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Record W4402805541 · doi:10.1175/bams-d-23-0222.1

Desert–Oasis Convergence Line and Deep Convection Experiment (DECODE)

2024· article· en· W4402805541 on OpenAlexaff
Zhiyong Meng, Xuefeng Meng, Chenggang Wang, Yipeng Huang, Shuhao Zhang, Hongjun Liu, Murong Zhang, Yijing Liu, Hao Huang, Lijuan Su, Quxin Cui, Feng Lu, Kun Zhao, Zhi‐Hua Zhou, Linchun Liu, Xuefeng Ma, Jiutao Shan, Xiao Yao, Daoru Zhu, Zhengwei Yang, Xucheng Zheng, Bo Fan, Lanqiang Bai, Xiaojuan Yao, Yonggang Sun, Lin Manyun, Zimeng Zheng, Zhou Liao, Xuelei Wang, Ke Liu, Luyi Chen, Lebao Yao, Ming Guan, Weikang Kong, Shaoyang Sun, Jiaxin Wang, Yi‐Kai Wu, Yaqi Qin, Xiaoying Jiang, Xiang Pan, Mufei Wang, Changan Zhang, Hanchao Li, Huijun Li, Lixia Shi, Xiaohong Fang, Feng Zhu, Xin Sun, Jingbo Yun, Shiyun Liu, Huiqing Wang, Yawen Yang, Jingyi Wen, Peiyu Wang, Lanbo Liu, Nan Ren, Xiufeng Wu, Z. Zhang, Jianyu Pei, Zhi Yang, Cheng Xia

Bibliographic record

VenueBulletin of the American Meteorological Society · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsDesert (philosophy)Convergence (economics)Line (geometry)ConvectionDeep convectionMeteorologyGeologyClimatologyEnvironmental scienceGeographyMathematicsGeometryPolitical science

Abstract

fetched live from OpenAlex

Abstract The heterogeneous land surface spanning the Yellow River irrigated oasis and the adjacent Kubuqi and Ulan Buh Desert (Hetao area) in Inner Mongolia, China, has been noted to frequently generate planetary boundary layer convergence line (BLCL), providing an important source of low-level lifting for convection initiation (CI). As the first field experiment to collect comprehensive observations of vegetation-contrast-resulting thermal circulations that consistently generate BLCLs and lead to CI, the Desert–Oasis Convergence Line and Deep Convection Experiment (DECODE) was conducted from 5 July to 9 August 2022 in the Hetao area. Two oasis and four desert observation sites were set up in the region that exhibits the highest frequency of BLCL and CI occurrences, equipped with a suite of advanced instruments probing land–atmosphere interactions, planetary boundary layer processes, and evolution of BLCLs and their associated CI, including Doppler lidars, microwave radiometers, soil temperature and moisture sensors, eddy covariance systems, portable radiosondes, C-band polarimetric Doppler radar, aircraft, and Geostationary High-speed Imager onboard FY-4B satellite. DECODE captured 29 BLCLs (16 with CI), 66 gust fronts, 12 horizontal convective rolls, and one tornado. The observations unveiled full thermal circulations spanning the desert–oasis boundary characterized by a horizontal width of ∼25 km, a convergence height of ∼1 km above ground level (AGL), and divergence from 2 to ∼3.5 km AGL, with vertical wind speeds of up to 2 m s −1 . Future publications stemming from DECODE will delve into a spectrum of scientific inquiries, including but not limited to land surface and boundary layer processes, BLCL dynamics, CI mechanisms, convective organization, predictability, and model evaluation. Significance Statement The purpose of Desert–Oasis Convergence Line and Deep Convection Experiment (DECODE) is to collect intensive observations around desert–oasis divide to better understand the physical processes of boundary layer convergence lines (BLCLs), their associated thermal circulations, convection initiation (CI), and organization over heterogeneous land surfaces. This is important because deep convection initiation poses a major challenge for weather forecasting over heterogeneous land surfaces due to limited understanding of land–atmosphere interactions. In addition, the targeting divide between a desert and an irrigated oasis facilitates a unique situation where human activities can change the weather. This campaign captured 29 BLCLs (16 with CI), which provided a rich dataset for studies not only on convection dynamics and microphysics but also on land–atmosphere interactions, boundary layer processes, and even interdisciplinary studies such as wind farm impact and convective dust 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)0.000
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.234
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

Citations6
Published2024
Admission routes1
Has abstractyes

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