Evaluation and Calibration of Remotely Sensed High Winds from the HY-2B/C/D Scatterometer in Tropical Cyclones
Bibliographic record
Abstract
Haiyang-2 scatterometers (HY-2A/B/C/D) have limitations in high wind speed retrieval due to the complexity of the remote sensing mechanism and the influence of rainfall on the radar cross section under the conditions of tropical cyclones. In this study, we focus on the evaluation of Chinese scatterometer operational wind products from HY-2B/C/D over the period from July 2019 to December 2021. HY-2B/C/D scatterometer wind products are collocated with SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The results show that the underestimation of high wind speed occurs in the HY-2B/C/D wind speed products. The machine learning algorithms are explored to improve this underestimation issue, including the back propagation neural network (BP-NN), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and Bayesian ridge (BR) regression algorithms. Comparisons show that the BP-NN algorithm shows the best performance with a small RMSE (root-mean-square error) of 3.40 m/s, and high correlation coefficient of 0.88, demonstrating an improvement of 20.4% in RMSE (root-mean-square error) compared with the HY-2B/C/D wind speed products. In addition, the revised high winds are in good agreement with the ground truth measurements from the SFMR (Stepped Frequency Microwave Radiometer), which are useful for tropical cyclone disaster prevention and mitigation and are of vital importance in the numerical simulation of storm surges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".