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Record W4413245660 · doi:10.5194/ecss2025-312

Ground Observations from the In Situ Collaborative Experiment for the Collection of Hail in the Plains

2025· article· en· W4413245660 on OpenAlexaff
John T. Allen, Ian M. Giammanco, Rebecca Adams‐Selin, Brenna Meisenzahl, Jake Sorber, Julian Brimelow, Aaron Kennedy, Hannah C. Vagasky, Daniel T. Dawson, Sabrina Servey, Kyle Brooks, Kaleb Clover, Talia Kurtz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsWestern University
Fundersnot available
KeywordsStormMeteorologyTransectEnvironmental scienceRadarSampling (signal processing)Winter stormGeologyRemote sensingGeographyEngineeringAerospace engineeringTelecommunications

Abstract

fetched live from OpenAlex

Detailed ground observations of hailstones are historically rare, particularly as it relates to properties that describe hail beyond its maximum single axis diameter, or in sampling the hail swath in substantive detail. To address this gap the In Situ Collaborative Experiment for the Collection of Hail in the Plains (ICECHIP) campaign was conducted between May 15 and June 28th of 2025. Active periods of convection persisted throughout the campaign yielding over 20 intensive observation periods. These included measurement of hail in numerous storms producing 50 mm or greater hail, with both in situ measurement platforms and post-storm transects of the hailswaths sampled close to time of fall in cooled environments and regularly thereafter at horizontal resolutions in the hundreds of meters. Five types of instruments focused on direct hail capture: impact disdrometers with video cameras and hailpads formed the bulk of the sensing array, deployed ahead of the storm. These were complemented by mesonet pods for near-storm environment and SUMHOs, (Super Mobile Hail Observatory) deployable instrumented supersites that funnel hail into cooled storage with colocated hailpad, and measure hailstone fall speed using vertical pointing radar. High resolution and speed video cameras additionally captured hail fall speed and orientation. These were operated in a range of array configurations to best sample storm evolution or the swath at impressive resolution. Post storm sampling accumulated thousands of hailstones, and performed 2-3 axial dimensional measurements along with mass, and for a subset of stones exceeding 2cm, compressive strength testing via crushing.This presentation will focus on three exemplary cases, a supercell in northeast Colorado producing a 14 mile-wide swath with hail diameters reaching 90 mm, a merging supercell that produced giant hailstones measuring as large as 150mm, and a non-supercell case producing extremely soft accumulating hail. Hailstone size and sphericity distributions, compressive strength properties and mass will be compared across the respective swaths to provide preliminary insights into the variability of hail and its potential for damage under different classes of storms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.060
GPT teacher head0.276
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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