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for the evaluation of atmospheric numerical model simulations and their parameterized ice-phase microphysics (e.g., Delanoë et al. 2011 ; Stein et al. 2015 ; Ori et al. 2020 ). Despite many advances in satellite remote sensing techniques and sensors in the past few decades, the uncertainty in the estimate of the atmosphere’s ice water path remains large, and there is poor agreement between observational retrievals and numerical models (e.g., Duncan and Eriksson 2018 ). The best way to retrieve
for the evaluation of atmospheric numerical model simulations and their parameterized ice-phase microphysics (e.g., Delanoë et al. 2011 ; Stein et al. 2015 ; Ori et al. 2020 ). Despite many advances in satellite remote sensing techniques and sensors in the past few decades, the uncertainty in the estimate of the atmosphere’s ice water path remains large, and there is poor agreement between observational retrievals and numerical models (e.g., Duncan and Eriksson 2018 ). The best way to retrieve
1. Introduction Numerical climate and land–atmosphere models are widely used for providing land–atmospheric predictions at different time scales. These models typically capture both atmospheric thermodynamic processes and cloud microphysics to predict the dynamics of land–atmosphere water and energy fluxes. To improve the predictions of land–atmosphere state variables and parameters, a common practice is to assimilate observations from in situ gauges, radiosondes, and satellite measurements
1. Introduction Numerical climate and land–atmosphere models are widely used for providing land–atmospheric predictions at different time scales. These models typically capture both atmospheric thermodynamic processes and cloud microphysics to predict the dynamics of land–atmosphere water and energy fluxes. To improve the predictions of land–atmosphere state variables and parameters, a common practice is to assimilate observations from in situ gauges, radiosondes, and satellite measurements
extending from the surface to 50 hPa. The physics option follows the one outlined in Lin et al. (2015) , including the WRF single-moment 3-class microphysics scheme ( Hong et al. 2004 ), the Rapid Radiative Transfer Model for longwave radiation ( Mlawer et al. 1997 ), the Dudhia shortwave radiation ( Dudhia 1989 ), the unified Noah land surface model ( Chen and Dudhia 2001 ), the revised MM5 Monin–Obukhov surface layer scheme, the Yonsei University (YSU) planetary boundary layer ( Hong et al. 2006
extending from the surface to 50 hPa. The physics option follows the one outlined in Lin et al. (2015) , including the WRF single-moment 3-class microphysics scheme ( Hong et al. 2004 ), the Rapid Radiative Transfer Model for longwave radiation ( Mlawer et al. 1997 ), the Dudhia shortwave radiation ( Dudhia 1989 ), the unified Noah land surface model ( Chen and Dudhia 2001 ), the revised MM5 Monin–Obukhov surface layer scheme, the Yonsei University (YSU) planetary boundary layer ( Hong et al. 2006
capable of simulating three-dimensional radiative transfer primarily in the microwave to infrared parts of the spectrum using a variety of different instrument geometries ( Eriksson et al. 2011 ; Buehler et al. 2018 ). ARTS can handle scattering via the discrete ordinate iterative method or Monte Carlo integration. Monte Carlo integration is used here because it is more appropriate for three-dimensional calculations. The background atmosphere for our simulations was derived from the Fort Worth
capable of simulating three-dimensional radiative transfer primarily in the microwave to infrared parts of the spectrum using a variety of different instrument geometries ( Eriksson et al. 2011 ; Buehler et al. 2018 ). ARTS can handle scattering via the discrete ordinate iterative method or Monte Carlo integration. Monte Carlo integration is used here because it is more appropriate for three-dimensional calculations. The background atmosphere for our simulations was derived from the Fort Worth
would help mitigate ambiguities in the ice-to-rain relationship. In an attempt to do so, this study seeks to utilize more-complex links between observed cloud properties and common atmospheric parameters (e.g., the large-scale environment). Based on findings presented in PK2017 , it is hypothesized that such information is correlated with the synoptic state of the atmosphere. Earlier studies suggest that parameters such as temperature and humidity, atmospheric stability ( Behrangi et al. 2015
would help mitigate ambiguities in the ice-to-rain relationship. In an attempt to do so, this study seeks to utilize more-complex links between observed cloud properties and common atmospheric parameters (e.g., the large-scale environment). Based on findings presented in PK2017 , it is hypothesized that such information is correlated with the synoptic state of the atmosphere. Earlier studies suggest that parameters such as temperature and humidity, atmospheric stability ( Behrangi et al. 2015
Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), 4) Kwajalein Experiment (KWAJEX), 5) Tropical Warm Pool–International Cloud Experiment (TWP-ICE), 6) Midlatitude Continental Convective Clouds Experiment (MC3E), 7) Dynamics of the MJO (DYNAMO), 8) Green Ocean Amazon Experiment (GoAMAZON), and 9) the U.S. Department of Energy’s Atmospheric Radiation Measurement Southern Great Plains site (1997 and 2002). These field campaigns are used to provide large-scale advective tendencies in
Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), 4) Kwajalein Experiment (KWAJEX), 5) Tropical Warm Pool–International Cloud Experiment (TWP-ICE), 6) Midlatitude Continental Convective Clouds Experiment (MC3E), 7) Dynamics of the MJO (DYNAMO), 8) Green Ocean Amazon Experiment (GoAMAZON), and 9) the U.S. Department of Energy’s Atmospheric Radiation Measurement Southern Great Plains site (1997 and 2002). These field campaigns are used to provide large-scale advective tendencies in
