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The first part of the present study investigated the relationship among the number of yaw gear and motor failures and turbulence intensity (TI) at all the wind turbines under investigation with the use of in situ data. The investigation revealed that wind turbine #7 (T7), which experienced a large number of failures, was affected by terrain-induced turbulence with TI that exceeded the TI presumed for the wind turbine design class to which T7 belongs. Subsequently, a computational fluid dynamics (CFD) simulation was performed to examine if the abovementioned observed wind flow characteristics could be successfully simulated. The CFD software package that was used in the present study was RIAM-COMPACT, which was developed by the first author of the present paper. RIAM-COMPACT is a nonlinear, unsteady wind prediction model that uses large-eddy simulation (LES) for the turbulence model. RIAM-COMPACT is capable of simulating flow collision, separation, and reattachment and also various unsteady turbulence–eddy phenomena that are caused by flow collision, separation, and reattachment. A close examination of computer animations of the streamwise (x) wind velocity revealed the following findings: As we predicted, wind flow that was separated from a micro-topographical feature (micro-scale terrain undulations) upstream of T7 generated large vortices. These vortices were shed downstream in a nearly periodic manner, which in turn generated terrain-induced turbulence, affecting T7 directly. Finally, the temporal change of the streamwise (x) wind velocity (a non-dimensional quantity) at the hub-height of T7 in the period from 600 to 800 in non-dimensional time was re-scaled in such a way that the average value of the streamwise (x) wind velocity for this period was 8.0 m/s, and the results of the analysis of the re-scaled data were discussed. With the re-scaled full-scale streamwise wind velocity (m/s) data (total number of data points: approximately 50,000; time interval: 0.3 s), the time-averaged streamwise (x) wind velocity and TI were evaluated using a common statistical processing procedure adopted for in situ data. Specifically, 10-min moving averaging (number of sample data points: 1932) was performed on the re-scaled data. Comparisons of the evaluated TI values to the TI values from the normal turbulence model in IEC61400-1 Ed.3 (2005) revealed the following: Although the evaluated TI values were not as large as those observed in situ, some of the evaluated TI values exceeded the values for turbulence class A, suggesting that the influence of terrain-induced turbulence on the wind turbine was well simulated.

In the present study, we perform numerical simulations considering various stable atmospheric conditions for a small-scale simple topography. Based on the obtained simulation results, we visualize the flow field and discuss drastic changes in the flow patterns. A flow pattern similar to the potential flow suddenly appears around an isolated hill as the stability increases, regardless of the inclination angle of the hill. We show that a critical Richardson number clearly exists. Furthermore, the effect of stable stratification on the evaluation of power generation is shown for typical complex terrain. We evaluate the capacity factor (%) of a 2 MW large wind turbine based on one-year virtual mast data and consider the effect of stable stratification. It is shown, in the case of stable stratification, that the capacity factor is 2.775 times greater than that under neutral stratification.

The present study scrutinized the impacts of terrain-induced turbulence on wind turbine blades, examining measurement data regarding wind conditions and the strains of wind turbine blades. Furthermore, we performed a high-resolution large-eddy simulation (LES) and identified the three-dimensional airflow structures of terrain-induced turbulence. Based on the LES results, we defined the Uchida-Kawashima Scale_1 (the U-K scale_1), which is a turbulence evaluation index, and clarified the existence of the terrain-induced turbulence quantitatively. The threshold value of the U-K scale_1 was determined as 0.2, and this index was confirmed to not be dependent on the inflow profile, the influence of the horizontal grid resolution, and the influence of the computed azimuth. In addition, we defined the Uchida-Kawashima Scale_2 (the U-K scale_2), which is a fatigue damage evaluation index based on the measurement data and the design value obtained by DNV GL’s Bladed. DNV GL (Det Norske Veritas Germanischer Lloyed) is a third party certification body in Norway, and Bladed has been the industry standard aero-elastic wind turbine modeling software. Using the U-K scale_2, the following results were revealed: the U-K scale_2 was 0.86 < 1.0 (within the designed value) in the case of northerly wind, and the U-K scale_2 was 1.60 > 1.0 (exceeding the designed value) in the case of easterly wind. As a result, it was revealed that the blades of the target wind turbine were directly and strongly affected by terrain-induced turbulence when easterly winds occurred.

