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Record W4211249906 · doi:10.1175/bams-d-21-0130.1

Advances in the Prediction of MJO Teleconnections in the S2S Forecast Systems

2022· article· en· W4211249906 on OpenAlexaff
Cristiana Stan, Cheng Zheng, Edmund K. M. Chang, Daniela I. V. Domeisen, Chaim I. Garfinkel, Andrea M. Jenney, Hyemi Kim, Young‐Kwon Lim, Hai Lin, Andrew W. Robertson, Chen Schwartz, Frédéric Vitart, Jiabao Wang, Priyanka Yadav

Bibliographic record

VenueBulletin of the American Meteorological Society · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric AdministrationIsrael Science FoundationNational Natural Science Foundation of ChinaEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDepartment of Water ResourcesNational Science Foundation
KeywordsTeleconnectionClimatologyMadden–Julian oscillationEnvironmental scienceMeteorologyTroposphereMiddle latitudesEl Niño Southern OscillationGeographyGeology

Abstract

fetched live from OpenAlex

Abstract This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden–Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.

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)0.002
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 score0.660
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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

Citations77
Published2022
Admission routes1
Has abstractyes

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