the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Mar 2026
Global-ABLWind: a global atmospheric boundary layer wind speed profile dataset derived from Aeolus and surface ancillary information
Abstract. Accurate wind speed profiles within the atmospheric boundary layer (ABL) are essential for understanding atmospheric processes, climate change, and wind energy assessment. However, existing global ABL wind products lack either sufficient vertical resolution or accuracy, limiting their ability to resolve wind structures throughout the ABL. Here, we propose a physics-constrained machine learning framework designed to reconstruct continuous ABL wind speed profiles by integrating physically interpretable bias-correction mechanisms with dynamical constraints from Aeolus L2C observations. The proposed method enables the reconstruction of high-accuracy wind speed profiles at a vertical resolution of 100 m across the full ABL (0–2 km), overcoming the trade-off between accuracy and vertical resolution that characterizes existing products. Independent validation against RS observations demonstrates that the proposed method achieves high accuracy across all ABL heights. It has an overall correlation coefficient (R) of 0.92 and a root mean square error (RMSE) of 1.94 m s-1, outperforming the original Aeolus L2C product (R = 0.90, RMSE = 2.23 m s-1). Further comparisons at 100 m vertical resolution with the fifth generation ECMWF reanalysis (ERA5) and the power law method confirm the superior accuracy of XGB-Wind, especially in the near-surface layer (0–500 m). Applying the proposed framework to the full Aeolus mission period (from July 2020 to April 2023), we generate a global high-resolution ABL wind speed profile dataset, termed Global-ABLWind. This dataset provides 100 m vertical resolution wind profiles with enhanced accuracy, continuous ABL coverage, and reduced data gaps on a global scale. The dataset is freely available at https://doi.org/10.5281/zenodo.18286457 (Tong et al., 2026) and represents a valuable remote sensing resource for boundary-layer wind studies and wind-related environmental applications.
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Status: open (until 09 Jul 2026)
- RC1: 'Comment on essd-2026-73', Anonymous Referee #1, 29 May 2026 reply
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RC2: 'Comment on essd-2026-73', Anonymous Referee #1, 29 May 2026
reply
This manuscript presents Global-ABLWind, a global atmospheric boundary layer wind speed profile dataset derived from Aeolus L2C observations, radiosonde measurements, and ERA5 ancillary variables for July 2020 to April 2023. The stated aim is to reconstruct wind speed profiles at 100 m vertical spacing within the atmospheric boundary layer using an XGBoost-based framework with near-surface bias-correction terms and upper-level Aeolus dynamical constraints. The dataset is potentially valuable because global high-vertical-resolution ABL wind profiles are difficult to obtain from existing observing systems, and the combination of Aeolus wind lidar information with radiosonde and reanalysis constraints addresses a relevant community need. This manuscript can be published after considering the following comments concerning clarifications or extensions:
- 3.2.2 states that 10 m RS wind speed is used as the bottom constraint during model construction because ERA5 10 m wind correlates weakly with 100 m RS wind speed. Sect. 4.3 then states that the global product uses ERA5 10 m wind speed as the alternative input where RS is unavailable. The reported model-validation metrics are derived from a configuration using RS 10 m wind, whereas the released global product depends on ERA5 10 m wind. The rationality of this substitution needs further clarification.
- 4.3 states that Global-ABLWind maintains the same spatial and temporal resolution as Aeolus L2C. The product is therefore along Aeolus observation profiles rather than a globally gridded continuous field. Several figures use global map shading that visually resembles gridded continuous coverage. The author should provide a clearer explanation in the manuscript.
- The bias-correction method relies on quadratic relationships between binned Ray/Mie wind biases and selected variables. The statistical basis of these relationships is unclear because the manuscript does not specify how bins were defined, how sample sizes vary by bin, whether the R² values are based on raw samples or binned means, or whether these fitted corrections were estimated on independent data from the validation folds.
- The term “missing rate” appears throughout the manuscript, but it is not always clear whether it refers to missing height levels within an observed profile, missing Aeolus profiles, missing valid retrievals, missing model outputs, or missing map bins after aggregation.
- The relationship between the Ray-based and Mie-based products is not sufficiently explicit. Whether they use the same processing procedure is not clearly stated. The main text is based on Ray wind, while the supplement presents Mie-derived results. However, the manuscript does not make sufficiently clear whether the released dataset should be regarded as one primary Ray-based product with a secondary Mie-based companion, two parallel products, or two alternative realizations of the same product. This distinction is important for users deciding which files to use under different atmospheric or cloud/aerosol conditions.
- The inclusion of R and RMSE in a hyperparameter table is ambiguous because they are performance metrics rather than hyperparameters. The RMSE units are not shown in the table.
- Terminology is inconsistent in several places. The most conspicuous example is Fig. 12 and Fig. S10, where the legend reads “Global-PBLWind” while the manuscript and data record describe “Global-ABLWind”. The title of section 3.2 is “XBG-Wind”, while elsewhere in the manuscript it is “XGB-Wind”. Fig. 8 also has panel-lettering inconsistencies between the visual layout and caption. Tables 1 and S1 contain the misspelling “learing_rate.” The title of the subgraph in Figure 2 does not match the content. Please carefully review the entire manuscript.
Citation: https://doi.org/10.5194/essd-2026-73-RC2
Data sets
Global atmospheric boundary layer wind speed profile dataset derived from Aeolus observations from July 2020 to April 2023 Zhe Tong et al. https://doi.org/10.5281/zenodo.18286457
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This manuscript presents Global-ABLWind, a global atmospheric boundary layer wind speed profile dataset derived from Aeolus L2C observations, radiosonde measurements, and ERA5 ancillary variables for July 2020 to April 2023. The stated aim is to reconstruct wind speed profiles at 100 m vertical spacing within the atmospheric boundary layer using an XGBoost-based framework with near-surface bias-correction terms and upper-level Aeolus dynamical constraints. The dataset is potentially valuable because global high-vertical-resolution ABL wind profiles are difficult to obtain from existing observing systems, and the combination of Aeolus wind lidar information with radiosonde and reanalysis constraints addresses a relevant community need. This manuscript can be published after considering the following comments concerning clarifications or extensions: