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1. Introduction Ice clouds are ubiquitous and have significant impacts on the Earth radiation budget and hydrological cycle, which have been extensively studied for several decades based on numerical radiative transfer models (e.g., Liou 1986 ; Lohmann and Roeckner 1995 ; Waliser et al. 2009 ; Yang et al. 2015 ) and remote sensing techniques (e.g., Platnick et al. 2003 ; Sassen and Comstock 2001 ; Sassen et al. 2008 ; Yang et al. 2018 ). The single-scattering properties of ice crystals
1. Introduction Ice clouds are ubiquitous and have significant impacts on the Earth radiation budget and hydrological cycle, which have been extensively studied for several decades based on numerical radiative transfer models (e.g., Liou 1986 ; Lohmann and Roeckner 1995 ; Waliser et al. 2009 ; Yang et al. 2015 ) and remote sensing techniques (e.g., Platnick et al. 2003 ; Sassen and Comstock 2001 ; Sassen et al. 2008 ; Yang et al. 2018 ). The single-scattering properties of ice crystals
physical parameters related to rapid atmospheric adjustments and climate feedbacks (e.g., atmospheric temperatures). Radiative kernels therefore have a wide variety of applications, since they can be employed to translate a change in climate state to a quantifiable perturbation of Earth’s energy balance without the need for explicit radiative transfer calculations (e.g., Soden and Held 2006 ; Solomon et al. 2010 ; Rap et al. 2015 ; Riese et al. 2012 ; Iglesias-Suarez et al. 2018 ; Smith et al
physical parameters related to rapid atmospheric adjustments and climate feedbacks (e.g., atmospheric temperatures). Radiative kernels therefore have a wide variety of applications, since they can be employed to translate a change in climate state to a quantifiable perturbation of Earth’s energy balance without the need for explicit radiative transfer calculations (e.g., Soden and Held 2006 ; Solomon et al. 2010 ; Rap et al. 2015 ; Riese et al. 2012 ; Iglesias-Suarez et al. 2018 ; Smith et al
OCTOaER 1979 KUO-NAN LIOU AND SZU-CHENG OU 1985~nfrared Radiative Transfer in lFfi~fite Cloud Layers Kuo-NAN LIOU AND Szu-CHENG OUDepartment of Meteorology, University of Utah, Salt Lake City 84112(Manuscript received 3 November 1978, in final form 17 May 1979) ABSTRACT Analytic solutions to the three-dimensional infrared radiative transfer equation for an anisotropic
OCTOaER 1979 KUO-NAN LIOU AND SZU-CHENG OU 1985~nfrared Radiative Transfer in lFfi~fite Cloud Layers Kuo-NAN LIOU AND Szu-CHENG OUDepartment of Meteorology, University of Utah, Salt Lake City 84112(Manuscript received 3 November 1978, in final form 17 May 1979) ABSTRACT Analytic solutions to the three-dimensional infrared radiative transfer equation for an anisotropic
1M^Y 1987 HOWARD P. HANSON 1287Radiative/Turbulent Transfer Interactions in Layer Clouds HOWARD P. HANSONCooperative Institute for Research in Environmental Sciences, University of Colorado/NOAA, Boulder, CO 80309(Manuscript received 4 June 1985; in final form 12 November 1986) ABSTRACT The differential absorption and emission of radiation with height inside clouds creates
1M^Y 1987 HOWARD P. HANSON 1287Radiative/Turbulent Transfer Interactions in Layer Clouds HOWARD P. HANSONCooperative Institute for Research in Environmental Sciences, University of Colorado/NOAA, Boulder, CO 80309(Manuscript received 4 June 1985; in final form 12 November 1986) ABSTRACT The differential absorption and emission of radiation with height inside clouds creates
; Di Giuseppe and Tompkins 2005 ). The 3D radiative transfer effect on domain-averaged solar fluxes has been divided into two physical processes, as summarized by Várnai and Davies (1999) . The first, which is termed the “one-dimensional (1D) heterogeneity effect,” arises from the nonlinear relationship between cloud optical depth and albedo. The mean transmission of a cloud with horizontally varying optical depth is more than the transmission of a uniform cloud with the mean optical depth. As a
; Di Giuseppe and Tompkins 2005 ). The 3D radiative transfer effect on domain-averaged solar fluxes has been divided into two physical processes, as summarized by Várnai and Davies (1999) . The first, which is termed the “one-dimensional (1D) heterogeneity effect,” arises from the nonlinear relationship between cloud optical depth and albedo. The mean transmission of a cloud with horizontally varying optical depth is more than the transmission of a uniform cloud with the mean optical depth. As a
