Comment on amt-2023-215
Bibliographic record
Abstract
Abstract. Temperature and water vapor profiles are essential to climate change studies and weather forecasting. Hyperspectral instruments are of great value for retrieving temperature and water vapor profiles, enabling accurate monitoring of their changes. Successful retrievals of temperature and water vapor profiles require accuracy of hyperspectral radiometer measurements. In this study, the radiometric accuracy of an airborne hyperspectral microwave radiometer, the High Spectral Resolution Airborne Microwave Sounder (HiSRAMS), and a ground-based hyperspectral infrared radiometer, the Atmospheric Emitted Radiance Interferometer (AERI), is simultaneously assessed by performing radiative closure tests under clear-sky conditions in Ottawa, Canada. As an airborne instrument, HiSRAMS has two radiometers measuring radiance in the oxygen band (49.6–58.3 GHz) and water vapor band (175.9–184.6 GHz) for zenith-pointing and nadir-pointing observations. AERI provides ground-based, zenith-pointing radiance measurements between 520 and 1800 cm−1. A systematic warm radiance bias is present in AERI observations in the window band. Upon removal of this bias, improved radiative closure was attained in the window band. The brightness temperature (BT) bias in nadir-pointing HiSRAMS observations is smaller than at the zenith. A novel but straightforward method is developed to diagnose the radiometric accuracy of the two instruments in comparison based on the relationship between radiometric bias and optical depth. Compared to AERI, HiSRAMS demonstrates similar radiometric accuracy for nadir-pointing measurements but exhibits relatively poor accuracy for zenith-pointing measurements, which requires further characterization. Future work on temperature and water vapor concentration retrievals using HiSRAMS and AERI is warranted.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.013 |
| Insufficient payload (model declined to judge) | 0.256 | 0.243 |
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".