scholarly journals Avaliação do desempenho do Modelo WRF para prognóstico do vento na Região Central de Alagoas – Craíbas

2018 ◽  
Vol 40 ◽  
pp. 187
Author(s):  
Silvania Maria Santos da Silva ◽  
Roberto Fernando da Fonseca Lyra ◽  
Rosiberto Salustiano da Silva Júnior ◽  
Nareida Simone Delgado da Cruz ◽  
Sâmara Dos Santos Silva
Keyword(s):  
Wind Speed ◽  
Wind Velocity ◽  
Central Region ◽  
Wrf Model ◽  
Wind Farms ◽  
Dry Season ◽  
Mean Difference ◽  
Good Tool ◽  
The Mean ◽  
Brazilian Northeast

The aim of this work was to evaluate the performance of the WRF model for estimate wind speed in the central region of Alagoas State (Brazil). The wind velocity (WRF outputs) were compared with anemometric data of the PVPN projetc (Previsão do Vento em Parques Eólicos no Nordeste Brasileiro “Wind forecast for wind farms in the Brazilian Northeast”), from January to December 2014. The results showed that the WRF model estimated very well the monthly and daily wind speed averages. Deviations are greater in the dry season. Throughout the year the mean difference (WRF-OBS) was only 0.23 m.s-1 (6.14 m.s-1 versus 5.91 m.s-1), showing that this model is a good tool for forecasting wind for wind farms.

scholarly journals PROGNÓSTICO DA VELOCIDADE DO VENTO UTILIZANDO O MODELO WRF NA REGIÃO CENTRAL DE ALAGOAS

2016 ◽  
Vol 38 ◽  
pp. 447
Author(s):  
Roberto Fernando Da Fonseca Lyra ◽  
Rosiberto Salustiano da Silva Junior ◽  
Marcos Antonio Lima Moura ◽  
Marney Chaves de Aragão Lisboa Amorim
Keyword(s):  
Latent Heat ◽  
Wind Velocity ◽  
Friction Velocity ◽  
Wrf Model ◽  
Correlation Coefficients ◽  
Wind Farms ◽  
Sensible Heat ◽  
Medium Term ◽  
Daily Cycles ◽  
Brazilian Northeast

The PVPN project (Previsão do Vento em Parques Eólicos no Nordeste Brasileiro “Wind forecast for wind farms in the Brazilian Northeast”) has been made aiming to developing a methodology for the prediction of short and medium-term wind energy in wind farms, proper Brazilian Northeast. This paper presents results from an intensive campaign, which, wind velocity and micrometeorological measurements including turbulence are made. Estimates made by the WRF model for the wind velocity were compared with friction velocity and the fluxes (sensible heat and latent heat). The results showed that the model represented well the daily cycles of the four variables with correlation coefficients between 0.79 and 0.94. The estimation of the wind velocity were very good with a difference of only 10.55%. The estimate of the remaining variables was bad to reasonable.

Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model

2020 ◽  
Vol 148 (12) ◽  
pp. 4823-4835
Author(s):  
Cristina L. Archer ◽  
Sicheng Wu ◽  
Yulong Ma ◽  
Pedro A. Jiménez
Keyword(s):  
Wind Speed ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Wind Farms ◽  
Near Surface ◽  
Large Eddy ◽  
The Way

AbstractAs 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 CTKE, 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 CTKE 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 CTKE 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.

scholarly journals Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

2018 ◽  
Vol 57 (3) ◽  
pp. 733-753 ◽  
Author(s):  
Sergio Fernández-González ◽  
María Luisa Martín ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
Jesús Lorenzana ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Wrf Model ◽  
Wind Farms ◽  
Ensemble Prediction ◽  
Mountain Range ◽  
Ensemble Prediction System ◽  
Initial And Boundary Conditions

AbstractWind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.

scholarly journals Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 684 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Numerical Model ◽  
Wind Velocity ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Global Analysis ◽  
Wrf Model ◽  
Weather Data ◽  
Northern Taiwan

A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area.

scholarly journals Wind speed analysis based on the logarithmic wind shear model: a case study for some brazilian cities

