scholarly journals Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

2021 ◽  
Vol 6 (6) ◽  
pp. 1473-1490
Author(s):  
Alexander Basse ◽  
Doron Callies ◽  
Anselm Grötzner ◽  
Lukas Pauscher
Keyword(s):  
Wind Speed ◽  
Reference Data ◽  
Measurement Data ◽  
Theoretical Framework ◽  
Reanalysis Data ◽  
Seasonal Effects ◽  
Data Sets ◽  
Mean Wind Speed ◽  
Wind Measurements

Abstract. Measure–correlate–predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on-site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on-site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

scholarly journals Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

2021 ◽  
Author(s):  
Alexander Basse ◽  
Doron Callies ◽  
Anselm Grötzner ◽  
Lukas Pauscher
Keyword(s):  
Reference Data ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Seasonal Effects ◽  
Data Sets ◽  
Data Set ◽  
Abstract Measure ◽  
Wind Measurements ◽  
Theoretical Considerations

Abstract. Measure-Correlate-Predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared, namely Variance Ratio and Linear Regression with Residuals. Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. Besides experimental results, theoretical considerations are presented which provide the mathematical background for understanding the observations. General relationships are derived which trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. This allows the transfer of the results of this study to different measurement durations, other reference data sets and other regions of the world. In this context, it is shown both theoretically and experimentally that the results do not only depend on the selected reference data set but also significantly change with the choice of the MCP method.

Ten years of the Wind Atlas of South Africa: Final results from WASA 3

2021 ◽  
Author(s):  
Andrea Hahmann ◽  
Chris Lennard ◽  
Rogier Floors ◽  
Dalibor Cavar ◽  
Niels G. Mortensen ◽  
...  
Keyword(s):  
South Africa ◽  
Wind Speed ◽  
Power Density ◽  
Horizontal Resolution ◽  
Data Sets ◽  
Error Statistics ◽  
Mean Power ◽  
Mean Wind Speed ◽  
Wind Climate

<p>We present the evolution of the methods used to create and validate the various numerical wind atlases during the past ten years of the Wind Atlas for South Africa (WASA) project. In WASA 3, we improved on the previous numerical wind atlases by:</p><ul><li>Creating an ensemble of 2-year simulations to find the optimal set of parameterisations and surface conditions for the wind climate of South Africa.</li> <li>Using a new method of generalisation and downscaling of the WRF-derived wind climate using the PyWAsP engine.</li> <li>Producing the most extensive to date wind climatology for South Africa, 30 years (1990–2019) simulation covering all South Africa at 3.33 km × 3.33 km spatial resolution and 30 minutes time output.</li> </ul><p>We will discuss these three areas and their improvements to the wind atlas' quality. The WASA 3 wind atlas' final error statistics show that the new WRF + PyWAsP method has a MAPE of 11.8% and 3.5% for the long-term mean power density and mean wind speed, respectively. These statistics are improved from those in WASA 1 and WASA 2.</p><p>When disregarding the two masts (WM09 and WM11) located in highly complex terrain, where the methodology was never designed, the use of the WRF and WRF + PyWAsP downscaling narrows the error distributions for both long-term wind speed and power density compared to the global reanalysis, ERA5.</p><p>The validated numerical wind atlas has further been used to model the wind resources of the entire land area of South Africa using the microscale WAsP model. Raster data exist with a horizontal resolution of 250 meters and three levels of 50, 100 and 150 meters a.g.l. of mean wind speed, power density, air density, Weibull <em>A </em>and<em> k </em>parameters, and ruggedness index.  These data sets and the WRF dataset will be made available in the public domain at the end of the project. Data sets for other heights above the ground and offshore can easily be added later.</p>

scholarly journals Winter Subseasonal Wind Speed Forecasts for Finland from ECMWF

2021 ◽  
Vol 18 ◽  
pp. 127-134
Author(s):  
Otto Hyvärinen ◽  
Terhi K. Laurila ◽  
Olle Räty ◽  
Natalia Korhonen ◽  
Andrea Vajda ◽  
...  
Keyword(s):  
Wind Speed ◽  
Lead Time ◽  
Reference Data ◽  
Data Sets ◽  
Weather Forecasts ◽  
The Third ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Mean Wind Speed ◽  
Gaussian Regression

