WRF model sensitivity studies and specifications for the NEWA mesoscale wind atlas production runs

Witha, Björn; Hahmann, Andrea N.; Sīle, Tija; Dörenkämper, Martin; Ezber, Yasemin; García‐Bustamante, Elena; González‐Rouco, J. F.; Leroy, Grégoire; Navarro, Jorge

doi:10.5281/zenodo.2682604

Cited by 8 publications

(14 citation statements)

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“…WRF has higher overall RMSE values and lower R 2 compared to the intercomparisons of the other measured datasets, all summarized in Table 5. FINO2 yielded the highest overall error at both heights which is consistent with [14,43]. This is seen in both the 30 min direct comparison and the 1 h comparison similar to that in [14].…”

Section: Wrfsupporting

confidence: 86%

“…The New European Wind Atlas (NEWA) [14] provides a publicly available wind resource dataset for Europe. It is based on 30 years of mesoscale simulations using the Weather Research and Forecasting (WRF) model [15], available every 30 min with a 3 km × 3 km grid resolution.…”

Section: Introductionmentioning

confidence: 99%

“…It is based on 30 years of mesoscale simulations using the Weather Research and Forecasting (WRF) model [15], available every 30 min with a 3 km × 3 km grid resolution. As mesoscale models are not specifically developed for wind energy applications, the main objective for the NEWA project was to gather the best practices and to create a unified modeling methodology [14]. Although WRF has been shown to compare well with measurements at relatively shallow-water offshore locations [16], deep sea sites have not been assessed, and site-specific measurements are still vital to study the wind climate.…”

Section: Introductionmentioning

confidence: 99%

“…[20,27,28] studied satellite synthetic aperture radar (SAR) and ASCAT measurements over the area of interest with [19], using the FINO2 location as reference for the long-term stability correction used in profile extrapolation. A study of the NEWA WRF model production runs used in this study with that of the FINO met masts is detailed and presented in [14].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

2022

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This article highlights the inter-comparisons of the wind measurement techniques available in deep water areas working towards combining them to obtain optimal estimates of the wind power potential. More specifically, this article presents comparisons of the Ferry Lidar Experiment wind data with those of the Advanced Scatterometer (ASCAT), the FINO2 meteorological mast, and the New European Wind Atlas (NEWA) simulations performed using the Weather Research, and Forecasting (WRF) mesoscale model. To be comparable to ASCAT surface winds, which are referenced at 10 m, the ferry lidar and FINO2 wind profile measurements were extrapolated down to 10 m using atmospheric stability information derived from the bulk Richardson number formulation. ASCAT had the lowest associated error compared with that of the ferry lidar in near-neutral atmospheric stratifications, whereas FINO2, despite a distance range of 30 km and a moving ferry lidar target, had the highest correlation and lowest RMSE in all atmospheric conditions. Due to the high frequency of low-level jets caused by the proximity to land from all directions as well as typically stable atmospheric conditions, the extrapolated ferry lidar measurements underpredicted the ASCAT 10 m wind speeds. WRF consistently underperformed compared to the other measurement methods, even with the ability to directly compare results with all other sources at all heights.

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Section: Wrfsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

2022

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“…From these models, the wind-resource potential was analyzed in large regions with high precision [139], reducing the time cost required to establish wind-power plant areas. The dataset has 30 years (1989-2018) of mesoscale simulations with the Weather, Research, and Forecasting (WRF) model at a 3 km 2 resolution [140]. With regard to the data download options, only the mesoscale model data are able to be downloaded.…”

Section: Characterization Of Wind Data Sourcesmentioning

confidence: 99%

Online Wind-Atlas Databases and GIS Tool Integration for Wind Resource Assessment: A Spanish Case Study

et al. 2022

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Renewable energy sources are becoming increasingly integrated into the electricity-generation sector, being eco-friendly solutions, decreasing global warming, and improving the energy transition process. Among the different renewables, wind energy is considered a mature, clean, renewable, and inexhaustible technology as well, becoming one of the main resources in a sustainable framework. Aiming to evaluate the wind resource, scientific contributions have mostly presented a common basis: historical data campaigns of the wind resource mainly considering wind speed—including the module, direction, standard deviation, etc. However, online wind-atlas databases are becoming tools widely used for both wind-resource assessment and optimal wind-power locations. Under this framework, this study analyzed and compared such online wind data sources and their integration with GIS tools for optimal wind-resource-assessment purposes. The proposed methodology identified the corresponding wind-atlas databases directly on their websites and indirectly through the wind data used in relevant contributions about the optimal location of wind sites. Our contribution to the scientific community is thus the review and comparison of these atlas databases for reducing the barrier to access wind data—including GIS-tool-integration analysis. The limitations raised by civil societies, particularly regarding environmental and bird concerns, were not included in this study. Nevertheless, the authors are aware of these concerns and limitations. A Spanish case study was also included in this work, comparing both estimated and collected wind-atlas databases in terms of wind-resource assessment.

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A new Wind Atlas to support the expansion of the Italian wind power fleet

Sperati,

Alessandrini,

D'Amico

et al. 2024

Wind Energy

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As a contribution to national strategic energy planning, recent developments in meteorological modeling and wind generation technologies have improved the representation of the spatio‐temporal features of wind. This paper describes an updated Italian Wind Atlas (Atlante EOLico ItaliANo [AEOLIAN]) released in the early 2000s. The objective of AEOLIAN is to guide future wind generation to accord with ambitious European greenhouse gas emission targets set for 2030 and 2050. AEOLIAN is the result of a collaboration effort between Ricerca sul Sistema Energetico (RSE) SpA and the National Center for Atmospheric Research (NCAR), which jointly developed a novel approach combining high‐resolution numerical weather modeling with the Analog Ensemble (AnEn) statistical technique. This paper uses dynamical model runs with hourly output for 1990–2019 with 4 km horizontal grid spacing. For 2015–2019, an inner grid nest with 1.33 km horizontal grid spacing is used. The AnEn is then employed to temporally extend the 5 years of high‐resolution runs back through 1990–2014 to create a 30‐year dataset for Italy and surrounding marine areas. A thorough verification is carried out using 104 observational stations homogeneously distributed throughout the territory. Compared with other state‐of‐the‐art products, AEOLIAN provides enhanced accuracy over complex terrain thanks to higher horizontal resolution and the assimilation of observational wind data over the domain, which result in a reduction of model bias on complex terrain and a better reconstruction of the wind distributions. Finally, a new WebGIS interface (https://atlanteeolico.rse-web.it/) to explore AEOLIAN data is described.

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WRF model sensitivity studies and specifications for the NEWA mesoscale wind atlas production runs

Cited by 8 publications

References 0 publications

Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

Comparing Offshore Ferry Lidar Measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF

Online Wind-Atlas Databases and GIS Tool Integration for Wind Resource Assessment: A Spanish Case Study

A new Wind Atlas to support the expansion of the Italian wind power fleet

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