2020 ◽  
Vol 9 (7) ◽  
pp. e298973984
Author(s):  
Anny Key de Souza Mendonça ◽  
Antonio Cezar Bornia
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Shear ◽  
Wind Farms ◽  
Shear Model ◽  
The Public ◽  
Wind Speeds ◽  
Generating Capacity ◽  
Mean Wind Speed ◽  
The Mean

The wind power’ share in electricity generating capacity has increased significantly in recent years. Due to the variability in wind power generation, given the variations in wind speed and considering the increase in wind participation in the Brazilian energy matrix, a fact that reinforces the relevance of the source, this article aims to present the methods used to analyze the wind speed more used in the literature and to analyze the wind speed in several Brazilian cities. The logarithmic wind shear model was used to analyze mean wind speeds based on historical data of twelve Brazilian cities available to the public on the ESRL database for a period of eight years 2010 to 2018. The study showed that in localities such as Uruguaiana/RS, Campo Grande/MS, Uberlândia/MG, São Luiz/MA and Corumba/MS, mean wind speeds are strong in all altitudes of reference, with a gain of ± 2m/s of wind speed as the operational altitude increases. The logarithmic wind gain in high altitudes or low altitudes can be seen in z = 100 meters, where the mean wind speed found was Wn ≈ 8 m/s in Uruguaiana/RS and Campo Grande/MS, whereas in Manaus it was Wn ≈ 5 m/s. In Porto Alegre (RS), Florianópolis (SC), Curitiba/PR and Brasília/DF, the mean wind speed in altitudes ≥ 250 m becomes significant, allowing the implementation of wind farms if the technology proves to be economically feasible.

scholarly journals Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

2020 ◽  
Vol 12 (6) ◽  
pp. 973
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Satellite Data ◽  
Surface Wind ◽  
Wrf Model ◽  
Wind Farms ◽  
Resource Assessment ◽  
Near Surface ◽  
Wind Resource Assessment ◽  
Wind Resource

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.

scholarly journals Use of the WRF-DA 3D-Var Data Assimilation System to Obtain Wind Speed Estimates in Regular Grids from Measurements at Wind Farms in Uruguay

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 142 ◽  
Author(s):  
Gabriel Cazes Boezio ◽  
Sofía Ortelli
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Wind Farms ◽  
Forecast Model ◽  
Model Data ◽  
Assimilation Process ◽  
The Mean ◽  
Data Assimilation System ◽  
Assimilation System

This work assessed the quality of wind speed estimates in Uruguay. These estimates were obtained using the Weather Research and Forecast Model Data Assimilation System (WRF-DA) to assimilate wind speed measurements from 100 m above the ground at two wind farms. The quality of the estimates was assessed with an anemometric station placed between the wind farms. The wind speed estimates showed low systematic errors at heights of 87 and 36 m above the ground. At both levels, the standard deviation of the total errors was approximately 25% of the mean observed speed. These results suggested that the estimates obtained could be of sufficient quality to be useful in various applications. The assimilation process proved to be effective, spreading the observational gain obtained at the wind farms to lower elevations than those at which the assimilated measurements were taken. The smooth topography of Uruguay might have contributed to the relatively good quality of the obtained wind estimates, although the data of only two stations were assimilated, and the resolution of the regional atmospheric simulations employed was relatively low.

scholarly journals The Making of the New European Wind Atlas – Part 2: Production and evaluation

2020 ◽  
Vol 13 (10) ◽  
pp. 5079-5102 ◽  
Author(s):  
Martin Dörenkämper ◽  
Bjarke T. Olsen ◽  
Björn Witha ◽  
Andrea N. Hahmann ◽  
Neil N. Davis ◽  
...  
Keyword(s):  
Wind Speed ◽  
Mesoscale Model ◽  
Comprehensive Evaluation ◽  
Positive Impact ◽  
Wrf Model ◽  
Added Value ◽  
Large Set ◽  
Mean Wind Speed ◽  
The Mean ◽  
Model Setup

Abstract. This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s−1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results.

scholarly journals WRF-simulated low-level jets over Iowa: characterization and sensitivity studies

