Wind Speed Deficit Research Articles

Mesoscale impact of the sea surface on the performance of offshore wind farms

The impact of the ocean on the performance of offshore wind farms is a challenging study in the wind energy field. In the present study, by coupling a mesoscale numerical weather prediction model, Weather Research and Forecasting (WRF), with a sea surface model and a refined wind farm parameterization, a systematic atmosphere-sea surface-wind farm simulation method is established. The question of how the ocean-atmosphere interaction changes the wake effect and power output of wind farms based on the method is explored. The results show that the momentum deficit due to the operating wind turbines is remarkable, and the wind speed deficit has a strong horizontal diffusion on a sea surface. Wake from the offshore wind farm is prone to mix and extends with a shorter length than that on the flat land. The wake mixing enhances the interference from upwind to downwind turbines, resulting in the power outputs of downwind turbines being decreased by 10%. Besides, the increasing wind shear due to the ocean waves decreases the wind power by around 3.5%. Accordingly, it is significant for applying such a coupled air-sea surface-wind farm simulation method when assessing the offshore wind farm performance and the impact of the wind farms on the marine atmospheric boundary layer.

Chasing the offshore wind farm wind-wake-induced upwelling/downwelling dipole

The operational principle of offshore wind farms (OWF) is to extract kinetic energy from the atmosphere and convert it into electricity. Consequently, a region of reduced wind speed in the shadow zone of an OWF, the so-called wind-wake, is generated. As there is a horizontal wind speed deficit between the wind-wake and the undisturbed neighboring regions, the locally reduced surface stress results in an adjusted Ekman transport. Subsequently, the creation of a dipole pattern in sea surface elevation induces corresponding anomalies in the vertical water velocities. The dynamics of these OWF wind-wake induced upwelling/downwelling dipoles have been analyzed in earlier model studies, and strong impacts on stratified pelagic ecosystems have been predicted. Here we provide for the first time empirical evidence of the existence of such upwelling/downwelling dipoles. The data were obtained by towing a remotely operated vehicle (TRIAXUS ROTV) through leeward regions of operational OWFs in the summer stratified North Sea. The undulating TRIAXUS transects provided high-resolution CTD data which enabled the characterization of three different phases of the ephemeral life cycle of a wind-wake-induced upwelling/downwelling dipole: development, operation, and erosion. We identified two characteristic hydrographic signatures of OWF-induced dipoles: distinct changes in mixed layer depth and potential energy anomaly over a distance < 5 km and a diagonal excursion of the thermocline of ~10–14 m over a dipole dimension of ~10–12 km. Whether these anthropogenically induced abrupt changes are significantly different from the corridor of natural variability awaits further investigations.

Historical droughts manifest an abrupt shift to a wetter Tibetan Plateau

Abstract. The Tibetan Plateau (TP) plays a vital role in Asian and even global atmospheric circulation, through the interactions between land and atmosphere. It has experienced significant climate warming and spatially and temporally variant wetting over the past half century. Because of the importance of land surface status to the interactions, determining the wetting and drying of the TP from individual changes in precipitation (Prep) or temperature is difficult. Soil moisture (SM) is the water synthesis of the surface status. The persistent deficit of SM (SM drought) is more sensitive to climate change than normal SM. This study first explored the climate wetting and drying of the TP from variations in historical SM droughts over 1961–2014, with a focus on spatiotemporal patterns, long-term variations, and climate causes of summer (May–September) SM droughts based on multiple observation and reanalysis data. The results showed comparatively frequent and severe droughts in the central and southern area, particularly in the semiarid and subhumid regions. SM drought exhibited an abrupt and significant (p < 0.05) alleviation in the interior and central-west TP in the middle to late 1990s. The prominent drought alleviation indicated a hydroclimate shift to a wetter plateau, not merely steady trends as given in the literature. We demonstrated that the wetting shift was dominated by Prep over potential evapotranspiration (PET). By contrast, the in-phase trends were combined forces of Prep and PET, with increased forces of PET after the wetting shift. Furthermore, the Prep dominance was largely attributed to a phase transition of the Atlantic multi-decadal oscillation from cold to warm since the mid-1990s. The PET impacts on the wetting trends were likely dominated by solar radiation, wind speed, and vapor pressure deficit. Regionally, the wetting shift was distinct from the arid to semiarid and semiarid to subhumid climate. Such spatiotemporal changes may affect the TP's atmospheric circulation and, subsequently, the Asian monsoon and global circulation, in addition to the fragile ecosystem in the TP.

