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drivers (current output from thunderstorms and other highly electrified clouds); and the distribution of ionosphere–surface column resistance (which depends on ion production from cosmic rays and other energetic particles from the space environment, as well as ionization losses on natural and anthropogenic aerosols). Effects of J z on cloud microphysics arise because J z generates space charge in conductivity gradients because of droplet concentration gradients as it passes through clouds, and the
drivers (current output from thunderstorms and other highly electrified clouds); and the distribution of ionosphere–surface column resistance (which depends on ion production from cosmic rays and other energetic particles from the space environment, as well as ionization losses on natural and anthropogenic aerosols). Effects of J z on cloud microphysics arise because J z generates space charge in conductivity gradients because of droplet concentration gradients as it passes through clouds, and the
1. Introduction Cloud ice and occurrence frequency in the upper troposphere contribute significantly to Earth’s total radiation and energy budgets. However, current climate and weather models produce a wide spread of values for these variables, leading to large uncertainties in the predicted dynamics and precipitation at the surface (e.g., Waliser et al. 2009 ; Eliasson et al. 2011 ; Jiang et al. 2012 ). Improving cloud ice retrieval and modeling is imperative and can be achieved by reducing
1. Introduction Cloud ice and occurrence frequency in the upper troposphere contribute significantly to Earth’s total radiation and energy budgets. However, current climate and weather models produce a wide spread of values for these variables, leading to large uncertainties in the predicted dynamics and precipitation at the surface (e.g., Waliser et al. 2009 ; Eliasson et al. 2011 ; Jiang et al. 2012 ). Improving cloud ice retrieval and modeling is imperative and can be achieved by reducing
1. Introduction For a given patch of sky, the distribution of horizontal cloud sizes plays an important role in setting the total cloud cover (e.g., Koren et al. 2008 ), the cloud radiative forcing (e.g., Marshak and Davis 2005 ), convective entrainment rates (e.g., Stirling and Stratton 2012 ; Neggers 2015 ), and the likelihood of precipitation (e.g., Jiang et al. 2010 ). Despite the importance of the cloud size distribution, it is not often measured directly. Instead, during field
1. Introduction For a given patch of sky, the distribution of horizontal cloud sizes plays an important role in setting the total cloud cover (e.g., Koren et al. 2008 ), the cloud radiative forcing (e.g., Marshak and Davis 2005 ), convective entrainment rates (e.g., Stirling and Stratton 2012 ; Neggers 2015 ), and the likelihood of precipitation (e.g., Jiang et al. 2010 ). Despite the importance of the cloud size distribution, it is not often measured directly. Instead, during field
1. Introduction An understanding of global, three-dimensional clouds, including particle phase and size distributions of cloud particles, is essential for understanding man-made radiative forcing in the atmosphere (see Forster et al. 2007 ). This paper describes a new ground-based instrument to provide such measurements for clouds with optical depths ranging from 0 to ∼10. A network of such sensors could be used to gain information on the global statistical properties of these optically thin
1. Introduction An understanding of global, three-dimensional clouds, including particle phase and size distributions of cloud particles, is essential for understanding man-made radiative forcing in the atmosphere (see Forster et al. 2007 ). This paper describes a new ground-based instrument to provide such measurements for clouds with optical depths ranging from 0 to ∼10. A network of such sensors could be used to gain information on the global statistical properties of these optically thin
1. Introduction In an effort to enhance precipitation, especially in arid regions, glaciogenic cloud seeding has been conducted since the 1940s (e.g., Smith 1949 ; Langmuir 1950 ; Vonnegut and Chessin 1971 ; Hobbs et al. 1981 ; Bruintjes 1999 ). Silver iodide (AgI) has been widely used in both ground-based and airborne seeding because it has a crystal structure that is similar to that of ice ( Vonnegut and Chessin 1971 ) and therefore AgI particles can act as ice nuclei at temperatures
1. Introduction In an effort to enhance precipitation, especially in arid regions, glaciogenic cloud seeding has been conducted since the 1940s (e.g., Smith 1949 ; Langmuir 1950 ; Vonnegut and Chessin 1971 ; Hobbs et al. 1981 ; Bruintjes 1999 ). Silver iodide (AgI) has been widely used in both ground-based and airborne seeding because it has a crystal structure that is similar to that of ice ( Vonnegut and Chessin 1971 ) and therefore AgI particles can act as ice nuclei at temperatures
The World Climate Research Programme's Global Energy and Water Cycle Experiment (GEWEX) addresses both the hydrological and meteorological components of the water cycle. One of the biggest challenges in GEWEX is to improve the understanding of how the wide range of processes within clouds affects the atmosphere on the large scale and, thereby, to develop ways of parameterizing these processes within climate and NWP models. The Joint Scientific Committee of WCRP at its 1992 meeting approved the establishment of a GEWEX Cloud System Study (GCSS) as a long-term program that will address these issues mainly through the development of cloud-resolving models and their use to generate realizations of a set of archetypal cloud systems. The focus of GCSS is on cloud systems spanning the mesoscale rather than on individual clouds. Observations from field programs will be used to develop and validate the cloud-resolving models, which in turn will be used as test-beds to develop the parameterizations for the largescale models. The cloud-resolving models provide synthetic datasets representing rather complete descriptions of entire cloud systems, from which it will also be possible to develop algorithms for remote-sensing observations.