2021 ◽  
Vol 6 (4) ◽  
pp. 1015-1030
Author(s):  
Jeanie A. Aird ◽  
Rebecca J. Barthelmie ◽  
Tristan J. Shepherd ◽  
Sara C. Pryor
Keyword(s):  
Wind Speed ◽  
Polynomial Interpolation ◽  
Wrf Model ◽  
Detection Algorithm ◽  
The State ◽  
Sensitivity Analyses ◽  
Vertical Resolution ◽  
Low Level ◽  
Low Level Jets ◽  
The Mean

Abstract. Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring in the contemporary climate. Low-level jets affect rotor plane aerodynamic loading, turbine structural loading and turbine performance, and thus accurate characterization and identification are pertinent. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20–530 m a.g.l. (above ground level) indicate the presence of an LLJ in at least one of the 14 700 4 km×4 km grid cells over Iowa on 98 % of nights. Nocturnal LLJs are most frequently associated with stable stratification and low turbulent kinetic energy (TKE) and hence are more frequent during the winter months. The spatiotemporal mean LLJ maximum (jet core) wind speed is 9.55 m s−1, and the mean height is 182 m. Locations of high LLJ frequency and duration across the state are seasonally varying, with a mean duration of 3.5 h. The highest frequency occurs in the topographically complex northwest of the state in winter and in the flatter northeast of the state in spring. Sensitivity of LLJ characteristics to the (i) LLJ definition and (ii) vertical resolution at which the WRF output is sampled is examined. LLJ definitions commonly used in the literature are considered in the first sensitivity analysis. These sensitivity analyses indicate that LLJ characteristics are highly variable with definition. Use of different definitions identifies both different frequencies of LLJs and different LLJ events. Further, when the model output is down-sampled to lower vertical resolution, the mean jet core wind speed height decreases, but spatial distributions of regions of high frequency and duration are conserved. Implementation of a polynomial interpolation to extrapolate down-sampled output to full-resolution results in reduced sensitivity of LLJ characteristics to down-sampling.

scholarly journals A deep CNN model for medium-range spatio-temporal wind speed prediction for wind energy applications

2021 ◽  
Author(s):  
Daan Scheepens ◽  
Katerina Hlavackova-Schindler ◽  
Claudia Plant ◽  
Irene Schicker
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Velocity ◽  
Wind Farms ◽  
Relative Vorticity ◽  
Horizontal Wind ◽  
Energy Applications ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Deep Cnn

&lt;p&gt;The amount of wind farms and wind power production in Europe, on-shore and off-shore, increased rapidly in the past years. To ensure grid stability, omit fees in energy trading, and on-time (re)scheduling of maintenance tasks accurate predictions of wind speed and wind energy is needed. Especially for the prediction range of +48 hours up to 2 weeks ahead at least hourly predictions are envisioned by the users. However, these are either not covered by the high-resolution models or are on a spatial and temporal course scale.&amp;#160;&lt;/p&gt;&lt;p&gt;To address this as a first step we therefore propose a deep CNN based model for wind speed prediction&amp;#160; using the ECMWF ERA5 to train our model using at least seven wind-related temporal variables, i.e. divergence, geopotential, potential vorticity, temperature, relative vorticity, vertical wind velocity and horizontal wind velocity.&lt;/p&gt;&lt;p&gt;The input of the CNN is represented by&amp;#160; the 3-dim tensor (size of the 2-dim figures x time shots), one for each variable. The CNN&amp;#160; outputs the most probable of the six categories in which the wind speed will be during the following 96 hours, in 6h intervals. Different combinations of input data are investigated in terms of temporal input.&lt;/p&gt;&lt;p&gt;We analyse the influence of prediction range on the predicted category as well as the relevance of each of the wind-related variables in the prediction of this category.&amp;#160; The model will be tested and applied to the ECMWF IFS forecasts over Austria. The ensure a higher spatial and temporal resolution an additional step will be used for downscaling the CNN directly to a 1 km grid.&lt;/p&gt;&lt;p&gt;This work is performed as part of the MEDEA project, which is funded by the Austrian Climate Research Program.&lt;/p&gt;

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