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Pedro A. Jiménez and Jimy Dudhia

Abstract

The Weather Research and Forecasting (WRF) model presents a high surface wind speed bias over plains and valleys that constitutes a limitation for the increasing use of the model for several applications. This study attempts to correct for this bias by parameterizing the effects that the unresolved topographic features exert over the momentum flux. The proposed parameterization is based on the concept of a momentum sink term and makes use of the standard deviation of the subgrid-scale orography as well as the Laplacian of the topographic field. Both the drag generated by the unresolved terrain and the possibility of an increase in the speed of the flow over the mountains and hills, where it is herein shown that WRF presents a low wind speed bias, are considered in the scheme. The surface wind simulation over a complex-terrain region that is located in the northeast of the Iberian Peninsula is improved with the inclusion of the new parameterization. In particular, the underestimation of the wind speed spatial variability resulting from the mentioned biases is corrected. The importance of selecting appropriate grid points to compare with observations is also examined. The wind speed from the nearest grid point is not always the most appropriate one for this comparison, nearby ones being more representative. The new scheme not only improves the climatological winds but also the intradiurnal variations at the mountains, over which the default WRF shows limitations in reproducing the observed wind behavior. Some advantages of the proposed formulation for wind-resource evaluation are also discussed.

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Pedro A. Jiménez and Jimy Dudhia

Abstract

The wind stress formulation in an atmospheric model over shallow waters is investigated using year-long observations of the wind profile within the first 100 m of the atmosphere and mesoscale simulations. The model experiments use a range of planetary boundary layer parameterizations to quantify the uncertainty related to the turbulent closure assumptions and thus to isolate the dominant influence of the surface roughness formulation. Results indicate that a positive wind speed bias exists when common open-ocean formulations for roughness are adopted for a region with a water depth of 30 m. Imposition of a wind stress formulation that is consistent with previous shallow-water estimates is necessary to reconcile model wind speeds with observations, providing modeling evidence that supports the increase of surface drag over shallow waters. The possibility of including water depth in the parameterization of roughness length is examined.

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Pedro A. Jiménez and Jimy Dudhia

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The ability of the Weather Research and Forecasting (WRF) model to reproduce the surface wind direction over complex terrain is examined. A simulation spanning a winter season at a high horizontal resolution of 2 km is compared with wind direction records from a surface observational network located in the northeastern Iberian Peninsula. A previous evaluation has shown the ability of WRF to reproduce the wind speed over the region once the effects of the subgrid-scale topography are parameterized. Hence, the current investigation complements the previous findings, providing information about the model's ability to reproduce the direction of the surface flow. The differences between the observations and the model are quantified in terms of scores explicitly designed to handle the circular nature of the wind direction. Results show that the differences depend on the wind speed. The larger the wind speed is, the smaller are the wind direction differences. Areas with more complex terrain show larger systematic differences between model and observations; in these areas, a statistical correction is shown to help. The importance of the grid point selected for the comparison with observations is also analyzed. A careful selection is relevant to reducing comparative problems over complex terrain.

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Cristina L. Archer, Sicheng Wu, Yulong Ma, and Pedro A. Jiménez

Abstract

As wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called C TKE, that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of C TKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF Model for a single turbine with and without the bug fix and the corrected C TKE and compare them with high-fidelity large-eddy simulations. These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected, and their conclusions may need to be revisited.

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Raquel Lorente-Plazas, Pedro A. Jiménez, Jimy Dudhia, and Juan P. Montávez

Abstract

This study assesses the impact of the atmospheric stability on the turbulent orographic form drag (TOFD) generated by unresolved small-scale orography (SSO) focusing on surface winds. With this aim, several experiments are conducted with the Weather Research and Forecasting (WRF) Model and they are evaluated over a large number of stations (318 at 2-m height) in the Iberian Peninsula with a year of data. In WRF, Jiménez and Dudhia resolved the SSO by including a factor in the momentum equation, which is a function of the orographic variability inside a grid cell. It is found that this scheme can improve the simulated surface winds, especially at night, but it can underestimate the winds during daytime. This suggests that TOFD can be dependent on the PBL’s stability. To inspect and overcome this limitation, the stability conditions are included in the SSO parameterization to maintain the intensity of the drag during stable conditions while attenuating it during unstable conditions. The numerical experiments demonstrate that the inclusion of stability effects on the SSO drag parameterization improves the simulated surface winds at diurnal, monthly, and annual scales by reducing the systematic daytime underestimation of the original scheme. The correction is especially beneficial when both the convective velocity and the boundary layer height are used to characterize the unstable conditions.

