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Record W3082477035 · doi:10.1071/es19025

Atmospheric rivers in the Australia-Asian region: a BoM–CMA collaborative study

2020· article· en· W3082477035 on OpenAlexaff
Chengzhi Ye, Huqiang Zhang, Aurel Moise, Ruping Mo

Bibliographic record

VenueJournal of Southern Hemisphere Earth System Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsOrographic liftMonsoonEnvironmental sciencePrecipitationClimatologyTroposphereMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

The name ‘atmospheric river’ (AR) could easily be misinterpreted to mean rivers flowing in the sky. But, ARs actually refer to narrow bands of strong horizontal water vapour transport that are concentrated in the lower troposphere. These bands are called ‘atmospheric rivers’ because the water vapour flux they carry is close to the volume of water carried by big river systems on the ground. ARs can cause heavy rainfall events if some physical mechanisms, such as orographic enhancement, exist to set up the moisture convergence and vertical motions necessary to produce condensation. In recent decades, these significant moisture plumes have attracted increasing attention from scientific communities, especially in North America and western Europe, to further understand the connections between ARs and extreme precipitation events which can trigger severe natural disasters such as floods, mudslides and avalanches. Yet very limited research has been conducted in the Australia-Asian (A-A) region, where the important role of atmospheric moisture transport has long been recognised for its rainfall generation and variations. In this paper, we introduce a collaborative project between the Australian Bureau of Meteorology and China Meteorological Administration, which was set up to explore the detailed AR characteristics of atmospheric moisture transport embedded in the A-A monsoon system. The project in China focused on using AR analysis to explore connections between moisture transport and extreme rainfall mainly during the boreal summer monsoon season. In Australia, AR analysis was used to understand the connections between the river-like Northwest Cloud Band and rainfall in the region. Results from this project demonstrate the potential benefits of applying AR analysis to better understand the role of tropical moisture transport in rainfall generation in the extratropics, thus achieve better rainfall forecast skills at NWP (Numerical Weather Prediction), sub-seasonal and seasonal time scales. We also discuss future directions of this collaborative research, including further assessing potential changes in ARs under global warming.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.520

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.250
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2020
Admission routes1
Has abstractyes

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