Retrieval of Cloud, Atmospheric, and Surface Properties from Far-Infrared Spectral Radiances Measured by FIRMOS-B During the 2022 HEMERA Stratospheric Balloon Campaign
Bibliographic record
Abstract
The knowledge of the radiative properties of clouds and the atmospheric state is of fundamental importance in modelling phenomena in numerical weather predictions and climate models. In this study, we show the results of the retrieval of cloud properties, along with the atmospheric state and the surface temperature, from far-infrared spectral radiances, in the 100–1000 cm−1 range, measured by the Far-Infrared Radiation Mobile Observation System-Balloon version (FIRMOS-B) spectroradiometer from a stratospheric balloon launched from Timmins (Canada) in August 2022 within the HEMERA 3 programme. The retrieval study is performed with the Optimal Estimation inversion approach, using three different forward models and retrieval codes to compare the results. Cloud optical depth, particle effective size, and cloud top height are retrieved with good accuracy, despite the relatively high measurement noise of the FIRMOS-B observations used for this study. The retrieved atmospheric profiles, computed simultaneously with cloud parameters, are in good agreement with both co-located radiosonde measurements and ERA-5 profiles, under all-sky conditions. The findings are very promising for the development of an optimised retrieval procedure to analyse the high-precision FIR spectral measurements, which will be delivered by the ESA FORUM mission.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".