Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska
Bibliographic record
Abstract
Atmospheric water vapor is a key parameter for the analysis of climatic systems (greenhouse gas effect), in particular over high latitudes where water vapor displays an important seasonal variability. The sparse spatial and temporal sampling of atmospheric water vapor observations across Canada needs to be improved. A series of instruments and methods including a 940‐nm solar absorption band radiometer (R) and radiosonde (S) analysis from a numerical weather prediction model and a ground‐based bi‐frequency Global Positioning System (GPS) were used to evaluate the integrated atmospheric water vapor (IWV) at various sites in Canada and Alaska from a multiyear database. The IWV‐R measurements were collected within the framework of the North American Sun Radiometry network (AERONET/AEROCAN). Intercomparisons between [IWV‐GPS and IWV‐S], [IWV‐R and IWV‐GPS], and [IWV‐R and IWV‐S] show root mean square (RMS) differences of 1.8, 1.9, and 2.2 kg m−2, respectively. GPS meteorology appears to be the easiest approach to calibrate the solar radiometer water vapor band owing to its flexibility, and it allows us to overcome the Sun radiometry limitation in high‐latitude areas like the Arctic. The sensitivity of the GPS retrieval to various parameters like GPS satellite constellation and meteorological data are discussed. The classical linear relationship between the surface temperature and the integrated weighted mean temperature profile needed for IWV‐GPS retrieval may be significantly different for Arctic air masses compared with midlatitude air masses in the case of tropospheric temperature profile inversion. An ever‐expanding multiyear (1994–2001) North American summer water vapor climatology, derived from AERONET/Canadian Sun Radiometer Network, is presented and analyzed, showing a mean value of 19.8 ± 6.1 kg m−2and variations from 17 kg m−2in Alaska to 23 kg m−2in southeastern Canada. The results in Bonanza Creek, Alaska, show significant interannual variations with a peak in 1997, which may be linked to an El Niño event that occurred in the same year. Such a database may also be useful for climate model validation as shown for the Canadian Global Environmental Model (RMS difference of 3.4 kg m−2). In the end we show that, even if data are selected only for cloud‐free atmospheres, there are no significant differences as compared with radiosonde climatology at Canadian Northwestern sites (≤3% relatively to Bonanza Creek summer mean value).
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".