Observing the Diurnal Variation of Atmospheric Ozone From the Geostationary Interferometric Infrared Sounder (GIIRS) Over East Asia
Bibliographic record
Abstract
Abstract Ozone is an important atmospheric constituent, exerting a pivotal influence on atmospheric chemistry, air quality, and climate change. The monitoring of its distribution and variation is crucial for advancing our understanding of ozone development and related processes. This study presents the first spatial and temporal distributions of total ozone columns (TOC) retrieved from the Geostationary Interferometric Infrared Sounder (GIIRS), on board China's FengYun‐4B satellite (FY‐4B/GIIRS) launched in 2021. Particularly, we focus on the variations of TOCs in East Asia from diurnal to seasonal time scales. Retrievals are implemented using spectra from March, June, September, and December, representing different seasons. The results show that the degree of freedom for the signal (DOFS) typically exhibited a range of 0.8–1.4, with the vertical detection sensitivity of GIIRS peaking in the upper troposphere/lower stratosphere (UTLS) region, where the ozone variability is the highest. Collocation comparisons with the Infrared Atmospheric Sounding Interferometer (IASI) retrievals, the Ozone Monitoring Instrument (OMI) measurements, the European Center for Medium‐Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) simulations, and in situ ozone observations show good agreement. The comparisons of TOCs between GIIRS, Pandora and ERA5 at different latitudes and different time scales demonstrate the ability of FY‐4B/GIIRS in capturing the temporal and latitudinal ozone variations, particularly at middle and high latitudes. Our work demonstrates that FY‐4B/GIIRS has good capability to track ozone variations from diurnal to seasonal in East Asia, which will contribute to the understanding of regional and global ozone variations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".