. This treatment of overlap is important for nonlocal cloud processes such as precipitation evaporation ( Jakob and Klein 1999 ) and radiation transfer ( Morcrette and Jakob 2000 ; Chen et al. 2000 ; Barker and Räisänen 2005 ). In particular, shortwave (SW) radiative transfer is further complicated by the fact that the effective total cloud cover (TCC) as appreciated by an unscattered photon ultimately depends on the solar zenith angle (SZA). At low sun angles, photons have a reduced chance of
. This treatment of overlap is important for nonlocal cloud processes such as precipitation evaporation ( Jakob and Klein 1999 ) and radiation transfer ( Morcrette and Jakob 2000 ; Chen et al. 2000 ; Barker and Räisänen 2005 ). In particular, shortwave (SW) radiative transfer is further complicated by the fact that the effective total cloud cover (TCC) as appreciated by an unscattered photon ultimately depends on the solar zenith angle (SZA). At low sun angles, photons have a reduced chance of
channels. WindSAT/Coriolis and the Conical Microwave Imager Sounder (CMIS) aboard future U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) platforms will provide the measurements of Stokes vector at lower frequencies in addition to the polarization measurements at higher frequencies. The data can be best utilized in physical retrieval algorithms ( Kummerow et al. 1989 ; Petty 1994 ) if a fast and accurate radiative transfer model is available. Atmospheric events produce polarization
channels. WindSAT/Coriolis and the Conical Microwave Imager Sounder (CMIS) aboard future U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) platforms will provide the measurements of Stokes vector at lower frequencies in addition to the polarization measurements at higher frequencies. The data can be best utilized in physical retrieval algorithms ( Kummerow et al. 1989 ; Petty 1994 ) if a fast and accurate radiative transfer model is available. Atmospheric events produce polarization
1. Introduction The development of fast and accurate thermal infrared (IR) radiative transfer (RT) models for clear atmospheric conditions has enabled the direct assimilation of satellite-based radiance measurements in numerical weather prediction (NWP) models. Most fast RT models are based on fixed transmittance coefficients that relate atmospheric conditions to optical properties. One such fast RT model is the Community Radiative Transfer Model (CRTM; Weng et al. 2005 ; Han et al. 2006
1. Introduction The development of fast and accurate thermal infrared (IR) radiative transfer (RT) models for clear atmospheric conditions has enabled the direct assimilation of satellite-based radiance measurements in numerical weather prediction (NWP) models. Most fast RT models are based on fixed transmittance coefficients that relate atmospheric conditions to optical properties. One such fast RT model is the Community Radiative Transfer Model (CRTM; Weng et al. 2005 ; Han et al. 2006
; Kraas et al. 2013 ). However, more recently, studies with direct outputs of DNI, as calculated by the respective radiative transfer schemes, have been made available through Weather Research and Forecasting (WRF) Model developments and evaluations under clear skies (e.g., albeit primarily in North America; Ruiz-Arias et al. 2013b , 2014 ; Jimenez et al. 2016 ). In previous studies where DNI used to be evaluated, it was derived from component separation of the global irradiance ( Lara-Fanego et al
; Kraas et al. 2013 ). However, more recently, studies with direct outputs of DNI, as calculated by the respective radiative transfer schemes, have been made available through Weather Research and Forecasting (WRF) Model developments and evaluations under clear skies (e.g., albeit primarily in North America; Ruiz-Arias et al. 2013b , 2014 ; Jimenez et al. 2016 ). In previous studies where DNI used to be evaluated, it was derived from component separation of the global irradiance ( Lara-Fanego et al
al. 2000 ). In this study, we focus on the striping related to the differences in spectral response and geometry among detectors, because such a striping is a real instrument artifact. Both spectral response difference and geometric difference can be considered in radiative transfer calculations. Removing the striping may cause the inconsistency between measurements and radiative transfer calculations, which can be an issue in direct radiance assimilation. The successful launch of the Suomi
al. 2000 ). In this study, we focus on the striping related to the differences in spectral response and geometry among detectors, because such a striping is a real instrument artifact. Both spectral response difference and geometric difference can be considered in radiative transfer calculations. Removing the striping may cause the inconsistency between measurements and radiative transfer calculations, which can be an issue in direct radiance assimilation. The successful launch of the Suomi
