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these two active remote sensors, when combined with the passive remote sensors of the A-Train constellation ( Stephens et al. 2008 ), have provided an unprecedented global view of clouds ( Sassen et al. 2009 ; G. G. Mace et al. 2009 ) and precipitation ( Stephens et al. 2010 ) and also motivated development of a series of cloud property retrieval algorithms using various combinations of radar, lidar, and radiometer measurements ( Austin and Stephens 2001 ; Hogan et al. 2006 ; Young and Vaughan
these two active remote sensors, when combined with the passive remote sensors of the A-Train constellation ( Stephens et al. 2008 ), have provided an unprecedented global view of clouds ( Sassen et al. 2009 ; G. G. Mace et al. 2009 ) and precipitation ( Stephens et al. 2010 ) and also motivated development of a series of cloud property retrieval algorithms using various combinations of radar, lidar, and radiometer measurements ( Austin and Stephens 2001 ; Hogan et al. 2006 ; Young and Vaughan
Resolution Imaging Spectroradiometer (MODIS) operational cloud-property retrieval is a bispectral method employing solar-reflectance bands ( Platnick et al. 2003 ). The premise of this approach is that a weakly absorbing, visible or near-infrared window band (VIS/NIR) (e.g., 0.64 or 0.86 μ m) is sensitive mainly to τ , whereas an ice absorbing shortwave infrared (SWIR) band (e.g., 1.6 or 2.13 μ m) is sensitive to both D eff and τ . To be more specific, in a VIS band, scattering is dominant so that
Resolution Imaging Spectroradiometer (MODIS) operational cloud-property retrieval is a bispectral method employing solar-reflectance bands ( Platnick et al. 2003 ). The premise of this approach is that a weakly absorbing, visible or near-infrared window band (VIS/NIR) (e.g., 0.64 or 0.86 μ m) is sensitive mainly to τ , whereas an ice absorbing shortwave infrared (SWIR) band (e.g., 1.6 or 2.13 μ m) is sensitive to both D eff and τ . To be more specific, in a VIS band, scattering is dominant so that
each instrument to sample the physical state within some types of cloud regimes is still poorly understood ( Fetzer et al. 2006 , 2008 ) and must be compared further before the derived geophysical variables reach a nominal level of maturity for application to climate monitoring and trends. Furthermore, before AIRS and MODIS can be combined together in joint retrievals of surface and/or atmospheric properties (e.g., L’Ecuyer et al. 2006 ; Li et al. 2005 ), a rigorous intercomparison of retrieval
each instrument to sample the physical state within some types of cloud regimes is still poorly understood ( Fetzer et al. 2006 , 2008 ) and must be compared further before the derived geophysical variables reach a nominal level of maturity for application to climate monitoring and trends. Furthermore, before AIRS and MODIS can be combined together in joint retrievals of surface and/or atmospheric properties (e.g., L’Ecuyer et al. 2006 ; Li et al. 2005 ), a rigorous intercomparison of retrieval
). While these studies have been useful in highlighting specific sources of bias, significant uncertainty remains in both retrieval methods. There is a need to identify and characterize independent remote sensing methods to constrain W cld to resolve these lingering discrepancies around this essential climate variable. Previously, Lebsock et al. (2011) used measurements of the CloudSat path-integrated attenuation (PIA) to derive W cld for shallow clouds over the ocean. They used a surface
). While these studies have been useful in highlighting specific sources of bias, significant uncertainty remains in both retrieval methods. There is a need to identify and characterize independent remote sensing methods to constrain W cld to resolve these lingering discrepancies around this essential climate variable. Previously, Lebsock et al. (2011) used measurements of the CloudSat path-integrated attenuation (PIA) to derive W cld for shallow clouds over the ocean. They used a surface
because they can significantly impact the quality of the analysis. There are many approaches to detect clouds, but not all meet the constraints of operational assimilation. The need for robustness and speed makes collocation between different instruments difficult in real time. An alternative method is proposed in this paper for efficient retrieval of information about the clouds and identification of clear channels. This paper is organized in the following manner. The description of the new method is
because they can significantly impact the quality of the analysis. There are many approaches to detect clouds, but not all meet the constraints of operational assimilation. The need for robustness and speed makes collocation between different instruments difficult in real time. An alternative method is proposed in this paper for efficient retrieval of information about the clouds and identification of clear channels. This paper is organized in the following manner. The description of the new method is
