Quantitative retrieval of aerosol optical thickness from FY-2 VISSR data
Bibliographic record
Abstract
Atmospheric aerosol, as particulate matter suspended in the air, exists in a variety of forms such as dust, fume and mist. It deeply affects climate and land surface environment in both regional and global scales, and furthermore, lead to be hugely much influence on human health. For the sake of effectively monitoring it, many atmospheric aerosol observation networks are set up and provide associated informational services in the wide world, as well-known Aerosol robotic network (AERONET), Canadian Sunphotometer Network (AeroCan) and so forth. Given large-scale atmospheric aerosol monitoring, that satellite remote sensing data are used to inverse aerosol optical depth is one of available and effective approaches. Nowadays, special types of instruments aboard running satellites are applied to obtain related remote sensing data of retrieving atmospheric aerosol. However, atmospheric aerosol real-timely or near real-timely monitoring hasn't been accomplished. Nevertheless, retrievals, using Fengyun-2 VISSR data, are carried out and the above problem resolved to certain extent, especially over China. In this paper, the authors have developed a new retrieving model/mode to retrieve aerosol optical depth, using Fengyun-2 satellite data that were obtained by the VISSR aboard FY-2C and FY-2D. A series of the aerosol optical depth distribution maps with high time resolution were able to obtained, is helpful for understanding the forming mechanism, transport, influence and controlling approach of atmospheric aerosol.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".