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Offshore wind speed observations often suffer from data gaps, limiting the accuracy of wind resource assessment and wind farm operation.
A ratio-based interpolation method for reconstructing missing wind speed data using ERA5 reanalysis and floating LiDAR observations is proposed. Taking 100 m wind speed data from a coastal buoy as a case study, the method is evaluated across annual scale, seasonal variability, wind speed levels, and typical extreme weather events.
Results show that the method effectively captures temporal wind speed trends, with an annual average correlation coefficient of 0.839. However, it tends to underestimate wind speed magnitudes, with errors increasing notably under high wind conditions, especially during convective summer periods and typhoon events. Compared to traditional linear regression methods, the ratio method performs better in maintaining trends and controlling errors, and it demonstrates greater stability and robustness under conditions of severe wind speed fluctuations or extreme weather.
Overall, the ratio method demonstrates good applicability in stable wind environments and is suitable for long-term wind resource evaluation and data reconstruction. Nevertheless, its accuracy under extreme weather remains limited, suggesting the need for integration with high-resolution simulations or multi-source data fusion approaches.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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