Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China
Climate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observator...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/6573202 |
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| author | Xiangxiang Li Xiaochen Qin Jun Yang Weiming Xu |
| author_facet | Xiangxiang Li Xiaochen Qin Jun Yang Weiming Xu |
| author_sort | Xiangxiang Li |
| collection | DOAJ |
| description | Climate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observatories (TTOs) in South China was assessed. For each TTO, linear and nonlinear downscaling techniques, namely, multiple linear regression (MLR) and an artificial neural network (ANN), respectively, were adopted for the downscaling of the scalar wind speed and the corresponding U/V components. The downscaled wind speed and U/V components were subsequently compared with the TTO observations by correlation coefficient (Pearson’s r), the root mean square error (RMSE), the uncertainty analysis (U95), and the reliability analysis (RE). According to the results, ERA5 had a better applicability (higher Pearson’s r and RE, but lower RMSE and U95) in estimating TTO wind speed than MERRA2 when using both the MLR and ANN downscaling method. The average Pearson’s r, RE, RMSE, and U95 of the downscaled wind from ERA5 by the MLR (ANN) method were 0.66 (0.69), 40.8% (41.8%), 2.20 m/s (2.11 m/s), 0.181 m/s (0.179 m/s), respectively, and 0.60 (0.63), 38.0% (39.7%), 2.32 m/s (2.25 m/s), 0.189 m/s (0.187 m/s), respectively, for MERRA2. The wind components analysis showed that the better performance of ERA5 was attributed to its smaller error in estimating V component than MERRA2. For the wind direction, the two reanalysis datasets did not display distinct differences. Additionally, the misalignment of the wind direction between the reanalysis products and the TTOs was higher for the secondary predominant wind direction (SPWD) than for the predominant wind direction (PWD). Furthermore, we found that the reanalysis U wind had a higher RMSE but a lower RE and Pearson’s r than the V wind, which indicates that the misalignment in the wind direction was mainly associated with the deviation in the U component. |
| format | Article |
| id | doaj-art-7be11b8b49eb4042aca2539484b1370f |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-7be11b8b49eb4042aca2539484b1370f2025-02-03T06:08:42ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/6573202Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South ChinaXiangxiang Li0Xiaochen Qin1Jun Yang2Weiming Xu3Meteorological Science Institute of Jiangxi ProvinceJiangxi Provincial Climate CenterMeteorological Science Institute of Jiangxi ProvinceMeteorological Science Institute of Jiangxi ProvinceClimate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observatories (TTOs) in South China was assessed. For each TTO, linear and nonlinear downscaling techniques, namely, multiple linear regression (MLR) and an artificial neural network (ANN), respectively, were adopted for the downscaling of the scalar wind speed and the corresponding U/V components. The downscaled wind speed and U/V components were subsequently compared with the TTO observations by correlation coefficient (Pearson’s r), the root mean square error (RMSE), the uncertainty analysis (U95), and the reliability analysis (RE). According to the results, ERA5 had a better applicability (higher Pearson’s r and RE, but lower RMSE and U95) in estimating TTO wind speed than MERRA2 when using both the MLR and ANN downscaling method. The average Pearson’s r, RE, RMSE, and U95 of the downscaled wind from ERA5 by the MLR (ANN) method were 0.66 (0.69), 40.8% (41.8%), 2.20 m/s (2.11 m/s), 0.181 m/s (0.179 m/s), respectively, and 0.60 (0.63), 38.0% (39.7%), 2.32 m/s (2.25 m/s), 0.189 m/s (0.187 m/s), respectively, for MERRA2. The wind components analysis showed that the better performance of ERA5 was attributed to its smaller error in estimating V component than MERRA2. For the wind direction, the two reanalysis datasets did not display distinct differences. Additionally, the misalignment of the wind direction between the reanalysis products and the TTOs was higher for the secondary predominant wind direction (SPWD) than for the predominant wind direction (PWD). Furthermore, we found that the reanalysis U wind had a higher RMSE but a lower RE and Pearson’s r than the V wind, which indicates that the misalignment in the wind direction was mainly associated with the deviation in the U component.http://dx.doi.org/10.1155/2022/6573202 |
| spellingShingle | Xiangxiang Li Xiaochen Qin Jun Yang Weiming Xu Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China Advances in Meteorology |
| title | Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China |
| title_full | Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China |
| title_fullStr | Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China |
| title_full_unstemmed | Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China |
| title_short | Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China |
| title_sort | comparison of the applicability of two reanalysis products in estimating tall tower wind based on multiple linear regression and artificial neural network in south china |
| url | http://dx.doi.org/10.1155/2022/6573202 |
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