Relative merits of the 1.6 and 3.75 μm channels of the AVHRR/3 for cloud detection
Bibliographic record
Abstract
A study was performed to investigate the potential impacts of the cloud-masking capability of the advanced very high resolution radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites because of the addition of the 1.6 µm channel (ch3a) and the removal of the 3.75 µm channel (ch3b) during daylight operation. Both channels are measured by the AVHRR, but only one is available in the data stream. Because the AVHRR presents the longest time series of global imager data, this change could impact a critical source of climate data. Specifically, changes in its cloud-masking capability may introduce a discontinuity in its derived clear-sky data records. To study the relative cloud-detection capabilities of the AVHRR with ch3a or ch3b, data from the moderate resolution imaging spectroradiometer (MODIS) on the National Aeronautics and Space Administration (NASA) TERRA mission was used because it offers ch3a, ch3b, and the other AVHRR channels simultaneously. The MODIS data analysis indicated that ch3b offered more capability in separating cloud from snow. Cloud-masking results with ch3a and ch3b were comparable with respect to the separation of cloud from aerosol and the detection of cloud over a desert scene. Although these results are preliminary and based on a limited analysis, they do indicate that the switch from ch3b to ch3a may have significant impacts on the cloud-detection capability of the AVHRR.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".