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

1. Introduction Accurate temperature forecasts by numerical weather prediction (NWP) models are critical for the protection of life and property, economic and operational activities, and routine day-to-day planning. Temperature forecasts not only affect near-surface (2 m) conditions, but also atmospheric stability, planetary boundary layer (PBL) heights, near-surface winds, and precipitation type. Large systematic temperature errors from the Weather Research and Forecasting (WRF) Model are

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Feimin Zhang and Zhaoxia Pu

weather prediction (NWP; e.g., Golding 1993 ; Meyer and Rao 1999 ; Gultepe et al. 2016 ; Pu et al. 2016 ; Pu 2017 ). Zhou et al. (2012) evaluated the performance of low visibility/fog predictions over North America using the National Centers for Environmental Prediction (NCEP) operational forecast models. Results showed that the accuracy of visibility/fog forecasts from these models was poor in comparison to the accuracy of operational precipitation forecasts from the same models. Previous

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

1. Introduction Near-surface (2 m) temperature (NST) forecasts are critical for the protection of life and property, for economic and operational activities, and for routine day-to-day planning but remain a major challenge for numerical weather prediction. Modeling systems in many regions of the world have trouble simulating NSTs and typically underpredict the diurnal NST cycle, which largely reflects a pronounced nighttime NST warm bias (e.g., Steeneveld et al. 2008 ; Edwards et al. 2011

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

the supplemental materials of Fernando et al. (2015) . To supplement the observations, numerical simulations were conducted using the Weather Research and Forecasting (WRF) Model, version 3.4.1 ( Skamarock et al. 2008 ). The model configuration included four one-way nested domains, 50 vertical levels (22 levels below 600 m), and a horizontal resolution of 500 m in the innermost domain, which is a 60-km square centered on the east slope of GM. At 500-m horizontal resolution, the steepest slopes on

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

anemometer-height observations contain potentially useful information for both forecasters and NWP model initialization. Surface observation networks could conceivably be designed to improve fog forecasts in regions particularly susceptible. At the heart of the network design is an understanding of numerical forecast sensitivity to initial-condition analysis perturbations that result from assimilating proposed hypothetical observations. One candidate method for quantifying forecast sensitivity to

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Joshua P. Hacker and Lili Lei

new observation can affect a forecast by assimilating it at analysis time, or understanding the impacts of existing observation sets on forecast skill. Deploying a new observation based on the predicted response (e.g., error reduction) is usually called targeting . Singular vector targeting ( Buizza and Montani 1999 ; Gelaro et al. 1999 ; Langland et al. 1999 ), which makes use of an adjoint, and ensemble-based targeting ( Bishop and Toth 1999 ; Bishop et al. 2001 ; Hamill and Snyder 2002

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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

Comprehensive, multiscale, and multidisciplinary observations allow scientists to discover novel flow physics, address current deficiencies of predictive models, and improve weather prediction in mountainous terrain. Through woods and mountain passes the winds, like anthems, roll. —Henry Wadsworth Longfellow For centuries, humans have been both fascinated and awed by mountain weather, and its intriguing aberrancy continues to baffle weather forecasters. For instance, a clear morning on a

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

mesoscale atmospheric model was done by Colette et al. (2003) in the Advanced Regional Prediction System (ARPS). Since then, other models have included topographic effects on radiation. These include the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Zängl 2005 ) and the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), which is used in this study. Although topographic shading improves the representation of surface

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