from various general circulation models (GCMs) and in model comparisons with flux measurements ( Potter and Cess 2004 ; Webb et al. 2001 ). To increase confidence in climate prediction, global observations on fine spatial and temporal scales from satellite-based instruments are needed to retrieve ice cloud properties such as effective particle size and cloud optical thickness, which are essential to climate studies ( Wielicki et al. 1995 ). Methodologies have been developed for daytime retrievals
from various general circulation models (GCMs) and in model comparisons with flux measurements ( Potter and Cess 2004 ; Webb et al. 2001 ). To increase confidence in climate prediction, global observations on fine spatial and temporal scales from satellite-based instruments are needed to retrieve ice cloud properties such as effective particle size and cloud optical thickness, which are essential to climate studies ( Wielicki et al. 1995 ). Methodologies have been developed for daytime retrievals
weather phenomena passing over the radar sites. Airborne Doppler radars enable the study of clouds and precipitation structure over remote locations and oceans ( Heymsfield et al. 1996 ; French et al. 1999 ; Guimond et al. 2010 ; Lorsolo et al. 2010 ; Rogers et al. 2013 ). Because of their compact design, millimeter wavelength Doppler (cloud) radars operating at 95 GHz are particularly well suited for use on aircraft (e.g., Li et al. 2004 ; Kollias et al. 2007 ). The retrieval of the vertical
weather phenomena passing over the radar sites. Airborne Doppler radars enable the study of clouds and precipitation structure over remote locations and oceans ( Heymsfield et al. 1996 ; French et al. 1999 ; Guimond et al. 2010 ; Lorsolo et al. 2010 ; Rogers et al. 2013 ). Because of their compact design, millimeter wavelength Doppler (cloud) radars operating at 95 GHz are particularly well suited for use on aircraft (e.g., Li et al. 2004 ; Kollias et al. 2007 ). The retrieval of the vertical
resulting from the different scan period (i.e., 30 and 15 min, respectively). By contrast, the time difference between Meteosat-5 and Meteosat - 7 observations is small (on average less than ±10 s, except for regions toward the image borders). Hence, it is not obvious that by replacing Meteosat-7 with Meteosat-8 HRV observations, the CTH accuracy would increase as expected by Campbell and Holmlund (2004) . This paper extends the multiview cloud-top height and motion retrievals described in
resulting from the different scan period (i.e., 30 and 15 min, respectively). By contrast, the time difference between Meteosat-5 and Meteosat - 7 observations is small (on average less than ±10 s, except for regions toward the image borders). Hence, it is not obvious that by replacing Meteosat-7 with Meteosat-8 HRV observations, the CTH accuracy would increase as expected by Campbell and Holmlund (2004) . This paper extends the multiview cloud-top height and motion retrievals described in
of several years of cloud vertical structure (CVS) retrievals from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) now makes it possible to investigate the relationship of CVS and the WSs. The study of Zhang et al. (2007) derived tropical cloud clusters applying the same clustering technique to CloudSat histograms of CVSs and found good correspondence and clear physical connections between those clusters and the ones derived from ISCCP PC
of several years of cloud vertical structure (CVS) retrievals from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) now makes it possible to investigate the relationship of CVS and the WSs. The study of Zhang et al. (2007) derived tropical cloud clusters applying the same clustering technique to CloudSat histograms of CVSs and found good correspondence and clear physical connections between those clusters and the ones derived from ISCCP PC
only be applied during daytime. Because outgoing surface radiance significantly contributes to the radiance measured by a satellite instrument, thermally based cloud-phase retrievals are sensitive to errors when detecting optically thin and broken clouds. In addition, brightness temperature (BT) thresholding affects cloud phase by assuming a sudden transition from water to ice clouds below a certain temperature threshold, whereas in reality this transition depends on, for example, cloud dynamics
only be applied during daytime. Because outgoing surface radiance significantly contributes to the radiance measured by a satellite instrument, thermally based cloud-phase retrievals are sensitive to errors when detecting optically thin and broken clouds. In addition, brightness temperature (BT) thresholding affects cloud phase by assuming a sudden transition from water to ice clouds below a certain temperature threshold, whereas in reality this transition depends on, for example, cloud dynamics
