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studies with numerical models have been conducted to investigate the various factors that influence the accuracy of fog simulation and prediction. Musson-Genon (1987) indicated the importance of turbulence in fog formation and evolution. Bergot and Guedalia (1994) pointed out that the radiative balance of the surface layer is a key factor that affects the cooling rate of the surface and the likelihood of fog formation. Siebert et al. (1992) showed that surface vegetation strongly influences fog
studies with numerical models have been conducted to investigate the various factors that influence the accuracy of fog simulation and prediction. Musson-Genon (1987) indicated the importance of turbulence in fog formation and evolution. Bergot and Guedalia (1994) pointed out that the radiative balance of the surface layer is a key factor that affects the cooling rate of the surface and the likelihood of fog formation. Siebert et al. (1992) showed that surface vegetation strongly influences fog
: The operational mesogamma-scale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part I: Overview of the modeling system, the forecast products, and how the products are used . J. Appl. Meteor. Climatol. , 47 , 1077 – 1092 , doi: 10.1175/2007JAMC1653.1 . 10.1175/2007JAMC1653.1 Malek , E. , 2003 : Microclimate of a desert playa: Evaluation of annual radiation, energy, and water budgets components . Int. J. Climatol. , 23 , 333 – 345 , doi: 10.1002/joc.873 . 10
: The operational mesogamma-scale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part I: Overview of the modeling system, the forecast products, and how the products are used . J. Appl. Meteor. Climatol. , 47 , 1077 – 1092 , doi: 10.1175/2007JAMC1653.1 . 10.1175/2007JAMC1653.1 Malek , E. , 2003 : Microclimate of a desert playa: Evaluation of annual radiation, energy, and water budgets components . Int. J. Climatol. , 23 , 333 – 345 , doi: 10.1002/joc.873 . 10
) ES-1; (b) ES-2; (c) ES-3; (d) ES-4; (e) ES-5; (f) tethered-balloon soundings; (g) radiosondes; (h) HOBO weather stations; (i) dividing streamline smoke release located on the northwest side of Granite Mountain; (j) LEMS weather stations; (k) 3D hot-film combo probe; (l) Krypton hygrometer; (m) radiation balance observations at IOS-Playa; (n) radiation balance observations at IOS-Sagebrush; (o) net radiometer as the tower-mounted component of the energy budget; (p) ceilometers; (q) distributed
) ES-1; (b) ES-2; (c) ES-3; (d) ES-4; (e) ES-5; (f) tethered-balloon soundings; (g) radiosondes; (h) HOBO weather stations; (i) dividing streamline smoke release located on the northwest side of Granite Mountain; (j) LEMS weather stations; (k) 3D hot-film combo probe; (l) Krypton hygrometer; (m) radiation balance observations at IOS-Playa; (n) radiation balance observations at IOS-Sagebrush; (o) net radiometer as the tower-mounted component of the energy budget; (p) ceilometers; (q) distributed
et al. 2003 ; Cheng and Steenburgh 2005 ). In this paper, we concentrate on the initialization and parameterization of land surface characteristics and processes, which control the surface energy budget and contribute to NST errors through the inaccurate partitioning of sensible, latent, and ground heat fluxes (e.g., Huang et al. 1996 ; Davis et al. 1999 ; Marshall et al. 2003 ; Reeves et al. 2011 ). In most land surface models (LSMs), land surface parameters (e.g., albedo, roughness length
et al. 2003 ; Cheng and Steenburgh 2005 ). In this paper, we concentrate on the initialization and parameterization of land surface characteristics and processes, which control the surface energy budget and contribute to NST errors through the inaccurate partitioning of sensible, latent, and ground heat fluxes (e.g., Huang et al. 1996 ; Davis et al. 1999 ; Marshall et al. 2003 ; Reeves et al. 2011 ). In most land surface models (LSMs), land surface parameters (e.g., albedo, roughness length
