Dry events in the winter in Israel and its linkage to synoptic and large‐scale circulations
Bibliographic record
Abstract
Dry spells during the rainy season have a far‐reaching environmental impact in the Levant. A previous study by Saaroni et al. investigated prolonging dry spells (PDSs) and classified them subjectively into three types: “subtropical,” “baroclinic” and “polar.” They developed quantitative indices to identify each type. The present study analyses dry events, both individual days and spells, based on the above classification. We found that the indices identifying PDS types are effective for distinguishing among individual dry days of the three types, but not as precursors of dry days. However, when the indices keep positive for 2 or 3 days consecutively, they become effective precursors of dry spells. An alternative, automatic classification, using the K‐means technique, yielded classes similar to those defined subjectively. The analysis revealed the dominance of the “baroclinic” type and the tendency of “subtropical” type to become “baroclinic.” Composite maps of cyclone tracks which were derived for days belonging to each type showed track distributions characteristic to particular types. For every type, the evolution of dry spells was studied through composite maps of 500‐hPa geopotential height (GPH) anomaly. The maps were derived for 1–3 days preceding the event. Pronounced patterns were identified as precursors of specific dry spell types; positive anomaly over Canada and Greenland, combined with negative anomaly over northwest Europe turned out as precursors of “baroclinic” and “subtropical” events, whereas the North Atlantic Oscillation (NAO), in its positive phase, was found as an exclusive precursor of the “polar” type. All three types of dry days are characterized by an offshore lower‐level wind, and by an absence of Cyprus Lows, due to deflection of cyclone tracks away from the Middle East. This implies that dry conditions in the Levant are caused by both thermodynamic (mesoscale) and dynamic (synoptic scale) factors.
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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 | 0.001 | 0.001 |
| 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".