Abstract. The subseasonal forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts) were used to construct weekly mean wind speed forecasts for the spatially aggregated area in Finland. Reforecasts for the winters (November, December and January) of 2016–2017 and 2017–2018 were analysed. The ERA-Interim reanalysis was used as observations and climatological forecasts. We evaluated two types of forecasts, the deterministic forecasts and the probabilistic forecasts. Non-homogeneous Gaussian regression was used to bias-adjust both types of forecasts. The forecasts proved to be skilful until the third week, but the longest skilful lead time depends on the reference data sets and the verification scores used.

scholarly journals ON THE CORRECTION OF LAND-BASED WIND MEASUREMENTS FOR OCEANOGRAPHIC APPLICATIONS

1980 ◽  
Vol 1 (17) ◽  
pp. 43 ◽  
Author(s):  
S.A. Hsu
Keyword(s):  
Wind Speed ◽  
Gulf Of Alaska ◽  
Internal Boundary Layer ◽  
Internal Boundary ◽  
Data Sets ◽  
Onshore Wind ◽  
Mean Wind Speed ◽  
Wind Measurements ◽  
Nocturnal Inversion

Simultaneous offshore and onshore wind measurements were made at stations ranging from Somalia, near the equator, to the Gulf of Alaska. Offshore data obtained from standard U.S. NOAA buoys, research platforms, and merchant ships were compared with data from coastal stations. The results indicated that, under the commonly observed speed of 5-6 m/s, land measurements of mean wind speed are only 63% of the offshore mean speed. Furthermore, it was found that only those stations located in the beach area that measure wind speed above both the internal boundary layer and the nocturnal inversion height represent offshore conditions. In order to correct land-measured wind data, a formula is developed and verified by all 2/3 existing data sets. A simplified equation, i.e., U = 3 U1 s is proposed for offshore applications. Criteria for in situ wind measurements near the coast are outlined. Data reduction procedures for inland stations are also provided.

scholarly journals High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2

2013 ◽  
Vol 6 (2) ◽  
pp. 779-809 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
Horizontal Grid ◽  
Global Reanalysis ◽  
Marine Climate

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

Analysis of Environmental Conditions for the Conceptual Design of a 200 MW Floating Offshore Wind Farm in the East Sea, Korea

2019 ◽  
Author(s):  
Hyunkyoung Shin ◽  
Youngjae Yu ◽  
Thanh Dam Pham ◽  
Hyeonjeong Ahn ◽  
Byoungcheon Seo ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Environmental Conditions ◽  
Early Stage ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
East Sea ◽  
Wind Resource

Abstract Due to global climate change, concern regarding the environment is greater than ever. Also, the energy industry is constantly developing and investing in new and renewable energy to reduce carbon emissions. Korea is planning to increase the proportion of renewable energy generation to 20% by 2030, in accordance with the 3020 renewable energy policy. This will involve 16.5 GW (34%) from wind energy, with a capacity from offshore wind energy of approximately 13 GW. Considering domestic technological wind resource potential (33.2 GW), it seems to be a sufficient target amount. However, in order to start the wind power generation business, the installation area must be analyzed for environmental information, for the evaluation of the wind resource and the early-stage concept design. Because it is difficult to conduct long-term measurements of the entire sea area, the environmental conditions are generally estimated from short-term measurement data and long-term reanalysis data. In this study, the environmental conditions of the East Sea of Korea were selected, and a comparative analysis was performed on the meteorological agency’s oceanic meteorology buoy data, ERA-5 reanalysis data obtained from ECMWF, and NASA’s MERRA-2 data. The extreme sea states of 50 years and 100 years were analyzed by extreme statistical analysis. Finally, environmental conditions required for the basic design of wind turbines were selected following IEC and DNV standards.

