Exploring the Dynamics of Sea Breeze Fronts in the Southwestern Caspian Sea: Analysis Using Observational Data, WRF Simulations, and Machine Learning Approaches
Bibliographic record
Abstract
This research investigates the dynamics of the Sea Breeze Front (SBF) in the southwestern Caspian Sea, specifically focusing on Bandar Anzali, Iran. Utilizing two years of observational data alongside Weather Research and Forecasting (WRF) model simulations, the study examines the meteorological characteristics and behaviors associated with SBF events. SBF days were identified by analyzing land-sea temperature contrasts, supported by wind shifts, temperature decreases, increases in humidity, and cloud formation.In-depth analysis reveals consistent atmospheric patterns during SBF events, such as temperature variations and notable wind shifts. The intensity of the land-sea thermal contrast is influenced by both local topography and atmospheric stability. A detailed case study of March 4, 2022, highlighted key meteorological changes, including temperature drops and wind direction shifts. While the WRF model accurately captured temperature and pressure variations, it slightly underestimated humidity and dew point.Machine learning techniques, particularly K-means clustering, were employed to classify distinct atmospheric regimes linked to SBF occurrences. The clustering analysis identified two primary atmospheric patterns: cold, humid air masses favorable to SBF development, emphasizing the significant role of land-sea temperature gradients and local wind dynamics.This study highlights the value of combining observational data, numerical simulations, and machine learning techniques to better understand coastal mesoscale processes. The findings provide fresh insights into SBF behavior in the Caspian region, with implications for enhancing coastal weather forecasting and management. Future work should focus on improving the accuracy of WRF model simulations and further examining the impact of regional topography on SBF dynamics. Keywords: Sea Breeze Front, Machine Learning, WRF, K-means Clustering, Temperature Gradient, Caspian Sea,.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".