Pan-Arctic measurements of wintertime water vapour column using a satellite-borne microwave radiometer
Bibliographic record
Abstract
Abstract. A methodology for retrieving high-latitude winter water vapour columns from passive microwave satellite measurements from Perro et al. (2016) is extended to use measured surface reflectance ratios under more realistic surface reflection assumptions. Pan-Arctic wintertime water vapour is retrieved from Advanced Technology Microwave Sounder (ATMS) measurements made from January 2012 through March 2015 (December to March). The water vapour retrievals are validated using two ground based instruments: the G-band Vapor Radiometer (GVR) at Barrow, Alaska, and the Extended-Range Atmospheric Emitted Radiance Interferometer (E-AERI) at Eureka, Nunavut. E-AERI was chosen as an additional point of validation compared to Perro et al. (2016) due to the different technology and frequencies employed to determine water vapour column compared to the ATMS and GVR. For water vapour columns less than 6 kg m−2, the biases are +2.6 % and +0.01 % relative to the GVR and E-AERI, respectively. A comparison with radiosonde humidity measurements shows they are dry relative to the ATMS measurements in North America and Western Europe, and moist in Asia and Eastern Europe, with an apparent dependence on radiosonde manufacturer. Reanalyses (ERA-5, ERA-Interim, ASR V2, JRA-55 and NCEP) are systematically drier than the ATMS measurements for water vapour columns less than 6 kg m−2, with relative biases ranging from −10 % to −23 %. These differences could have implications for the understanding of the Arctic water budget and climate.
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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) | 0.001 | 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".