Revisiting the Pan Evaporation Trend in China During 1988–2017

AbstractPan evaporation decrease has been reported worldwide over the past decades. A recent increasing pan evaporation trend has been found globally. Remarkably, most studies on Chinese pan evaporation change were based on simulations involving meteorological variables, including temperature, radiation (sunshine duration [SSD]), wind speed, and relative humidity (RH), due to the pan evaporation observation inconsistency caused by the micro‐pan (D20) replacement with large pan (E601) around 2002. In addition, a large‐scale humidity sensor replacement across China occurred since the 2000s can cause an inconsistency of RH and in turn lead to that of simulated pan evaporation. Therefore, the recent pan evaporation trends independent of the observed RH in China should be revisited. In this study, we (a) generate merged D20 pan evaporation from 1988 to 2017 according to E601 observations under the constant conversion coefficient assumption between the evaporation observations of these pans in the same month of every year at each station, and (b) propose a framework based on the PenPan‐D20 model to attribute pan evaporation change into specific humidity (instead of RH or vapor pressure deficit in previous studies), SSD, air temperature, and wind speed, since RH and vapor pressure deficit are not independent from air temperature. The results show a significant 2.68 mm/a/a upward pan evaporation trend (p < 0.05) across China from 1988 to 2017, which was primarily driven by the rising air temperature. Humidity sensor replacement causes an ∼2.2% inconsistency in RH, which produces nonnegligible errors in simulated pan evaporation trend.

Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling

Abstract. As part of the ongoing X-Wakes research project, a 5-month wake-measurement campaign was conducted using a scanning lidar installed amongst a cluster of offshore wind farms in the German Bight. The main objectives of this study are (1) to demonstrate the performance of such a system and thus quantify cluster wake effects reliably and (2) to obtain experimental data to validate the cluster wake effect simulated by the flow models involved in the project. Due to the lack of free wind flow for the wake flow directions, wind speeds obtained from a mesoscale model (without any wind farm parameterization) for the same time period were used as a reference to estimate the wind speed deficit caused by the wind farm wakes under different wind directions and atmospheric stabilities. For wind farm waked wind directions, the lidar data show that the wind speed is reduced up to 30 % at a wind speed of about 10 m s−1, depending on atmospheric stability and distance to the wind farm. For illustrating the spatial extent of cluster wakes, an airborne dataset obtained during the scanning wind lidar campaign is used and compared with the mesoscale model with wind farm parameterization and the scanning lidar. A comparison with the results of the model with a wind farm parameterization and the scanning lidar data reveals a relatively good agreement in neutral and unstable conditions (within about 2 % for the wind speed), whereas in stable conditions the largest discrepancies between the model and measurements are found. The comparative multi-sensor and model approach proves to be an efficient way to analyze the complex flow situation in a modern offshore wind cluster, where phenomena at different length scales and timescales need to be addressed.

Comparison of individual versus ensemble wind farm parameterizations inclusive of sub‐grid wakes for the WRF model

AbstractWind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.

Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

Abstract. Numerical wind resource modelling across scales from the mesoscale to the turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore wind farm wakes can last several tens of kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed but it remains unclear how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that both WRF + FIT (BIAS = 0.52 m s−1) and WRF + EWP (BIAS = 0.73 m s−1) compare well with wind farm affected mast measurements. Compared with the RANS simulations, baseline intra-farm variability, i.e. the wind speed deficit in between turbines, can be captured reasonably well with WRF + FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF + EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate peak intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared with those of PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

Modeling and prediction of dengue cases in the short and long term in Villa Clara, Cuba using climatic variables and objective regressive regression

Climate has an important influence on infectious diseases and their different modes of transmission. Dengue is among the most relevant for Cuban public health. Due to the pronounced effect that environmental changes can have on the biology of Aedes aegypti, it is very likely that the epidemiology of this arbovirosis will be profoundly influenced by future climate change. The aim of the study was to analyze the possible relationship between meteorological variables and the incidence of Dengue in Villa Clara province, Cuba during the years 2017-2020, and to perform a predictive model of the behavior of the disease during 2021. Retrospective research was carried out in which the possible effects of diversifications of temperature, precipitation, humidity, water vapor tension, atmospheric pressure, wind speed, cloudiness and saturation deficit on the deviations in the epidemiological curves of Dengue infection in Villa Clara (2017-2020) were analyzed to predict the future behavior of the referred entity during the current year. A correlation of the infectious entity with minimum temperature (R=0.332; p=0.023) and water vapor tension (R=0.298; p=0.042) was obtained, as well as an inverse relationship with atmospheric pressure (R=-0.317; p=0.030). It is concluded that a predictive model was obtained for 2021 with high reliability, in which a decrease in the incidence of Dengue is predicted in the month of March until July, but after August to December the values will increase greatly.

