Comparison of OMI UV observations with ground-based measurements at high northern latitudes
Bibliographic record
Abstract
Abstract. The Dutch–Finnish Ozone Monitoring Instrument (OMI) on board NASA's Aura spacecraft provides estimates of erythemal (sunburning) ultraviolet (UV) dose rates and erythemal daily doses. These data were compared with ground-based measurements at 13 stations located throughout the Arctic and Scandinavia from 60 to 83° N. The study corroborates results from earlier work, but is based on a longer time series (8 versus 2 years) and considers additional data products, such as the erythemal dose rate at the time of the satellite overpass. Furthermore, systematic errors in satellite UV data resulting from inaccuracies in the surface albedo climatology used in the OMI UV algorithm are systematically assessed. At times when the surface albedo is correctly known, OMI data typically exceed ground-based measurements by 0–11 %. When the OMI albedo climatology exceeds the actual albedo, OMI data may be biased high by as much as 55 %. In turn, when the OMI albedo climatology is too low, OMI data can be biased low by up to 59 %. Such large negative biases may occur when reflections from snow and ice, which increase downwelling UV irradiance, are misinterpreted as reflections from clouds, which decrease the UV flux at the surface. Results suggest that a better OMI albedo climatology would greatly improve the accuracy of OMI UV data products even if year-to-year differences of the actual albedo cannot be accounted for. A pathway for improving the OMI albedo climatology is discussed. Results also demonstrate that ground-based measurements from the center of Greenland, where high, homogenous surface albedo is observed year round, are ideally suited to detect systematic problems or temporal drifts in estimates of surface UV irradiance from space.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".