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Pedro A. Jimenez, Jordi Vila-Guerau de Arellano, Jorge Navarro, and J. Fidel Gonzalez-Rouco
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Pedro A. Jiménez, J. Fidel González-Rouco, Jorge Navarro, Juan P. Montávez, and Elena García-Bustamante

Abstract

Meteorological data of good quality are important for understanding both global and regional climates. In this respect, great efforts have been made to evaluate temperature- and precipitation-related records. This study summarizes the evaluations made to date of the quality of wind speed and direction records acquired at 41 automated weather stations in the northeast of the Iberian Peninsula. Observations were acquired from 1992 to 2005 at a temporal resolution of 10 and 30 min. A quality assurance system was imposed to screen the records for 1) manipulation errors associated with storage and management of the data, 2) consistency limits to ensure that observations are within their natural limits of variation, and 3) temporal consistency to assess abnormally low/high variations in the individual time series. In addition, the most important biases of the dataset are analyzed and corrected wherever possible. A total of 1.8% wind speed and 3.7% wind direction records was assumed invalid, pointing to specific problems in wind measurement. The study not only tries to contribute to the science with the creation of a wind dataset of improved quality, but it also reports on potential errors that could be present in other wind datasets.

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Xabier Pedruzo-Bagazgoitia, Pedro A. Jiménez, Jimy Dudhia, and Jordi Vilà-Guerau de Arellano

Abstract

This study presents a systematic analysis of convective parameterizations performance with interactive radiation, microphysics, and surface on an idealized day with shallow convection. To this end, we analyze a suite of mesoscale numerical experiments (i.e., with parameterized turbulence). In the first set, two different convection schemes represent shallow convection at a 9-km resolution. These experiments are then compared with model results omitting convective parameterizations at 9- and 3-km horizontal resolution (gray zone). Relevant in our approach is to compare the results against two simulations by different large-eddy simulation (LES) models. Results show that the mesoscale experiments, including the 3-km resolution, are unable to adequately represent the timing, intensity, height, and extension of the shallow cumulus field. The main differences with LES experiments are the following: a too late onset, too high cloud base, and a too early transport of moisture too high, overestimating the second cloud layer. Related to this, both convective parameterizations produce warm and dry biases of up to 2 K and 2 g kg−1, respectively, in the cloud layer. This misrepresentation of the cloud dynamics leads to overestimated shortwave radiation variability, both spacewise and timewise. Domain-averaged shortwave radiation at the surface, however, compares satisfactorily with LES. The shortwave direct and diffuse partition is misrepresented by the convective parameterizations with an underestimation (overestimation) of diffuse (direct) radiation both locally and, by a relative 40% (10%), of the domain average.

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Jared A. Lee, Sue Ellen Haupt, Pedro A. Jiménez, Matthew A. Rogers, Steven D. Miller, and Tyler C. McCandless

Abstract

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

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Pedro A. Jiménez, Stefano Alessandrini, Sue Ellen Haupt, Aijun Deng, Branko Kosovic, Jared A. Lee, and Luca Delle Monache

Abstract

The shortwave radiative impacts of unresolved cumulus clouds are investigated using 6-h ensemble simulations performed with the WRF-Solar Model and high-quality observations over the contiguous United States for a 1-yr period. The ensembles use the stochastic kinetic energy backscatter scheme (SKEBS) to account for implicit model uncertainty. Results indicate that parameterizing the radiative effects of both deep and shallow cumulus clouds is necessary to largely reduce (55%) a systematic overprediction of the global horizontal irradiance. Accounting for the model’s effective resolution is necessary to mitigate the underdispersive nature of the ensemble and provide meaningful quantification of the short-range prediction uncertainties.

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