Evaluating offshore wind resources in India using ERA5 reanalysis: a statistical approach
Bibliographic record
Abstract
This study assesses the suitability of ERA5 reanalysis data for offshore wind resource assessment along the coasts of Gujarat and Tamil Nadu, India. Using metrics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), and R-squared (R²), ERA5 wind speed and direction estimates were validated against observational data from lidar and meteorological mast measurements. Results demonstrate strong agreement, with correlation coefficients (R) of 0.949 and 0.958 for Gujarat and Tamil Nadu, respectively, and MBE values of -0.6 m/s and -0.55 m/s. Further analysis examined wind power density and Weibull distribution parameters across different offshore zones, highlighting substantial differences in wind characteristics between the two regions. Tamil Nadu’s Zone E recorded the highest wind power density at 609 W/m², along with a mean wind speed of 9.101 m/s and International Electrotechnical Commission (IEC) Wind Class I, indicating its suitability for high-capacity wind turbines. Conversely, Gujarat’s zones generally presented lower wind power densities, with values around 263-302 W/m², classifying most as IEC Wind Class III areas. Despite ERA5’s reliability, limitations exist in capturing complex offshore wind conditions influenced by dynamic atmospheric factors. Future research may incorporate advanced statistical models for more detailed wind characterization, aiding in optimized turbine placement and supporting India’s renewable energy targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".