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general, ice fog forms through a complex interplay among surface radiative cooling, turbulent mixing in the surface layer, aerosol growth by deliquescence, activation of fog droplets related to the microphysical properties of crystals ( Gultepe et al. 2017a , b ), and mesoscale and microscale variations associated with changes in the landscape, etc. (e.g., snow cover). Because of the complexity of its formation, Gultepe et al. (2015) emphasized the difficulty of forecasting ice fog with numerical
general, ice fog forms through a complex interplay among surface radiative cooling, turbulent mixing in the surface layer, aerosol growth by deliquescence, activation of fog droplets related to the microphysical properties of crystals ( Gultepe et al. 2017a , b ), and mesoscale and microscale variations associated with changes in the landscape, etc. (e.g., snow cover). Because of the complexity of its formation, Gultepe et al. (2015) emphasized the difficulty of forecasting ice fog with numerical
observed SEB components over the Svalbard Archipelago in the Arctic Ocean north of Europe, identifying overpredictions of R n and the Bowen ratio, which they attributed to an underprediction of cloud cover and soil moisture, respectively. A major source of uncertainty with such validation studies, however, is that SEB observations do not close (i.e., R n > H + LE + G ) because of the presence of a residual error term ( Foken 2008 ). This study focuses on the SEB components and associated
observed SEB components over the Svalbard Archipelago in the Arctic Ocean north of Europe, identifying overpredictions of R n and the Bowen ratio, which they attributed to an underprediction of cloud cover and soil moisture, respectively. A major source of uncertainty with such validation studies, however, is that SEB observations do not close (i.e., R n > H + LE + G ) because of the presence of a residual error term ( Foken 2008 ). This study focuses on the SEB components and associated
compiled from a variety of studies (e.g., Cosby et al. 1984 ; Mahfouf et al. 1995 ; Peters-Lidard et al. 1998 ). In 2011 4DWX-DPG was initialized with the standard geographic data available with the community version of the WRF Model, modified to include three additional land-cover categories of playa, white sand, and lava. In 2012, the land-cover and terrain elevation were updated on the basis of the newer 33-category National Land Cover Database dataset ( Fry et al. 2011 ), which increased the
compiled from a variety of studies (e.g., Cosby et al. 1984 ; Mahfouf et al. 1995 ; Peters-Lidard et al. 1998 ). In 2011 4DWX-DPG was initialized with the standard geographic data available with the community version of the WRF Model, modified to include three additional land-cover categories of playa, white sand, and lava. In 2012, the land-cover and terrain elevation were updated on the basis of the newer 33-category National Land Cover Database dataset ( Fry et al. 2011 ), which increased the
playas. The basin in the eastern portion of DPG, labeled “East Basin,” is covered by sparse, low brush and grass with a mean slope of around 0.002%. Differences in soil thermal conductivity and other land surface contrasts between the two basins cause the east basin to develop a CAP that is stronger than its counterpart over the playa ( Rife et al. 2002 ). Separating these two basins is Granite Mountain (GM), which rises up to 800 m above the basin floors. The upper half of GM has numerous slopes
playas. The basin in the eastern portion of DPG, labeled “East Basin,” is covered by sparse, low brush and grass with a mean slope of around 0.002%. Differences in soil thermal conductivity and other land surface contrasts between the two basins cause the east basin to develop a CAP that is stronger than its counterpart over the playa ( Rife et al. 2002 ). Separating these two basins is Granite Mountain (GM), which rises up to 800 m above the basin floors. The upper half of GM has numerous slopes
