Satellite‐based estimation of cloud‐base heights using constrained spectral radiance matching
Bibliographic record
Abstract
A method for estimating cloud‐base heights (CBHs) across wide swaths of passive satellite imagery is introduced. The constrained spectral radiance matching (CSRM) algorithm assigns donor columns observed by CloudSat/CALIPSO to recipient pixels across MODIS imagery. The column meeting the constraint is selected as a donor via spectral radiance matching (SRM). Results are compared using eight cloud characteristics, retrieved from passive imagery, as constraints and distinct values for a matching controlling factor α . Cloud‐top pressure and α = 0.3 are used in the final algorithm. Estimates are made of CBH and layer‐cloud fraction profile made by SRM, CSRM, cloud‐type matching (CTM), and retrieved data matching (RDM) using a data‐exclusion procedure. Results show that the CSRM is superior at estimating lowest CBH and layer‐cloud fraction. It is also shown that, when cloud types are provided by merged CloudSat and CALIPSO data, the CTM provides the best estimates of uppermost CBH, with the CSRM taking second. Both the CSRM method and the straight SRM method construct layer‐cloud fraction profiles very well for clouds between 2 and ∼15 km. A preliminary 3D rendering of tropical storm Soulik is presented.
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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".