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Record W2969497373 · doi:10.1029/2019ea000638

A Relationship Between Ural‐Siberian Blocking and Himalayan Weather Anomalies

2019· article· en· W2969497373 on OpenAlexaff
Ujjwal Tiwari, Andrew B. G. Bush

Bibliographic record

VenueEarth and Space Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeologyTrough (economics)ClimatologySynoptic scale meteorologyMesoscale meteorologyAnomaly (physics)Atmospheric circulationPeninsulaJet streamPrecipitationSea levelLow-pressure areaConvergence zoneAtmospheric pressureOceanographyMeteorologyGeographyJet (fluid)

Abstract

fetched live from OpenAlex

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.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.231
Teacher spread0.212 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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