Using Microwave Satellite Data to Assess Changes in Storminess over the Pacific Ocean
Bibliographic record
Abstract
Abstract This study analyzes 25 years of Special Sensor Microwave Imager (SSM/I) retrievals of rain rate and wind speed to assess changes in storminess over the open water of the Pacific Ocean. Changes in storminess are characterized by combining trends in both the statistically derived 95th percentile exceedance frequencies of rain rate and wind speed (i.e., extremes). Storminess is computed annually and seasonally, with further partitioning done by phase of the El Niño–Southern Oscillation (ENSO) index and the Pacific decadal oscillation (PDO) index. Overall, rain-rate exceedance frequencies of 6–8 mm h−1 cover most of the western and central tropical Pacific, with higher values present around the Philippines, Japan, Mexico, and the northwest coast of Australia. Wind speed exceedance frequencies are a strong function of latitude, with values less (greater) than 12 m s−1 equatorward (poleward) of 30°N/S. Statistically significant increasing trends in rain rate were found in the western tropical Pacific near the Caroline Islands and the Solomon Islands, and in the extratropics from the Aleutian Islands down the coast along British Columbia and Washington State. Statistically significant increasing trends in wind speed are present in the equatorial central Pacific near Kiribati and the Republic of the Marshall Islands (RMI), and in the extratropics along the west coast of the United States and Canada. Thus, while extreme rain and winds are both increasing across large areas of the Pacific, these areas are modulated according to the phase of ENSO and the PDO, and their intersection takes aim at specific locations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".