Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions
<p>Locations of 10 meteorological stations in the state of Kuwait.</p> "> Figure 2
<p>Measured seasonal wind rose diagrams for 10 stations (St01–St10) showing wind directional distribution and speed magnitude across different seasons.</p> "> Figure 2 Cont.
<p>Measured seasonal wind rose diagrams for 10 stations (St01–St10) showing wind directional distribution and speed magnitude across different seasons.</p> "> Figure 3
<p>The scatter plot compares the measured wind speeds versus ERA5 predicted wind speeds across all ten stations. Data points are color-coded by station to illustrate the model’s performance variability in different geographical locations. The dashed line represents the perfect agreement between measured and predicted values.</p> "> Figure 4
<p>Box plots showing wind speed distribution for coastal and offshore stations. The circles represent outliers (extreme events) located beyond 1.5<span class="html-italic">IQR</span> of the whisker.</p> "> Figure 5
<p>Box plots showing the monthly variability of wind speeds for coastal and offshore stations. The circles represent outliers (extreme events) located beyond 1.5<span class="html-italic">IQR</span> of the whisker.</p> "> Figure 6
<p>Seasonal (<b>top</b>) and monthly (<b>bottom</b>) average Pearson correlation coefficients between measured and ERA5 wind speed data for the ten stations.</p> "> Figure 7
<p>Taylor diagram showing the comparison between ERA5 and observed wind speed data for coastal stations. The Pearson correlation coefficient is on the polar axis, the red dashed circles represent the normalized RMSE, and the horizontal and vertical axes represent the <span class="html-italic">σ</span><sub>n</sub>.</p> "> Figure 8
<p>Taylor diagram showing the comparison between ERA5 and observed wind speed data for offshore stations. The Pearson correlation coefficient is on the polar axis, the red dashed circles represent the normalized RMSE, and the horizontal and vertical axes represent the <span class="html-italic">σ</span><sub>n</sub>.</p> "> Figure 9
<p>Comparison of measured and modeled wind speed frequency distributions across the stations with Perkins Skill Score (PSS). The shaded blue areas represent the overlap between measured (dashed blue lines) and modeled (solid red lines) distribution.</p> "> Figure 10
<p>BSS evaluation across stations (St01–St10), showing the model’s performance relative to the baseline (BSS = 0). Positive BSS values indicate that the model outperforms the reference approach, while negative values highlight underperformance.</p> "> Figure 11
<p>ERA5 seasonal wind rose diagrams for the ten stations (st01–st10) showing wind directional distribution and speed magnitude across different seasons.</p> "> Figure 11 Cont.
<p>ERA5 seasonal wind rose diagrams for the ten stations (st01–st10) showing wind directional distribution and speed magnitude across different seasons.</p> ">
Abstract
:1. Introduction
2. Measured and ERA5 Data
2.1. Measured Data
2.2. ERA5 Data
2.3. Kuwait’s Coastal and Offshore Wind Climate
3. Methodology
4. Results and Discussion
4.1. Initial Assessment
4.2. Correlation Between the Measured and ERA5 Wind Speeds and the Goodness of Fit Measures Across the Stations
4.3. Evaluation of ERA5 Model Performance Using PSS and BSS
4.4. Wind Direction
5. Conclusions and Recommendations
5.1. Recommendations
- Localized calibration: Recalibrate ERA5 using high-resolution observational datasets to account for regional characteristics, particularly in coastal areas where land–sea interactions and mesoscale dynamics play a significant role.
- Hybrid modeling approaches: Combine ERA5 data with machine learning and hybrid models to enhance its ability to predict extreme events and represent localized wind variability more effectively.
- Enhanced observational networks: Develop and maintain high-resolution, long-term meteorological monitoring networks in Kuwait and similar regions. These networks can reduce data scarcity, support model validation, and improve extreme event representation.
- Extreme event focus: Improve the model’s capability to represent extreme wind events by refining its spatial resolution and adopting probabilistic frameworks that address spatiotemporal dependencies and quantify uncertainties.
- Advanced model refinement: Explore methodologies such as ensemble calibration, wind–wave interaction models, and improved parameterization schemes to mitigate biases and enhance the model’s performance in transitional and extreme conditions.
