Validation of middle atmospheric campaign-based water vapour measured by the ground-based microwave radiometer MIAWARA-C
Bibliographic record
Abstract
Abstract. Middle atmospheric water vapour can be used as a tracer for dynamical processes. It is mainly measured by satellite instruments and ground-based microwave radiometers. Ground-based instruments capable of measuring middle atmospheric water vapour are sparse but valuable as they complement satellite measurements, are relatively easy to maintain and have a long lifetime. MIAWARA-C is a ground-based microwave radiometer for middle atmospheric water vapour designed for use on measurement campaigns for both atmospheric case studies and instrument intercomparisons. MIAWARA-C's retrieval version 1.1 (v1.1) is set up in a way to provide a consistent data set even if the instrument is operated from different locations on a campaign basis. The sensitive altitude range for v1.1 extends from 4 hPa (37 km) to 0.017 hPa (75 km). MIAWARA-C measures two polarisations of the incident radiation in separate receiver channels and can therefore provide two independent measurements of the same air mass. The standard deviation of the difference between the profiles obtained from the two polarisations is in excellent agreement with the estimated random error of v1.1. In this paper, the quality of v1.1 data is assessed during two measurement campaigns: (1) five months of measurements in the Arctic (Sodankylä, 67.37° N/26.63° E) and (2) nine months of measurements at mid-latitudes (Zimmerwald, 46.88° N/7.46° E). For both campaigns MIAWARA-C's profiles are compared to measurements from the satellite experiments Aura MLS and MIPAS. In addition, comparisons to ACE-FTS and SOFIE are presented for the Arctic and to the ground-based radiometer MIAWARA for the mid-latitudinal campaign. In general all intercomparisons show high correlation coefficients, above 0.5 at altitudes above 45 km, confirming the ability of MIAWARA-C to monitor temporal variations on the order of days. The biases are generally below 10% and within the estimated systematic uncertainty of MIAWARA-C. No consistent wet or dry bias is identified for MIAWARA-C. In addition, comparisons to the reference instruments indicate the estimated random error of v1.1 to be a realistic measure of the random variation on the retrieved profile.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".