DIAL and GNSS observations of the diurnal water‐vapour cycle above Iqaluit, Nunavut
Bibliographic record
Abstract
Abstract Atmospheric water vapour is the dominant gas in the greenhouse effect and its diurnal cycle is an essential component of the hydrological cycle. High‐quality water‐vapour measurements are critical observations for numerical weather prediction (NWP) models, which encounter difficulties in the Arctic due to its unique weather. Accurate measurements of the diurnal water‐vapour cycle can improve precipitation rate and type predictions. In this study, we use a preproduction Vaisala broad‐band differential absorption lidar (DIAL), installed in Iqaluit, Nunavut (63.75°N, 68.55°W), to calculate seasonal height‐resolved diurnal water‐vapour cycles from 100–1,500 m altitude. We also calculate the surface water‐vapour mixing ratio and integrated water‐vapour (IWV) diurnal cycles using co‐located surface station and global navigation satellite system (GNSS) measurements. We find that the first 250 m of the DIAL water‐vapour mixing ratio height‐resolved diurnal cycle agrees within 0.02 gkg with the surface‐station amplitudes, and within 0–2 hr in phase. DIAL diurnal‐cycle values are close to 1 at the surface, and between 0.25 and 0.95 depending on the altitude and season. The phases of the diurnal cycle shift and the amplitudes increase with altitude. In the summer, all instruments observe a strong 24‐hr cycle. The amplitude of the 24‐hr component decreases with the solar cycle, such that the 12‐hr component begins to influence the total cycle significantly by the winter. The IWV has a large 12‐hr component throughout the year, which could be due to the superposition of two diurnal components at different altitudes or increasing contributions at altitudes higher than those measured by the DIAL. We have shown that using DIAL measurements to observe height‐resolved diurnal cycles provides a deeper understanding of the diurnal cycle than that achieved from GNSS and surface measurements alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".