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Record W2041199569 · doi:10.1175/mwr-d-14-00280.1

Using Microwave Satellite Data to Assess Changes in Storminess over the Pacific Ocean

2015· article· en· W2041199569 on OpenAlexaboutno aff
Michael C. Kruk, Kyle Hilburn, John J. Marra

Bibliographic record

VenueMonthly Weather Review · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
Fundersnot available
KeywordsPacific decadal oscillationClimatologyEl Niño Southern OscillationEnvironmental scienceStormLatitudePacific oceanOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.275
GPT teacher head0.353
Teacher spread0.078 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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