Identification of the cloud base height over the centralHimalayan region: Intercomparison of Ceilometer and DopplerLidar
Bibliographic record
Abstract
Abstract. We present the measurement of cloud base height (CBH) derived from the Doppler Lidar (DL), Ceilometer (CM) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite over a high altitude station in the central Himalayan region for the first time. We analyzed six cases of cloud overpass during the daytime convection period by using the cloud images captured by total sky imager. The occurrence of thick clouds (> 50 %) over the site is more frequent than thin clouds (< 40 %). In every case, the CBH indicates less than 1.2 km, above ground level (AGL) observed by both DL and CM instruments. The presence of low level clouds in the height-time variation of signal to noise ratio of DL and backscatter of CM shows a similar diurnal pattern on all days. Cloud fraction is found to be maximum during the convective period. The CBH estimated by the DL and CM showed reasonably good correlation (R2 = 0.76). The DL observed updraft fraction and cloud base vertical velocity also shows good correlation (R2 = 0.66). The inter-comparison between DL and CM will have implications in filling the gap of CBH measurements by the DL, in absence of CM. More deployments of such instruments will be invaluable for the validations of meteorological models over the observationally sparse Indian regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".