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  • Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) x
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Jeffrey D. Massey, W. James Steenburgh, Sebastian W. Hoch, and Jason C. Knievel

, and soil porosity) are specified using land-use and soil-type databases, whereas soil moisture and temperature are derived from observational data and/or land surface modeling. In either case, the incorrect specification of these land surface characteristics is at least partly responsible for NST forecast errors (e.g., Huang et al. 1996 ; Dirmeyer et al. 2000 ; Rife et al. 2004 ; Wen et al. 2012 ). Soil moisture is an important initialized variable because it strongly influences NSTs, surface

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

1. Introduction The variability of regional land surface characteristics in mesoscale numerical weather prediction (NWP) models has a potentially strong influence on near-surface forecasts. Some sources of land surface variability, such as coastlines and topographic features, are easily represented in NWP models, but other more subtle land surface characteristics (e.g., albedo, emissivity, roughness length, soil porosity, soil texture, and soil moisture) are more difficult to specify and

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Feimin Zhang and Zhaoxia Pu

formation by changing the surface heat and moisture budget. Results from Steeneveld et al. (2015) showed that boundary layer formulation is critical for forecasting fog onset. Recent studies also found that inaccurate forecasts of near-surface atmospheric conditions are associated mostly with the failure of fog prediction in many cases ( Pu et al. 2016 ; Chachere and Pu 2019 ). Despite these various factors that contribute to the inaccurate numerical prediction of fog events, different processes

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