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Record W4397022945 · doi:10.1175/bams-d-23-0014.1

Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE)

2024· article· en· W4397022945 on OpenAlexaffabout
Pavlos Kollias, Greg M. McFarquhar, Eric C. Bruning, Paul J. DeMott, Matthew R. Kumjian, Paul Lawson, Zachary J. Lebo, Timothy Logan, Kelly Lombardo, Mariko Oue, Greg Roberts, Raymond A. Shaw, Susan C. van den Heever, Mengistu Wolde, Kevin R. Barry, David M. Bodine, Roelof Bruintjes, V. Chandrasekar, Andrew M. Dzambo, Thomas C. J. Hill, Michael Jensen, Francesc Junyent, Sonia M. Kreidenweis, Katia Lamer, Edward Luke, Aaron Bansemer, Christina S. McCluskey, Leonid Nichman, Cuong Viet Nguyen, Ryan Patnaude, Russell Perkins, Heath Powers, Keyvan Ranjbar, Eric Roux, Jeffrey C. Snyder, Bernat P. Treserras, Nathan A. Wales, Nithin Allwayin, Ben Ascher, Jason Barr, Yishi Hu, Yongjie Huang, Miles Litzmann, Zackary Mages, Katherine E. McKeown, Saurabh Patil, Elise Rosky, Kristofer S. Tuftedal, Min‐Duan Tzeng, Zeen Zhu

Bibliographic record

VenueBulletin of the American Meteorological Society · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsMcGill UniversityNational Research Council Canada
FundersU.S. Department of EnergyBiological and Environmental ResearchOffice of ScienceNational Science Foundation
KeywordsSea breezePrecipitationEnvironmental scienceMeteorologyConvectionAtmospheric sciencesClimatologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract Convective clouds play an important role in Earth’s climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. To improve predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled “Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE).” ESCAPE took place between 30 May and 30 September 2022 in the vicinity of Houston, Texas, because this area frequently experiences isolated deep convection that interacts with the region’s mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling and developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars and one mobile Doppler lidar truck equipped with soundings were deployed from 30 May to 28 June. From 1 August to 30 September 2022, a dual-polarization C-band radar was deployed and operated using a novel, multisensor agile adaptive sampling strategy to track the entire life cycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle. Significance Statement The ESCAPE field experiment provided unique observations of coastal convective cloud vertical motions, microphysics, and precipitation across a wide range of summertime aerosol and meteorological regimes. The highest aerosol concentrations occurred near the refineries in eastern Houston but did not contribute to the cloud condensation nuclei and ice-nucleating particles. The airborne measurements included frequent sampling of intense convective updraft dynamics and microphysics. A novel radar-based sampling of convective cells provided unique observations of their 3D structure throughout their life cycle. Mobile trucks equipped with soundings provided a detailed sampling of the sea-breeze structure and evolution. These datasets will be used for improving high-resolution simulations of high-impact events in coastal urbanized areas.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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.

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

Citations16
Published2024
Admission routes2
Has abstractyes

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