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Abstract Measurements made in the Columbia River Basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modelled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin-Obukhov similarity and (ii) a modified Priestley-Taylor (P-T) algorithm with physically based ecophysiological constraints which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP 2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various timescales based on the modelled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP 2, a hybrid approach can be used for similar flux-tower sites and field campaigns. 

Abstract Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s −1 and RMSEs of 2-3 m s −1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced. 

Our study examines the horizontal variation of the nocturnal surface air temperature by analyzing measurements from four contrasting networks of stations with generally modest topography. The horizontal extent of the networks ranges from 1 to 23 km. For each network, we investigate the general relationship of the horizontal variation of temperature to the wind speed, wind direction, near-surface stratification, and turbulence. As an example, the horizontal variation of temperature generally increases with increasing stratification and decreases with increasing wind speed. However, quantitative details vary significantly between the networks. Needed changes of the observational strategy are discussed. 

Abstract. Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many machine learning methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables are most beneficial for forecasting, especially in handling non-linearities that lead to forecasting error. This question is addressed via creation of a hybrid model that utilizes an autoregressive integrated moving-average (ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables.Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction are found to be particularly useful when used in unison for hourly and 3 h timescales. The prediction accuracy of the developed ARIMA–random forest hybrid model is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA–random forest model is shown to improve upon the latter commonly employed modeling methods, reducing hourly forecasting error by up to 5 % below that of the bias-corrected ARIMA model and achieving an R2 value of 0.84 with true wind speed. 

Abstract This paper reports the findings of a comprehensive field investigation on flow through a mountain gap subject to a range of stably stratified environmental conditions. This study was embedded within the Perdigão field campaign, which was conducted in a region of parallel double-ridge topography with ridge-normal wind climatology. One of the ridges has a well-defined gap (col) at the top, and an array of in situ and remote sensors, including a novel triple Doppler lidar system, was deployed around it. The experimental design was mostly guided by previous numerical and theoretical studies conducted with an idealized configuration where a flow (with characteristic velocity U 0 and buoyancy frequency N ) approaches normal to a mountain of height h with a gap at its crest, for which the governing parameters are the dimensionless mountain height G = Nh / U 0 and various gap aspect ratios. Modified forms of G were proposed to account for real-world atmospheric variability, and the results are discussed in terms of a gap-averaged value G c . The nature of gap flow was highly dependent on G c , wherein a nearly neutral flow regime ( G c < 1), a transitional mountain wave regime [ G c ~ O (1)], and a gap-jetting regime [ G c > O (1)] were identified. The measurements were in broad agreement with previous numerical and theoretical studies on a single ridge with a gap or double-ridge topography, although details vary. This is the first-ever detailed field study reported on microscale [ O (100) m] gap flows, and it provides useful data and insights for future theoretical and numerical studies.