Radiosonde‐Derived Temperature Inversions and Their Association With Fog Over 37 Melt Seasons in East Greenland
Bibliographic record
Abstract
Abstract We present temperature inversion characteristics during fog and nonfog conditions at three east Greenland coastal weather stations during Arctic melt seasons 1980–2016. For this purpose, we developed a novel automated method to extract fog‐top height (FTH) from Integrated Global Radiosonde Archive data, which is applicable to any fog thermodynamic profile and includes an improved interpolation of saturation between sounding levels. From the analysis of >22,000 melt‐season soundings we conclude that inversions occur 85–95% of the time, are predominantly elevated, and have median depths >200 m. Fog at high‐Arctic locations often penetrates the inversion layer, especially in the late melt season, and is commonly several hundred meters thick. At low‐Arctic locations fog is thinner and generally restricted to the mixed layer. Inversions during fog are deeper and stronger compared to nonfog conditions. This effect is more pronounced at higher latitudes, which we attribute to distinct local boundary layer conditions and large‐scale processes. The Integrated Global Radiosonde Archive‐extracted FTHs have a cumulative error of 56 m and are in reasonable agreement with retrievals from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite cloud top data. The novel FTH extraction method can be applied to any polar sounding with >5 significant levels below 700 hPa and can be extended to boundary layer clouds other than fog, which represent the majority of cloud occurrence in the Arctic melt season. This study advances the understanding of interactions between low clouds and temperature inversions and improves retrieval of cloud geometrical thickness from radiosondes: both have important implications for the Arctic surface energy budget.
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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 | 0.001 | 0.001 |
| 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".