Atmospheric rivers in the Australia-Asian region: a BoM–CMA collaborative study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".