A 1D Var Retrieval of Relative Humidity Using the ERA5 Dataset for the Assimilation of Raman Lidar Measurements
Bibliographic record
Abstract
Abstract We present a one-dimensional variational (1D Var) retrieval of fifth-generation European Centre for Medium-Range Weather Forecast reanalysis (ERA5) temperature and relative humidity profiles above Payerne, Switzerland, assimilating raw backscatter measurements from the MeteoSwiss Raman Lidar for Meteorological Observations (RALMO). Our reanalysis is called ERA5-reRH. We use an optimal estimation method to perform the 1D Var data retrieval. The forward model combines the Raman lidar equation with the Hyland and Wexler expression for water vapor saturation pressure. The error covariance matrix of ERA5 was derived from the differences between ERA5 and a set of 50 special radiosoundings that have not been assimilated into ERA5. We validate ERA5-reRH, ERA5, and RALMO temperature and relative humidity profiles against the same set of special radiosoundings and found the best agreement was with our reanalysis, with a bias of less than 2% relative humidity with respect to water (%RHw) and a spread of less than 8%RHw below 8 km in terms of relative humidity. Improvements for temperature in our reanalysis are only found in the boundary layer, as ERA5 assimilates a large number of upper-air temperature observations. Our retrieval also provides a full uncertainty budget of the reanalyzed temperature and relative humidity including both random and systematic uncertainties.
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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 | 0.001 | 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".