Evaluation of Arctic Water Vapor Profile Observations from a Differential Absorption Lidar
Bibliographic record
Abstract
The continuous measuring of the vertical profile of water vapor in the boundary layer using a commercially available differential absorption lidar (DIAL) has only recently been made possible. Since September 2018, a new pre-production version of the Vaisala DIAL system has operated at the Iqaluit supersite (63.74°N, 68.51°W), commissioned by Environment and Climate Change Canada (ECCC) as part of the Canadian Arctic Weather Science project. This study presents its evaluation during the extremely dry conditions experienced in the Arctic by comparing it with coincident radiosonde and Raman lidar observations. Comparisons over a one year period were strongly correlated (r > 0.8 at almost all heights) and exhibited an average bias of +0.13 ± 0.01 g/kg (DIAL-sonde) and +0.18 ± 0.02 g/kg (DIAL-Raman). Larger differences exhibiting distinct artifacts were found between 250 and 400 m above ground level (AGL). The DIAL’s observations were also used to conduct a verification case study of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction. Comparisons to ECCC’s global environmental multiscale model (GEM-2.5 km and GEM-10 km) indicate good agreement with an average bias < 0.16 g/kg for the higher-resolution (GEM-2.5 km) models. All models performed significantly better during the winter than the summer, likely due to the winter’s lower water vapor concentrations and decreased variability. This study provides evidence in favor of using high temporal resolution lidar water vapor profile measurements to complement radiosonde observations and for NWP model verification and process studies.
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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 | 0.001 | 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".