5 years of research and operations with a Raman lidar for meteorological and climatological applications
Bibliographic record
Abstract
Raman lidars are widely used in research to measure the atmospheric profile of temperature and humidity. In operational meteorology, however, the technology is still emerging mostly because of its high costs, the high complexity and the difficulty of calibrating the measurements. The Raman Lidar for Meteorological Observations (RALMO) located at the Federal Office of Meteorology and Climatology MeteoSwiss in Payerne, Switzerland, measures humidity and temperature profiles continuously since 2008 demonstrating the technique’s potential for operational use. We have developed and implemented a calibration method based on the lidar’s solar background measurements allowing for daily calibrations and independently from external references like radiosondes. We assessed the impact of RALMO observations in the MeteoSwiss operational, convective-scale ensemble data assimilation and forecasting system in two two-week summer and winter experiments revealing the potential to improve the analysis, especially in regions without other profile observations. We further compiled a climatology of tropospheric temperature, water vapor mixing ratio and relative humidity from the 15-year data set and will present first results of relative humidity trends in the troposphere above Payerne, Switzerland.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".