The Haifa bay area (HBA), which includes Mount Carmel and the Zevulun valley is the third largest metropolitan area in Israel. It is also a centre of heavy industry and an important transportation hub which serve as sources of local anthropogenic pollution. Such sources are associated with adverse health effects. In order to estimate the possible exposure of the inhabitants in such heterogeneous orographic area, a detailed atmospheric transport and dispersion modelling study is required, which in turn must take into account the local micrometeorology. The aim of this study is to conduct a spatio-temporal analysis of the flow field in the HBA in order to identify the common patterns of the average wind and characterize the statistical parameters of turbulence in this area, essential for detailed pollutants dispersion modelling. This study analyses data collected during four months of summer in a network of 16 weather stations which extend across Mount Carmel and the Zevulun valley. It was found that, during the evening and night time on Mount Carmel, different flow patterns may develop on each side, separated by the watershed line. When such conditions do not develop, as well as during the daytime, the wind field, both on Mount Carmel and the Zevulun valley is approximately homogenous. The analysis of the Monin–Obukhov similarity theory functions for the velocity standard deviations show a distinct difference between Mount Carmel and the Zevulun valley, as well as between strong and weak winds. This difference can be clearly seen also in the diurnal hourly distribution of atmospheric stabilities which exhibit higher proportions of unstable conditions in the Zevulun valley during day time and higher proportion of stable stratifications at the Mount Carmel during night-time.

The quality of lidar and sodar wind estimates is generally judged through comparisons with mast-mounted instruments, and the resulting regressions. Evaluation of the relative merits of lidars versus sodars is complicated by the fact that lidars are generally placed close to a mast whereas sodars are generally placed some distance from a mast so that acoustic reflections off the mast are reduced. This leads to the two technologies, lidar and sodar, not being compared in similar situations. Differences arising from the two geometries can be expected to be larger in complex terrain, where the wind regime can vary significantly spatially. The current work explores these differences in moderately complex terrain. Lidar–mast comparisons are performed with the lidar close to an 80 m mast, and sodar–mast comparisons performed with the sodar 300 m from the mast. Systematic variations in estimated wind speed are found to occur with height, consistent with predictions from a simple flow model. When the lidar was moved to the sodar location, further from the mast, there were significant changes in the estimated wind speeds and a reduction in correlation with the mast-based wind speeds, as expected. However, the correlation between collocated lidar and sodar winds was high. This finding emphasizes that any comparison of two remote sensing instruments needs to be through similar experiments, and that differences in accuracy often reported for the lidar and sodar technologies are likely to be contaminated due to poor comparison configurations. A method was devised to simulate the sodar being collocated with the mast, by using the lidar–sodar measurements and the lidar–mast measurements. It was found that there was then no statistically detectable difference between lidar–mast regressions and sodar–mast regressions for the particular lidar and sodar tested. Both remote sensing instruments were also found to be good estimators of Weibull parameters, as compared with those derived from mast data. The conclusion is that the sodar measured the winds above the sodar with a similar accuracy to the lidar measuring winds above the lidar.

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

The Po Valley in northern Italy is a hotspot for tornadoes in Europe in spite of being surrounded by two mountain ridges: the Alps in the north and the Apennines in the southwest. The research focuses on the case study of 19 September 2021, when seven tornadoes (four of them rated as F2) developed in the Po Valley in a few hours. The event was analyzed using observations and numerical simulations with the convection-permitting Modello Locale in Hybrid Coordinates (MOLOCH) model. Observations show that during the event in the Po Valley, there were two surface boundaries that created a triple point: an outflow boundary generated by convection triggered in the Alpine foothills and a dryline generated by downslope winds from the Apennines, while warm and moist air advected westward from the Adriatic Sea east (ahead) of the boundaries. Tornadoes developed about 20 km northeast of the triple point. Numerical simulations with 500-m grid spacing suggest that the development of supercells and drylines in the Po Valley was sensitive to the elevation of the Apennines. Simulated vertical profiles show that the best combination of instability and wind shear for the development of tornadoes was attained within a narrow area located ahead of the dryline. A conceptual model for the development of tornadoes in the Po Valley is proposed, and the differences between tornado environments over a flat terrain and over a region with complex terrain are discussed.

The New European Wind Atlas project will create a freely accessible wind atlas covering Europe and Turkey, develop the model chain to create the atlas and perform a series of experiments on flow in many different kinds of complex terrain to validate the models. This paper describes the experiments of which some are nearly completed while others are in the planning stage. All experiments focus on the flow properties that are relevant for wind turbines, so the main focus is the mean flow and the turbulence at heights between 40 and 300 m. Also extreme winds, wind shear and veer, and diurnal and seasonal variations of the wind are of interest. Common to all the experiments is the use of Doppler lidar systems to supplement and in some cases replace completely meteorological towers. Many of the lidars will be equipped with scan heads that will allow for arbitrary scan patterns by several synchronized systems. Two pilot experiments, one in Portugal and one in Germany, show the value of using multiple synchronized, scanning lidar, both in terms of the accuracy of the measurements and the atmospheric physical processes that can be studied. The experimental data will be used for validation of atmospheric flow models and will by the end of the project be freely available. This article is part of the themed issue 'Wind energy in complex terrains'.

The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotorlayer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.

The evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China.