Applying the Optimal Estimation Method for Retrieving Rayleigh-Scatter Lidar Temperatures in the Mesosphere
Bibliographic record
Abstract
The Rayleigh-scatter lidar (RSL) system at the Atmospheric Lidar Observatory at Utah State University (ALO-USU) provided a rich database of absolute temperatures throughout the mesosphere from 45 km to above 90 km between 1993 and 2004. Recently, a new method for retrieving absolute temperatures from RSL observations has been developed by a group at the University of Western Ontario (UWO), Canada. The Optimal Estimation Method (OEM) uses machine learning to minimize a cost function by optimizing the temperature parameter in a forward model, in our case the lidar equation, to RSL data. This optimization provides some benefits over the existing method through a robust uncertainty budget and a quantitative determination of the cut-off altitude, or the topmost altitude in the temperature profile. Using this method also provides a slight increase in the top observable altitude and does not have a large dependence on the initial temperature. The OEM procedure was converted from MATLAB, which is used by the UWO group, into Python, which is used at ALO-USU. The temperatures were then reduced using the OEM from observations made between 1993 and 2004. Initial results obtained using the Python version of OEM were compared with those using MATLAB showing good agreement. More observations from ALO-USU were then reduced using OEM and compared with the original reduction method. The results show good agreement between the two methods until higher altitudes. These differences can be attributed to dependence on initial conditions in the original method or over-constraining from overestimating the altitude range to be used in the OEM retrieval. At higher altitudes, however, the temperatures tend to agree within the given uncertainties. Further work with this method is being done to generate a temperature climatology using ALO-USU observations and developing a method to retrieve absolute neutral densities using a modification of the forward model in the OEM.
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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 | 0.001 | 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 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".