The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global reanalysis ERA5 and two regional reanalyses CARRA and NORA3. This case study matches 238 scenes from 2022 for the Goliat station, an oil platform located 85 km northwest of Hammerfest in the Barents Sea, where a new offshore wind park has been proposed. The analysis showed that despite their unique limitations in spatial and temporal resolutions, all data sources have similar statistical properties (RMSE, correlation coefficient, and standard deviation). The Weibull parameters characterizing the wind speed distributions showed strong similarities between the Sentinel-1 and all reanalysis data. The Weibull parameters of the in situ measurements showed an underestimation of wind speed compared to all other sources. Comparing the full reanalysis datasets with the subsets matching the SAR scenes, only slight changes in Weibull parameters were found, indicating that, despite its low temporal resolution, the Sentinel-1 Level 2 OWI product can compete with the more commonly used reanalysis products in the estimation of offshore wind resources. Its high spatial resolution, which is unmatched by other methods, renders it especially valuable in offshore areas close to complex coastlines and in resolving weather events at a smaller scale.

In situ surface wind observation is a critical meteorological data source for various research fields. However, data quality is affected by factors such as surface friction changes, station relocations and anemometer updates. Previous methods to address discontinuities have been insufficient, and processing methods have not always adhered to World Meteorological Organization (WMO) World Climate Programme guidelines. We analyzed data discontinuity caused by anemometer changes and station relocations in China's daily in situ near-surface (∼ 10 m) wind speed observations and the impact of the processing methods on wind speed trends. By comparing the wind speed discontinuities with the recorded location changes, we identified 90 stations that showed abnormally increasing wind speeds due to relocation. After removing those stations, we followed a standard quality control method recommended by the World Meteorological Organization to improve the data reliability and applied Thiessen polygons to calculate the area-weighted average wind speed. The result shows that China's recent reversal of wind speed was reduced by 41 % after removing the problematic stations, with an increasing trend of 0.017 m s⁻¹ yr⁻¹ (R² = 0.64, P < 0.05), emphasizing the importance of robust quality control and homogenization protocols in wind trend assessments.

Accurate wind resource assessment depends on wind speed data that capture local wind conditions, crucial for energy estimates and site selection. The International Electrotechnical Commission (IEC) recommends at least one year of data collection, yet this duration may not fully account for interannual variability. While studies often maximize data length, guidance on the minimum duration required for reliable wind speed and power estimates remains limited. To address this gap, we propose a method to quantify the errors introduced by using wind speed series of different lengths for wind speed distributions fitting, relative to long-term data. This allows us to determine the minimum number of hourly observations needed to for a given accuracy level. We apply our method to in-situ weather station observations and ERA5 reanalysis data at 10-meter and 100-meter heights. Our results show that key parameters, including mean, standard deviation, and Weibull parameters, stabilize with relatively short records (~1 month of hourly data), whereas skewness requires at least 1.6 years, and kurtosis requires 88.6 years to stabilize. ERA5 data stabilize with fewer observations but differ from in-situ measurements, requiring careful use. Moreover, combining available hourly data for distribution fitting produces parameters comparable to those obtained when controlling for diurnal and seasonal effects, suggesting discontinuous data can be viable under certain conditions. These findings offer a practical framework for optimizing data collection in wind resource assessments, balancing accuracy and cost-effectiveness.

Accurate wind resource assessment depends on wind speed data that capture local wind conditions, which are crucial for energy yield estimates and site selection. While the International Electrotechnical Commission (IEC) recommends at least 1 year of data collection, this duration may be insufficient to fully capture interannual variability. Although studies often maximize data length, limited guidance exists on the minimum sample size required to reliably estimate wind statistics and energy potential. To address this gap, we propose a method to quantify errors in wind speed distribution parameters arising from the use of time series of varying lengths compared with long-term reference data. This enables us to determine the minimum number of hourly observations needed to achieve a given accuracy. We apply this method to in situ station observations and ERA5 reanalysis data at 10 and 100 m heights. Our results show that basic parameters (mean, standard deviation, and Weibull parameters) stabilize with a sample size equivalent to ∼ 1 month of hourly data (not a contiguous period) drawn across multiple years, while higher-order moments require substantially larger samples (skewness: equivalent to ∼ 1.6 years; kurtosis: equivalent to 88.6 years). Although ERA5 stabilizes faster, it exhibits systematic biases compared to in situ measurements. Moreover, random cross-year sampling yields comparable distribution parameters to diurnally or seasonally controlled sampling, while continuous sampling demands far longer records for the same accuracy. These findings provide a practical framework for optimizing data collection in wind resource assessments, balancing accuracy, temporal coverage, and resource constraints.

In the quest for accurate wind resource assessment crucial for the expansion of wind farms, this study tackles the scientific question of how varying time series lengths and temporal resolutions impact the estimation of wind resources, and introduce uncertainty into the assessment process. Recognizing the significant importance of considering temporal variability in wind speed distribution, we utilize in-situ observations from weather stations provided by the Norwegian Meteorological Institute, analyzing 1-hourly data spanning one to ten years. The study employs a comparative analysis of various wind speed distributions to determine the best-fit distribution for estimating wind resources. This process involves assessing the goodness-of-fit for each distribution under different time series lengths. Additionally, the study investigates the impact of temporal resolutions by examining data collected at 10-minute, hourly, daily, and monthly intervals from the same period and stations. The overarching goal is to systematically quantify uncertainty in wind resource estimation arising from the selection of wind speed distribution based on varying lengths and resolutions of time series data. The outcomes of this research aim not only to enhance the precision of wind resource assessments in the wind power sector but also to provide valuable insights applicable to fields influenced by wind conditions, including risk management and construction design. This study is financed by the Equinor academia project.