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Feimin Zhang and Zhaoxia Pu

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

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Jeffrey D. Massey, W. James Steenburgh, Sebastian W. Hoch, and Derek D. Jensen

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

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Jeffrey D. Massey, W. James Steenburgh, Sebastian W. Hoch, and Jason C. Knievel

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

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Hailing Zhang, Zhaoxia Pu, and Xuebo Zhang

spacing are generally incremental. With a regional climate simulation over complex terrain, Leung and Qian (2003) found that a higher-resolution simulation improves not only the spatial distribution and regional mean precipitation during summer but also snowpack during winter. However, they also commented that the accuracy of snow simulation is limited by factors such as deficiencies in the land surface model or biases in other model variables. The disagreement between these different studies

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Jeffrey D. Massey, W. James Steenburgh, Jason C. Knievel, and William Y. Y. Cheng

), Noah LSM ( Chen and Dudhia 2001 ), Yonsei University PBL parameterization (YSU; Hong et al. 2006 ), explicit sixth-order numerical diffusion ( Knievel et al. 2007 ), and the new Kain–Fritsch cumulus parameterization ( Kain 2004 ). The latter was used only in the 30- and 10-km domains through 2013, then on the 3.3-km domain thereafter. Thompson et al. (2004) microphysics replaced the Lin et al. (1983) microphysics prior to September and October 2012. Rayleigh damping and an updated land cover

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Matthew E. Jeglum, Sebastian W. Hoch, Derek D. Jensen, Reneta Dimitrova, and Zachariah Silver

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

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