The World Climate Research Programme's Global Energy and Water Cycle Experiment (GEWEX) addresses both the hydrological and meteorological components of the water cycle. One of the biggest challenges in GEWEX is to improve the understanding of how the wide range of processes within clouds affects the atmosphere on the large scale and, thereby, to develop ways of parameterizing these processes within climate and NWP models. The Joint Scientific Committee of WCRP at its 1992 meeting approved the establishment of a GEWEX Cloud System Study (GCSS) as a long-term program that will address these issues mainly through the development of cloud-resolving models and their use to generate realizations of a set of archetypal cloud systems. The focus of GCSS is on cloud systems spanning the mesoscale rather than on individual clouds. Observations from field programs will be used to develop and validate the cloud-resolving models, which in turn will be used as test-beds to develop the parameterizations for the largescale models. The cloud-resolving models provide synthetic datasets representing rather complete descriptions of entire cloud systems, from which it will also be possible to develop algorithms for remote-sensing observations.
1. Introduction There are 10 basic cloud types, grouped into three primary categories: high clouds, mid clouds, and low clouds ( http://www.srh.noaa.gov/srh/jetstream/clouds/cloudwise/types.html ). The focus of this study is to characterize the macrophysical and microphysical properties of the high clouds: cirrus. The motivation for this chapter comes from workshops conducted on “Data Analysis and Presentation of Cloud Microphysical Measurements” in Seaside, Oregon, in 2010, at the Swiss
1. Introduction There are 10 basic cloud types, grouped into three primary categories: high clouds, mid clouds, and low clouds ( http://www.srh.noaa.gov/srh/jetstream/clouds/cloudwise/types.html ). The focus of this study is to characterize the macrophysical and microphysical properties of the high clouds: cirrus. The motivation for this chapter comes from workshops conducted on “Data Analysis and Presentation of Cloud Microphysical Measurements” in Seaside, Oregon, in 2010, at the Swiss
1. Introduction Warm convective clouds play an essential role in the energy and moisture redistribution in the boundary layer and serve as the seed for deep convection. However, representation of these clouds in large-scale models is affected by the discrepancy between coarse model grid size and small cloud size, and large uncertainty in cloud modeling. Many traditional cumulus parameterization schemes focus on “relating the statistical properties of a cumulus cloud ensemble to the large
1. Introduction Warm convective clouds play an essential role in the energy and moisture redistribution in the boundary layer and serve as the seed for deep convection. However, representation of these clouds in large-scale models is affected by the discrepancy between coarse model grid size and small cloud size, and large uncertainty in cloud modeling. Many traditional cumulus parameterization schemes focus on “relating the statistical properties of a cumulus cloud ensemble to the large
1. Introduction The effects of precipitation on shallow convection have drawn much attention in recent years because of the desire to understand and quantify aerosol effects on clouds and climate. The conceptual framework for studies of this type follows a simple train of thought: Greater aerosol loading suppresses precipitation formation, increases cloud liquid water, and leads to longer-lived clouds and larger cloud fractions. Because the albedo effect of shallow clouds is greater than their
1. Introduction The effects of precipitation on shallow convection have drawn much attention in recent years because of the desire to understand and quantify aerosol effects on clouds and climate. The conceptual framework for studies of this type follows a simple train of thought: Greater aerosol loading suppresses precipitation formation, increases cloud liquid water, and leads to longer-lived clouds and larger cloud fractions. Because the albedo effect of shallow clouds is greater than their
1. Introduction The indirect aerosol effect (IAE) continues to be the largest climate uncertainty ( Alley et al. 2007 ). It has been well established that cloud condensation nuclei (CCN) concentrations ( N CCN ) have a large influence on cloud microphysics (e.g., Squires 1956 , 1958 ; Twomey and Warner 1967 ; Yum and Hudson 2002 ). Sometimes the aerosol influence is obvious, such as with ship tracks ( Hobbs et al. 2000 ; Hudson et al. 2000 ), and sometimes aerosol influence appears to be
1. Introduction The indirect aerosol effect (IAE) continues to be the largest climate uncertainty ( Alley et al. 2007 ). It has been well established that cloud condensation nuclei (CCN) concentrations ( N CCN ) have a large influence on cloud microphysics (e.g., Squires 1956 , 1958 ; Twomey and Warner 1967 ; Yum and Hudson 2002 ). Sometimes the aerosol influence is obvious, such as with ship tracks ( Hobbs et al. 2000 ; Hudson et al. 2000 ), and sometimes aerosol influence appears to be
