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

) reduced the early morning warm bias in WRF simulations of three fall days over northern Utah through the use of an alternate soil thermal conductivity parameterization combined with soil moisture analyses based on in situ observations. The afternoon cold bias, however, was only partially reduced in Massey et al. (2014) and remains associated with a myriad of plausible, yet untested, error sources including 1) differences between model and observation site elevations ( Liu et al. 2008b

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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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Robert S. Arthur, Katherine A. Lundquist, Jeffrey D. Mirocha, and Fotini K. Chow

are also initialized following Lundquist et al. (2010) . These include spatially uniform values of soil type (sandy loam), land use (savannah), and vegetation fraction (0.1). The initial soil temperature is equal to the initial surface atmospheric temperature, and the soil moisture is set to 0.0868 m 3 m −3 (a 20% saturation rate for sandy loam). Lateral boundary conditions are periodic for all variables. At the immersed boundary, either Dirichlet or Neumann boundary conditions can be used

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H. J. S. Fernando, E. R. Pardyjak, S. Di Sabatino, F. K. Chow, S. F. J. De Wekker, S. W. Hoch, J. Hacker, J. C. Pace, T. Pratt, Z. Pu, W. J. Steenburgh, C. D. Whiteman, Y. Wang, D. Zajic, B. Balsley, R. Dimitrova, G. D. Emmitt, C. W. Higgins, J. C. R. Hunt, J. C. Knievel, D. Lawrence, Y. Liu, D. F. Nadeau, E. Kit, B. W. Blomquist, P. Conry, R. S. Coppersmith, E. Creegan, M. Felton, A. Grachev, N. Gunawardena, C. Hang, C. M. Hocut, G. Huynh, M. E. Jeglum, D. Jensen, V. Kulandaivelu, M. Lehner, L. S. Leo, D. Liberzon, J. D. Massey, K. McEnerney, S. Pal, T. Price, M. Sghiatti, Z. Silver, M. Thompson, H. Zhang, and T. Zsedrovits

instrumented UAV, sensors for moisture and fog measurements, and a combined hot-film/sonic anemometer system for probing turbulence down to Kolmogorov scales. Advanced data retrieval and processing algorithms are also attempted. The parameterization component (MATERHORN-P) develops high-fidelity physics-based fundamental (quantitative) relationships for complex-terrain processes, which are implemented in mesoscale models followed by model evaluations. The Granite Mountain Atmospheric Science Testbed of the

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Sean M. Wile, Joshua P. Hacker, and Kenneth H. Chilcoat

radiative cooling at KSLC; precipitation falling into a weakened inversion and shallow cold pool case, where precipitation increased low-level moisture supporting fog formation; and shallow cold pool advecting from the GSL case, where radiative cooling over the lake enabled fog to form and later move over KSLC. b. Dense fog event At 2243 UTC 23 January 2009, dense fog developed over KSLC, forcing the closure of one runway and prompting the National Weather Service to issue a dense fog warning

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Hailing Zhang, Zhaoxia Pu, and Xuebo Zhang

(ABL) and the near-surface to behave differently from that in the free atmosphere because the ABL transports momentum, heat, and moisture between the earth's surface and the air above. Due to its unique features, accurate forecasts of near-surface atmospheric conditions are very important in many applications such as wind energy, agriculture, aviation, and fire weather forecasts. However, difficulties in forecasting near-surface variables such as temperature and wind have long been recognized and

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Raquel Lorente-Plazas and Joshua P. Hacker

: The GEOS moisture analysis . Mon. Wea. Rev. , 128 , 3268 – 3282 , doi: 10.1175/1520-0493(2000)128<3268:DAITPO>2.0.CO;2 . 10.1175/1520-0493(2000)128<3268:DAITPO>2.0.CO;2 Dee , D. P. , and S. Uppala , 2009 : Variational bias correction of satellite radiance data in the ERA-Interim reanalysis . Quart. J. Roy. Meteor. Soc. , 135 , 1830 – 1841 , doi: 10.1002/qj.493 . 10.1002/qj.493 Derber , J. C. , and W.-S. Wu , 1998 : The use of TOVS cloud-cleared radiances in the NCEP SSI analysis

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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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