5.2. Future Directions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Station | Station Name | Code | Longitude | Latitude | Start Date | End Date | % Missing Records |
---|---|---|---|---|---|---|---|
1 | Ahmadi Oil Pier | St01 | 48.16153 | 29.05958 | 1 January 2010 | 30 September 2017 | 1.02 |
2 | Beacon N6 | St02 | 48.20727 | 29.57306 | 1 January 2010 | 30 December 2017 | 0.44 |
3 | Beacon M28 | St03 | 48.60239 | 29.51936 | 1 January 2010 | 10 May 2015 | 0.35 |
4 | Julaia Port | St04 | 48.28982 | 28.86466 | 1 January 2010 | 30 September 2017 | 0.78 |
5 | Umm Mudayrah | St05 | 48.77395 | 28.93628 | 1 July 2013 | 30 December 2017 | 1.71 |
6 | Umm Almaradim Island | St06 | 48.6528 | 28.67652 | 1 January 2010 | 30 December 2017 | 0.42 |
7 | Salmiyah | St07 | 48.1014 | 29.34619 | 1 January 2010 | 30 December 2017 | 0.29 |
8 | South Dolphin | St08 | 47.99495 | 29.41367 | 1 January 2010 | 30 December 2017 | 0.22 |
9 | Qarouh Island | St09 | 48.77435 | 28.81619 | 3 May 2010 | 31 December 2017 | 0.56 |
10 | Sea Island Buoy | St10 | 48.29861 | 29.11222 | 1 January 2010 | 31 July 2017 | 11.33 |
Station | |||
---|---|---|---|
St01 | 0.71 | 0.68 | 0.5 |
St02 | 0.77 | 0.75 | 0.56 |
St03 | 0.69 | 0.69 | 0.5 |
St04 | 0.72 | 0.7 | 0.51 |
St05 | 0.85 | 0.84 | 0.66 |
St06 | 0.79 | 0.78 | 0.59 |
St07 | 0.59 | 0.58 | 0.41 |
St08 | 0.72 | 0.69 | 0.51 |
St09 | 0.83 | 0.81 | 0.63 |
St10 | 0.69 | 0.68 | 0.5 |
Station | σn | NME | NCRMSE | σr |
---|---|---|---|---|
St01 | 0.83 | −0.06 | 0.71 | 1.82 |
St02 | 0.91 | 0.00 | 0.66 | 1.61 |
St03 | 1.18 | −0.20 | 0.88 | 1.84 |
St04 | 0.95 | −0.06 | 0.74 | 1.63 |
St05 | 0.92 | 0.01 | 0.53 | 1.47 |
St06 | 0.87 | 0.02 | 0.62 | 1.67 |
St07 | 1.04 | −0.06 | 0.92 | 1.92 |
St08 | 0.80 | 0.03 | 0.70 | 1.88 |
St09 | 0.90 | −0.07 | 0.56 | 1.58 |
St10 | 0.90 | −0.12 | 0.75 | 1.86 |
Average | 0.93 | −0.05 | 0.71 | 1.73 |
Maximum | 1.18 | 0.03 | 0.92 | 1.92 |
Minimum | 0.80 | −0.20 | 0.53 | 1.47 |
Station | R2 | E | IA | FAC2 |
---|---|---|---|---|
St01 | 0.50 | 0.47 | 0.83 | 0.83 |
St02 | 0.59 | 0.57 | 0.87 | 0.88 |
St03 | 0.47 | 0.04 | 0.79 | 0.85 |
St04 | 0.51 | 0.44 | 0.84 | 0.90 |
St05 | 0.73 | 0.72 | 0.92 | 0.92 |
St06 | 0.62 | 0.61 | 0.88 | 0.91 |
St07 | 0.35 | 0.12 | 0.76 | 0.86 |
St08 | 0.52 | 0.51 | 0.83 | 0.85 |
St09 | 0.69 | 0.66 | 0.90 | 0.88 |
St10 | 0.48 | 0.38 | 0.82 | 0.82 |
Average | 0.55 | 0.45 | 0.84 | 0.87 |
Maximum | 0.73 | 0.72 | 0.92 | 0.92 |
Minimum | 0.35 | 0.04 | 0.76 | 0.82 |
Station | PSS | RMSE | MAE | Bias |
---|---|---|---|---|
St01 | 0.86 | 1.84 | 1.42 | 0.32 |
St02 | 0.91 | 1.61 | 1.26 | 0.03 |
St03 | 0.79 | 2.05 | 1.62 | 0.92 |
St04 | 0.89 | 1.66 | 1.29 | 0.29 |
St05 | 0.94 | 1.47 | 1.12 | −0.05 |
St06 | 0.93 | 1.68 | 1.28 | −0.14 |
St07 | 0.93 | 1.95 | 1.54 | 0.30 |
St08 | 0.87 | 1.89 | 1.47 | −0.19 |
St09 | 0.88 | 1.63 | 1.24 | 0.41 |
St10 | 0.84 | 1.96 | 1.47 | 0.62 |
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Alkhalidi, M.; Al-Dabbous, A.; Al-Dabbous, S.; Alzaid, D. Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions. J. Mar. Sci. Eng. 2025, 13, 149. https://doi.org/10.3390/jmse13010149
Alkhalidi M, Al-Dabbous A, Al-Dabbous S, Alzaid D. Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions. Journal of Marine Science and Engineering. 2025; 13(1):149. https://doi.org/10.3390/jmse13010149
Chicago/Turabian StyleAlkhalidi, Mohamad, Abdullah Al-Dabbous, Shoug Al-Dabbous, and Dalal Alzaid. 2025. "Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions" Journal of Marine Science and Engineering 13, no. 1: 149. https://doi.org/10.3390/jmse13010149
APA StyleAlkhalidi, M., Al-Dabbous, A., Al-Dabbous, S., & Alzaid, D. (2025). Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions. Journal of Marine Science and Engineering, 13(1), 149. https://doi.org/10.3390/jmse13010149