scholarly journals Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

2020 ◽  
Vol 10 (24) ◽  
pp. 9017
Author(s):  
Andoni Gonzalez-Arceo ◽  
Maitane Zirion-Martinez de Musitu ◽  
Alain Ulazia ◽  
Mario del Rio ◽  
Oscar Garcia
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Energy Production ◽  
Cost Effective ◽  
Reanalysis Data ◽  
Small Scale ◽  
Wide Range ◽  
Wind Measurements ◽  
Scale Experiment ◽  
Wind Resource

In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.

scholarly journals Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

2018 ◽  
Author(s):  
Laban N. Ongaki ◽  
Christopher M. Maghanga ◽  
Joash Kerongo
Keyword(s):  
Wind Speed ◽  
Rayleigh Distribution ◽  
Series Data ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Speeds ◽  
Average Wind ◽  
Mean Wind Speed ◽  
Wind Energy Potential

The research sought to investigate the long term characteristics of wind in the Kisii region (elevation 1710m above sea level, 0.68oS, 34.79o E). Wind speeds were analyzed and characterized on short term (per month for a year) and then simulated for long term (ten years) measured hourly series data of daily wind speeds at a height of 10m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent; the mean wind speed, diurnal variations, daily variations as well as the monthly variations. The wind speed frequency distribution at the height 10 m was found to be 2.9ms-1 with a standard deviation of 1.5. Based on the two month’s data that was extracted from the AcuRite 01024 Wireless Weather Stations with 5-in-1 Weather Sensor experiments set at three sites in the region, averages of wind speeds at hub heights of 10m and 13m were calculated and found to be 1.7m/s, 2.0m/s for Ikobe station, 2.4m/s, 2.8m/s for Kisii University stations, and 1.3m/s, 1.6m/s for Nyamecheo station respectively. Then extrapolation was done to determine average wind speeds at heights (20m, 30m, 50m, and 70m) which were found to be 85.55W/m2, 181.75W/m2, 470.4W/m2 and 879.9W/m2 respectively. The wind speed data was used statistically to model a Weibull probability density function and used to determine the power density for Kisii region.

scholarly journals The Potential Wind Power Resource in Central-South of the Constanta County

2019 ◽  
Vol 15 (3) ◽  
pp. 1-12
Author(s):  
Emilian Boboc
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Extreme Values ◽  
Reanalysis Data ◽  
Value Theory ◽  
Physical Models ◽  
Structural Safety ◽  
Energy Yield ◽  
Wind Speeds ◽  
Mean Wind Speed

Abstract Usually, wind turbine generator’s structures or radio masts are located in wind exposed sites. The paper aims to investigate the wind conditions in the nearby area of Cobadin Commune, Constanta County, Romania at heights of 150-200m above the surface using global reanalysis data sets CFSR, ERA 5, ERA I and MERRA 2. Using the extreme value theory and the physical models of the datasets, the research focuses on the assessment of the maximum values that are expected for the wind speeds, but the wind statistics created can be used for a further wind or energy yield calculation. Without reaching the survival wind speed for wind turbine generators, with mean wind speed values higher than 7 m/s and considering the cut-in and cut-out wind speeds of 3 m/s, respectively 25 m/s, the site can be exploited in more than 90% of the time to generate electricity, thus, the paper is addressed to the investors in the energy of renewable sources. At the same time, the insights of the wind characteristics and the knowledge of the extreme values of the wind speed can be useful, not just for the designers, in the rational assessment of the structural safety of wind turbines, but also those evaluating the insured losses.

scholarly journals Understanding and mitigating the impact of data gaps on offshore wind resource estimates

2021 ◽  
Vol 6 (2) ◽  
pp. 505-520
Author(s):  
Julia Gottschall ◽  
Martin Dörenkämper
Keyword(s):  
Wind Energy ◽  
Mesoscale Model ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Offshore Wind ◽  
Data Gaps ◽  
Mean Wind Speed ◽  
Harsh Climate ◽  
Almost All ◽  
The Impact

Abstract. Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps, in terms of a bias in the estimation of siting parameters and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering 2 years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact, i.e. a reduction of the observed biases, by a factor of 10 on mean wind speed, direction and Weibull scale parameter and a factor of 3 on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data.

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