A Relationship Between Ural‐Siberian Blocking and Himalayan Weather Anomalies
Bibliographic record
Abstract
Because of their distinctly high elevation, the Himalaya are directly influenced by upper‐tropospheric jet stream winds and circulation anomalies that on many occasions have brought unusual weather patterns to the region. In this study, we establish the dynamical relationship between upper‐tropospheric blocks formed over the Ural‐Siberian region and anomalous weather patterns over the Himalaya using a combination of reanalysis data and mesoscale atmospheric model simulations. We identify two distinct blocks (an omega (Ω) block and a dipole (Rex) block) using an appropriate blocking index and computed the anomalous atmospheric fields associated with these types of blocks. In both cases, the low‐pressure component of the block lies westward of the western Himalaya and the upper‐level convergence/divergence of ageostrophic winds along the upstream/downstream portion of the trough creates anomalous positive/negative sea level pressure at the surface. The local sea level pressure is enhanced over the Arabian Peninsula and the Arabian Sea, while downwind over the Himalaya there is a negative pressure anomaly. There is entrainment of high potential vorticity air that descends and flows equatorward. During the Ω blocking event, the trough remains quasi‐stationary over the Western Himalayan Notch and its circulation induces moisture transport from the Arabian Sea and the Bay of Bengal leading to strong precipitation events over the Western and Eastern Himalayan Notches. During the dipole blocking event, the trough gradually shifts southeastward and leads to widespread precipitation over the entire Himalayan arc.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".