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al. 2007 , 2016 ). Surface radiative cooling is closely related to the surface energy balance, which is composed of incoming and outgoing radiation and heat fluxes in the atmosphere, canopy, and soil. The complexities involved in the calculation of fluxes are substantial, especially in the case of radiation fog or ice fog. In numerical models, the parameterization of the surface energy balance is strongly related to the parameterizations of both the land surface and boundary layer. Specifically
al. 2007 , 2016 ). Surface radiative cooling is closely related to the surface energy balance, which is composed of incoming and outgoing radiation and heat fluxes in the atmosphere, canopy, and soil. The complexities involved in the calculation of fluxes are substantial, especially in the case of radiation fog or ice fog. In numerical models, the parameterization of the surface energy balance is strongly related to the parameterizations of both the land surface and boundary layer. Specifically
1. Introduction The near-surface atmosphere, namely, the bottom 10% of the atmospheric boundary layer, is unique due to its direct interaction with the earth's surface ( Stull 1988 ). For instance, near-surface temperature is characterized by diurnal variation, with a maximum at local afternoon and a minimum at local midnight. This is very different from the free atmosphere in which temperature shows little diurnal variation. Turbulence causes the wind field in the atmospheric boundary layer
1. Introduction The near-surface atmosphere, namely, the bottom 10% of the atmospheric boundary layer, is unique due to its direct interaction with the earth's surface ( Stull 1988 ). For instance, near-surface temperature is characterized by diurnal variation, with a maximum at local afternoon and a minimum at local midnight. This is very different from the free atmosphere in which temperature shows little diurnal variation. Turbulence causes the wind field in the atmospheric boundary layer
1. Introduction Accurate temperature forecasts by numerical weather prediction (NWP) models are critical for the protection of life and property, economic and operational activities, and routine day-to-day planning. Temperature forecasts not only affect near-surface (2 m) conditions, but also atmospheric stability, planetary boundary layer (PBL) heights, near-surface winds, and precipitation type. Large systematic temperature errors from the Weather Research and Forecasting (WRF) Model are
1. Introduction Accurate temperature forecasts by numerical weather prediction (NWP) models are critical for the protection of life and property, economic and operational activities, and routine day-to-day planning. Temperature forecasts not only affect near-surface (2 m) conditions, but also atmospheric stability, planetary boundary layer (PBL) heights, near-surface winds, and precipitation type. Large systematic temperature errors from the Weather Research and Forecasting (WRF) Model are
GM are approximately 20°C, precluding the need for alternative grid methods such as those described in Lundquist et al. (2012) . Physical parameterizations include the Lin et al. (1983) microphysical scheme, the RRTM longwave radiation parameterization ( Mlawer et al. 1997 ), the Dudhia shortwave radiation parameterization ( Dudhia 1989 ), and the quasi-normal scale elimination (QNSE) planetary boundary layer (PBL) scheme ( Sukoriansky et al. 2005 ). The model also included the improved land
GM are approximately 20°C, precluding the need for alternative grid methods such as those described in Lundquist et al. (2012) . Physical parameterizations include the Lin et al. (1983) microphysical scheme, the RRTM longwave radiation parameterization ( Mlawer et al. 1997 ), the Dudhia shortwave radiation parameterization ( Dudhia 1989 ), and the quasi-normal scale elimination (QNSE) planetary boundary layer (PBL) scheme ( Sukoriansky et al. 2005 ). The model also included the improved land
effect that clouds have on the SEB components, following Massey et al. (2016) , we delineate mostly clear and mostly cloudy days during the MATERHORN periods using atmospheric transmittance, defined as with SW sfc ( t ) being the observed downwelling shortwave (SW) radiation at the surface at time t and SW toa ( t ) being the theoretical downwelling top-of-atmosphere SW calculated from where S 0 is the solar constant (approximated to be 1370 W m −2 ), a is the annual mean distance between the
effect that clouds have on the SEB components, following Massey et al. (2016) , we delineate mostly clear and mostly cloudy days during the MATERHORN periods using atmospheric transmittance, defined as with SW sfc ( t ) being the observed downwelling shortwave (SW) radiation at the surface at time t and SW toa ( t ) being the theoretical downwelling top-of-atmosphere SW calculated from where S 0 is the solar constant (approximated to be 1370 W m −2 ), a is the annual mean distance between the
near the surface, with the lowest half- η level at ~15 m AGL, to ~1250 m in the upper troposphere and lower stratosphere. The physics packages include the Rapid Radiative Transfer Model longwave radiation parameterization ( Mlawer et al. 1997 ), Dudhia shortwave radiation parameterization ( Dudhia 1989 ), Noah LSM ( Chen and Dudhia 2001 ), Yonsei University planetary boundary layer parameterization ( Hong et al. 2006 ), Lin et al. (1983) microphysics, new Kain–Fritsch cumulus parameterization
near the surface, with the lowest half- η level at ~15 m AGL, to ~1250 m in the upper troposphere and lower stratosphere. The physics packages include the Rapid Radiative Transfer Model longwave radiation parameterization ( Mlawer et al. 1997 ), Dudhia shortwave radiation parameterization ( Dudhia 1989 ), Noah LSM ( Chen and Dudhia 2001 ), Yonsei University planetary boundary layer parameterization ( Hong et al. 2006 ), Lin et al. (1983) microphysics, new Kain–Fritsch cumulus parameterization
polarized radiation caused by environmental changes ( Pratt et al. 2014 ). This instrument measured surface moisture at approximately 1-km scale (i.e., mesoscale grid resolution). For both campaigns, a RF-crosshairs system was deployed at the IOS-Gap. Manual soil moisture observations were also conducted at IOS-Playa during the spring campaign to characterize soil moisture spatial variability and its role on the energy balance and land–atmosphere moisture exchange (Hang et al. 2015, manuscript submitted
polarized radiation caused by environmental changes ( Pratt et al. 2014 ). This instrument measured surface moisture at approximately 1-km scale (i.e., mesoscale grid resolution). For both campaigns, a RF-crosshairs system was deployed at the IOS-Gap. Manual soil moisture observations were also conducted at IOS-Playa during the spring campaign to characterize soil moisture spatial variability and its role on the energy balance and land–atmosphere moisture exchange (Hang et al. 2015, manuscript submitted
