Comparing Halo Doppler lidar depolarization ratio with PollyXT
Bibliographic record
Abstract
Depolarization ratio is highly valuable in lidar-based aerosol classification and can be used to quantify the contributions of different aerosol types to elevated layers [1]. Typically, aerosol particle depolarization ratio is determined at relatively short wavelengths of 355 nm and/or 532 nm, though some multi-wavelength case studies including 1064 nm have shown strong spectral dependency [2,3]. Here, we demonstrate that Halo Photonics Stream Line Doppler lidars can be used to retrieve aerosol particle depolarization ratio at 1.5 µm wavelength. We utilize measurements in April-May 2017 at Limassol, Cyprus to compare the Halo 1.5 µm aerosol particle depolarization ratio with Polly XT aerosol particle depolarization ratio. Recently developed post-processing [4] enables retrieving weak signals (as low as -32 dB) with the Halo Doppler lidar. At Limassol, we were able to determine particle depolarization ratio for several cases of mineral dust up to 3 km above ground. Generally, particle depolarization ratio for mineral dust at 1.5 µm appears higher than at shorter wavelengths of 355 nm and 532 nm retrieved by Polly XT. Overall, our results indicate that Halo Doppler lidars can add another wavelength at 1.5 µm to studies on the spectral dependency of aerosol depolarization ratio, at least in the lowest 2-3 km above ground. [1] Mamouri, R.-E. and Ansmann, A.: Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles, Atmos. Meas. Tech., 10, 3403-3427, https://doi.org/10.5194/amt-10-3403-2017, 2017. [2] Burton, S. P., Hair, J. W., Kahnert, M., Ferrare, R. A., Hostetler, C. A., Cook, A. L., Harper, D. B., Berkoff, T. A., Seaman, S. T., Collins, J. E., Fenn, M. A. and Rogers, R. R.: Observations of the spectral dependence of linear particle depolarization ratio of aerosols using NASA Langley airborne High Spectral Resolution Lidar, Atmos. Chem. Phys., 15, 13453–13473, doi:10.5194/acp-15-13453-2015, 2015. [3] Haarig, M., Ansmann, A., Baars, H., Jimenez, C., Veselovskii, I., Engelmann, R. and Althausen, D.: Depolarization and lidar ratios at 355, 532, and 1064 nm and microphysical properties of aged tropospheric and stratospheric Canadian wildfire smoke, Atmos. Chem. Phys., 18, 11847–11861, doi:10.5194/acp-18-11847-2018, 2018. [4] Vakkari, V., Manninen, A. J., O’Connor, E. J., Schween, J. H., van Zyl, P. G. and Marinou, E.: A novel post-processing algorithm for Halo Doppler lidars, Atmos. Meas. Tech., 12(2), 839–852, doi:10.5194/amt-12-839-2019, 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".