How Does Air‐Sea Wave Interaction Affect Tropical Cyclone Intensity? An Atmosphere‐Wave‐Ocean Coupled Model Study Based on Super Typhoon Mangkhut (2018)

AbstractCapturing TC intensity change remains a great challenge for most state‐of‐the‐art operational forecasting systems. Recent studies found TC intensity forecasts are sensitive to three‐dimensional ocean dynamics and air‐sea interface processes beneath extreme winds. By performing a series of numerical simulations based on hierarchical atmosphere‐wave‐ocean (AWO) coupling configurations, we showed how atmosphere‐ocean and atmosphere‐sea wave coupling can affect the intensity of super typhoon Mangkhut (2018). The AWO coupled model can simulate TC‐related strong winds, oceanic cold wake, and wind waves with high fidelity. With atmosphere‐ocean (AO) coupling implemented, the simulated maximum surface wind speed is reduced by 7 m s−1 compared to the atmosphere‐only run, due to TC‐induced oceanic cold wakes in the former experiment. In the fully coupled AWO simulations, the wind speed deficit can be completely compensated by the wave‐air coupling effect. Further analyses showed that, in the AWO experiment, two mechanisms contribute to the improvement of TC intensity. First, in the high wind scenario (>28 m s−1), the surface drag coefficient reaches an asymptotic level, assisting extreme wind speed to be maintained within the eyewall. Second, the wind speed distribution is modulated and becomes broader; higher wind within the TC area helps to offset the negative effect due to leveling off of the heat exchange coefficient as wind speed increases. Overall, the simulated TC in the AWO run can extract 8–9% more total heat energy from the ocean to maintain its strength, compared to that from the AO experiment.

Wind lidars reveal turbulence transport mechanism in the wake of a tree

Abstract. Solitary trees are natural land surface elements found in almost all climates, yet their influence on the surrounding airflow is poorly known. Here we use state-of-the-art, laser-based, remote sensing instruments to study the turbulent wind field in the near-wake region of a mature, open-grown oak tree. Our measurements provide for the first time a full picture of the mixing layer of high turbulence that surrounds the mean wind speed deficit. In this layer, we investigate the validity of a two-dimensional vectorial relation derived from the eddy-viscosity hypothesis, a hypothesis commonly used in modelling the turbulence transport of momentum and scalars in the atmosphere. We find that the momentum fluxes of the streamwise wind component can be adequately predicted by the transverse gradient of the mean flow. Using the mixing-length hypothesis we find that for this tree the corresponding turbulence length scale in the mixing layer can be approximated by one height-independent value. Further, the laser-based scanning technology used here was able to accurately reveal three-dimensional turbulent and spatially varying atmospheric flows over a large plane without seeding or intruding the atmospheric flow. This capability points to a new and more exact way of exploring the complex earth–atmosphere interactions.

Effect of climate change and elevated CO2 on reference evapotranspiration in Varanasi, India - A case study

Evapotranspiration (ET) is the major component of hydrological cycle, and will affect the crop water requirement and future planning and management of water resources under changing climate scenarios. In the present study, an attempt was made to study the sensitivity of reference evapotranspiration (ETo) to different climatic variables, and effect of temperature and elevated CO2 on ETo using meteorological data (1973-2010) of the Banaras Hindu University (BHU), Varanasi. The FAO-56 Penman–Monteith method was used to estimate ETo, and sensitivity of ETo was studied in terms of changes in temperature, solar radiation, wind speed and vapour pressure deficit. The combined effect of temperature and elevated CO2 levels was studied by varying the temperature from 1°C to 5°C, and the CO2 level from 330 ppm to 660 ppm. The simulation results showed that the mean temperature (Tmean) influenced the annual ETo the most followed by solar radiation (Rs), vapour pressure deficit (VPD) and wind speed (U2). There are about 18.20, 13.80, 7.90 and 5.40% increase in annual ETo with 25% increase in Tmean, Rs, VPD, and U2, respectively. Simulating the combined effect of temperature and elevated CO2 indicated that the effect of 2.5 °C rise in temperature is offset by doubling of CO2 concentration. Thus, the effect of rising temperature is moderated by the increasing CO2 concentrations, and the crop water demand may not rise significantly under the climate change scenarios.

Estimation of the rice water footprint based on machine learning algorithms

It is essential to investigate the impact of climate change on the water footprint (WF) of rice from both historical simulation and future projections. In this study, four machine learning (ML) models, including random tree (RT), random forest (RF), additive regression (AR) and reduced error pruning tree (REPT) were used to model blue and green water footprint (BWFP and GWFP) for the present and future stages in the Yunnan Province, southwest China. Climate variables of daily precipitation, temperature, solar radiation, sunshine hours, wind speed, relative humidity and vapor pressure deficit data, yield and sown areas of rice were collected form 16 districts from 1990 to 2018. Six different scenarios (Sc1-Sc6) with different combinations of climate variables, crop coefficient and sown areas were used as inputs of the ML models for each of blue and green water footprint. Also, future climate projections of maximum (Tmax) and minimum (Tmin) temperatures, precipitation and sunshine were adopted for two different emission scenarios, RCP 4.5 and 8.5 from 2021to 2050, based on Geophysical Fluid Dynamics Laboratory (GFDL-ESM2M) model. For BWFP, the RT model in Sc1 with inputs of solar radiation, humidity, and vapor pressure deficit, was superior to the other scenarios with root mean square error (RMSE) and mean average percentage error (MAPE) values of 11.82 (m3 ton-1) and 0.5%, respectively. Sc4 (sown area, Tmin, sunshine hours) and Sc5 (sown area, Tmin, Tmax, crop coefficient) were the best two scenarios for all models applied for BWFP, while addition of precipitation to these two scenarios were the best for GWFP. Further, GWFP was highly negative anomaly in 2011 by 46%, 34% and 32% for the districts 5, 1 and 2 respectively, followed by 2010. Predictions of variable trends in the future showed that crop evapotranspiration during the growing season would increase in all the districts, although effective precipitation (Peff) would have both decreasing and increasing trends. Furthermore, BWFP and GWFP will have an upward trend in the future based on RCP4.5 in Sc4. This investigation addresses water footprint prediction which may assist in mitigation plans such as policies for sustainable water-use and development plans for food security.

The Role of Atmospheric Stability and Turbulence in Offshore Wind-Farm Wakes in the German Bight

Airborne meteorological in situ measurements as well as stationary measurements at the offshore masts FINO1 and FINO3 in the German Bight are evaluated in order to examine the hypothesis that the wake dissipation downstream of large offshore wind farms depends on atmospheric stability. A long-term study of the mast data for the years 2016 and 2017 demonstrates a clear dependence of stability on the wind direction. Stable conditions are predominantly expected during southerly winds coming from the land. The analysis of various stability and turbulence criteria shows that the lapse rate is the most robust parameter for stability classification in the German Bight, but further implies that stability depends on the measurement height. A near-surface (0 to 30 m), predominantly convective, layer is present and more stable conditions are found aloft (55 to 95 m). Combing the stability data with the airborne measurements of the offshore wind-farm wakes reveals the trend of a correlation between longer wake lengths and an increase in the initial wind-speed deficit downwind of a wind farm with stronger thermal stability. However, the stability correlation criteria with the wake length downstream of the four investigated wind farms, Godewind, Amrumbank West, Meerwind Süd/Ost, and Nordsee Ost, contain large variance. It is assumed that the observed scattering is due to the influence of the wind-farm architecture and temperature inversions around hub height. These, however, are crucial for the classification of stability and illustrate the complexity of a clear stability metric.

Important meteorological predictors for long-range wildfires in China

Wildfire predictions provide useful information for fire management planning and implementation. Temperature and precipitation have been used as the primary meteorological predictors for wildfires in China. This study is to improve the prediction skills of long-range (monthly, seasonal, and annual) wildfires in China by identifying other important meteorological predictors. Provincial data during 1999–2020 were used to calculate the correlations between fire properties (fire count and burned area) and meteorological variables of maximum temperature, precipitation, relative humidity, wind speed, and vapor pressure deficit (VPD) and drought indices of Keetch-Bryam Drought Index (KBDI), Palmer Drought Severity Index (PDSI), and Standardized Precipitation Index (SPI). The fitting rates of the linear regression fire prediction models were compared among these meteorological variables and drought indices. The results indicate that the number of provinces with significant correlations and / or high fitting rates is the largest with VPD for monthly fires, KBDI for seasonal fires, and KBDI, PDSI, and SPI for annual fires. The number is larger in Northeast, Central, and South China than in other China regions. The number is comparable between spring and other seasons for KBDI but often smaller in spring for meteorological variables. The number is generally smaller for burned areas than fire count. It is concluded that the skills of long-range fire predictions are expected to be improved in many provinces of China by using VPD and KBDI as well as some other drought indices.

Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt

Timely and reliable water footprint prediction is imperative and prerequisite to mitigate climate risk and ensure water and food security and enhance the water-use efficiency. This study aims to model the Water Footprint (WF) by using the four kernels of Gaussian processes models (Polynomial, Normalized Poly, Radial Basis Function RBF, and Pearson Universal Function PUK) and select the best kernel with best climate scenario. This study investigates the predicting WF of maize based on meteorological variables including maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (SR), wind speed (WS), and vapor pressure deficit (VPD), Extraterrestrial radiation (Ra) relative humidity (RH) and crop coefficient (Kc) by applying ten scenarios of climate variables in the Egyptian Nile Delta, Ad Daqahliyah Governorate for predicting blue WF of maize during 2000–2019. The main findings are following as, firstly; based on developing four kernels, the performance of the PUK kernel in predicting blue WF is far better than the other three kernels followed by the Poly kernel. Secondly; for PUK kernel, model 7 (Tmax, Tmin, Tmean, WS, Sunshine Hours (SH), VPD and SR) has good performance which is close to models 8 (model 7 +Ra), model 9 (model 7 +Ra and RH) and model 10 (all inputs). Thirdly; in all four kernels, the error rate in small blue WF values is higher than the other values, moreover, the error value decreases at the medium blue WF values, while, it increases again at large WF values. Therefore, the developed models in this study can help and promote the decision makers to manage and secure the water resources management under the extreme climate events.

Wind farm layout optimization using pseudo-gradients

Abstract. This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a “pseudo-gradient”, that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points – speed, competitive reduction in wake losses, and flexibility – and weak points – partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.

Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials

The European Union has set ambitious CO2 reduction targets, stimulating renewable energy production and accelerating deployment of offshore wind energy in northern European waters, mainly the North Sea. With increasing size and clustering, offshore wind farms (OWFs) wake effects, which alter wind conditions and decrease the power generation efficiency of wind farms downwind become more important. We use a high-resolution regional climate model with implemented wind farm parameterizations to explore offshore wind energy production limits in the North Sea. We simulate near future wind farm scenarios considering existing and planned OWFs in the North Sea and assess power generation losses and wind variations due to wind farm wake. The annual mean wind speed deficit within a wind farm can reach 2–2.5 ms−1 depending on the wind farm geometry. The mean deficit, which decreases with distance, can extend 35–40 km downwind during prevailing southwesterly winds. Wind speed deficits are highest during spring (mainly March–April) and lowest during November–December. The large-size of wind farms and their proximity affect not only the performance of its downwind turbines but also that of neighboring downwind farms, reducing the capacity factor by 20% or more, which increases energy production costs and economic losses. We conclude that wind energy can be a limited resource in the North Sea. The limits and potentials for optimization need to be considered in climate mitigation strategies and cross-national optimization of offshore energy production plans are inevitable.

Offshore wind farm global blockage measured with scanning lidar

Abstract. The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global-blockage effect, in relation to atmospheric-stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced, and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global-blockage effect, which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a 4 % decrease in wind speed (accuracy range of 2 % to 6 %) at transition piece height (24.6 m) upwind of the wind farm with the turbines operating at high thrust coefficients above 0.8 in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions and for situations with low thrust coefficients (i.e. approx. 0 for not operating turbines and ≤ 0.3 for turbines operating far above rated wind speed) we identified no wind speed deficit. We discussed the significance of our measurements and possible sources of error in long-range scanning lidar campaigns and give recommendations on how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We further conclude that global blockage is dependent on atmospheric stratification.

Validation of the dynamic wake meandering model with respect to loads and power production

Abstract. The outlined analysis validates the dynamic wake meandering (DWM) model based on loads and power production measured at an onshore wind farm with small turbine distances. Special focus is given to the performance of a version of the DWM model that was previously recalibrated at the site. The recalibration is based on measurements from a turbine nacelle-mounted lidar system. The different versions of the DWM model are compared to the commonly used Frandsen wake-added turbulence model. The results of the recalibrated wake model agree very well with the measurements, whereas the Frandsen model overestimates the loads drastically for short turbine distances. Furthermore, lidar measurements of the wind speed deficit as well as the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.