ; Illingworth et al. 2007 ), and so considerable research effort has been dedicated to measuring these quantities over the last decades, be it in situ or remotely. Active remote sensing of liquid water content (LWC) and ice water content (IWC) from the ground, from aircraft and satellites is attempted using lidar or radar. Early studies explored techniques that
1. Introduction Water content and phase of clouds are key to a better understanding of weather and climate processes ( Baker 1997 ; Illingworth et al. 2007 ), and so considerable research effort has been dedicated to measuring these quantities over the last decades, be it in situ or remotely. Active remote sensing of liquid water content (LWC) and ice water content (IWC) from the ground, from aircraft and satellites is attempted using lidar or radar. Early studies explored techniques that
, cloud optical depth is primarily controlled by liquid water path (LWP), which is the vertically integrated cloud liquid ( Stephens 1978 ). Ice affects cloud optical depth to a lesser extent owing to larger sizes of ice particles and ice water path (IWP) being generally smaller than LWP ( Pruppacher and Klett 2010 ; McCoy et al. 2014 ; Cesana and Storelvmo 2017 ). GCMs predict a robust extratropical LWP increase in response to global warming, which is thought be the main driver of the negative SW
, cloud optical depth is primarily controlled by liquid water path (LWP), which is the vertically integrated cloud liquid ( Stephens 1978 ). Ice affects cloud optical depth to a lesser extent owing to larger sizes of ice particles and ice water path (IWP) being generally smaller than LWP ( Pruppacher and Klett 2010 ; McCoy et al. 2014 ; Cesana and Storelvmo 2017 ). GCMs predict a robust extratropical LWP increase in response to global warming, which is thought be the main driver of the negative SW
1. Introduction Clouds, which cover a significant fraction of the earth’s surface, play a critical role in affecting the radiation balance by partly reflecting the incoming shortwave sunlight back to space and by absorbing infrared radiation emitted by the surface. As a highly dispersed system made up of tiny droplets, a water cloud can be characterized in terms of liquid water content (LWC) and droplet number concentration ( N d ). The corresponding cloud optical depth ( τ ) is approximately
1. Introduction Clouds, which cover a significant fraction of the earth’s surface, play a critical role in affecting the radiation balance by partly reflecting the incoming shortwave sunlight back to space and by absorbing infrared radiation emitted by the surface. As a highly dispersed system made up of tiny droplets, a water cloud can be characterized in terms of liquid water content (LWC) and droplet number concentration ( N d ). The corresponding cloud optical depth ( τ ) is approximately
microphysical quantities such as cloud liquid water content (LWC) and drop size estimates ( Gosset and Sauvageot 1992 ; Vivekanandan et al. 1999a , 2001 , 2004 ; Hogan et al. 2005 ). If effective, this capability would offer the prospect of remotely monitoring cloud microphysical characteristics, providing data useful for understanding meteorological processes, assimilation into numerical weather prediction (NWP) models, validating NWP forecasts and satellite-based retrievals, identifying conditions
microphysical quantities such as cloud liquid water content (LWC) and drop size estimates ( Gosset and Sauvageot 1992 ; Vivekanandan et al. 1999a , 2001 , 2004 ; Hogan et al. 2005 ). If effective, this capability would offer the prospect of remotely monitoring cloud microphysical characteristics, providing data useful for understanding meteorological processes, assimilation into numerical weather prediction (NWP) models, validating NWP forecasts and satellite-based retrievals, identifying conditions
determination of these properties is usually based either on measurements (from satellites or airplanes) of reflected radiation within the visible or the near-infrared bands, or on ground measurements of radiation transmitted through the clouds. The scope of these methods is shown in a review by Clothiaux et al. (2005) . Optically thick water clouds allow the presence of the diffuse component of shortwave radiation but not the direct component. The cloud optical depths of such clouds can be evaluated from
determination of these properties is usually based either on measurements (from satellites or airplanes) of reflected radiation within the visible or the near-infrared bands, or on ground measurements of radiation transmitted through the clouds. The scope of these methods is shown in a review by Clothiaux et al. (2005) . Optically thick water clouds allow the presence of the diffuse component of shortwave radiation but not the direct component. The cloud optical depths of such clouds can be evaluated from
