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Record W3182630143 · doi:10.1002/essoar.10506988.1

CAPE-based derivation of approximate tropical cyclone potential intensity formula

2021· preprint· en· W3182630143 on OpenAlexaff
Timothy M. Merlis, Raphaël Rousseau‐Rizzi, Nadir Jeevanjee

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPreprintTropical cycloneSpace ScienceSpace (punctuation)Computer scienceMeteorologyPhysicsWorld Wide WebAstronomy

Abstract

fetched live from OpenAlex

Tropical cyclone (TC) potential intensity (PI) theory has been extensively used for future climate change assessments of TC activity. PI theory has a well known approximate form, consistent with a Carnot cycle interpretation of TC energetics, which relates PI to mean environmental conditions: the difference between surface and TC outflow temperatures and the air-sea enthalpy disequilibrium. The changes in these conditions (the increase in air-sea disequilibrium, in particular) provide a physical reason for the robust increase in tropical-mean PI simulated in future climate projections. Quantitative assessments of future changes, in contrast, make use of a numerical algorithm based on the relationship between PI and convective available potential energy (CAPE). Here, a recently developed analytic theory for CAPE is used to present an alternative derivation of an approximate form of PI. The derivation offers insight into the limited sensitivity of PI to the atmospheric stratification in the free troposphere. The resulting CAPE-based approximate formula nearly recovers the previous approximate PI formula, and the new formula helps account for the weaker-than-expected sensitivity of PI to surface relative humidity changes. The new analytic CAPE-based PI builds confidence in previous numerical CAPE-based PI calculations that use climate model projections of the future tropical environment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.239
Teacher spread0.217 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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