Integrated Raman Lidar and Microwave Radiometer Retrieval of Atmospheric Water Vapor
Bibliographic record
Abstract
Water vapor plays a critically important role in many atmospheric processes. However, it is poorly characterized throughout much of the atmosphere, particularly in the UTLS (Upper Troposphere Lower Stratosphere) region, due to lack of accurate measurements. Raman lidar boasts the capacity for excellent spatial and temporal resolution, but requires an external calibration. Microwave radiometers can be calibrated in absolute terms, but have poor height resolution. In this study, we introduce an integrated water vapor retrieval using an optimal estimation method, where the measurements from the Raman Lidar for Meteorological Observation (RALMO) and a RPG-HATPRO radiometer, both located at the MeteoSwiss station in Payerne, Switzerland. We consider two radiometer forward models for characterizing the radiometer: ARTS2 (Eriksson et al. 2011) and a “lightweight” radiative model (Schroeder & Westwater 1991), comparing and analyzing their performance. The radiometer forward model is combined with a lidar forward model (Sica & Haefele 2016) to yield a forward model capable of retrieval of a calibrated lidar water vapor profile.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".