Backward trajectory analysis of southern California atmospheric rivers
Bibliographic record
Abstract
Atmospheric rivers (ARs) are filamentary channels of high water vapor flux that transfer moisture horizontally through the atmosphere at low levels. ARs are often responsible for large annual rainfall totals as well as high-intensity storms due to orographic forcing. ARs are important features when predicting hazardous events such as flooding and are vital components of many regional water budgets. This is especially true for drought-prone areas such as southern California (SCA), which experiences relatively few storms per season, many from AR events. Here, we use a Lagrangian model to create backward air parcel trajectories of 159 AR events that made landfall on the US west coast from December 2004 to December 2015. Trajectories are used to examine the lifecycles and movements of these ARs and to differentiate ARs that made landfall in different regions. Prior to landfall, SCA ARs share similarities to but also have distinct differences from other ARs. At 1000 m above mean sea level (MSL), SCA AR trajectories travel shorter distances over the same 72 h time frame than trajectories for ARs that made landfall farther north. Additionally, along-trajectory measurements for SCA ARs tend to be warmer and have higher specific humidity values. This applies to both the 1000 and 2000 m MSL levels. These results imply that SCA ARs move slower and have the potential to produce higher intensity storms at landfall. An analysis of a case study event of an extreme AR that made landfall in SCA on 17 February 2017 confirms these results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".