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Record W4392595889 · doi:10.5194/egusphere-egu24-1132

Global evaluation of HCHO summertime diurnal variability using Pandonia Global Network (PGN)

2024· preprint· en· W4392595889 on OpenAlexaff
Tianlang Zhao, Jingqiu Mao, Xiaoyi Zhao, Elena Lind, T. F. Hanisco

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsEnvironmental scienceClimatologyDiurnal cycleDiurnal temperature variationAtmospheric sciencesMeteorologyGeographyPhysicsGeology

Abstract

fetched live from OpenAlex

Formaldehyde (HCHO) serves as an important proxy for emissions of volatile organic compounds (VOCs) and their subsequent photochemistry affecting air quality and climate. Understanding HCHO diurnal variability is essential to accurately represent emissions, chemistry, and planetary boundary layer (PBL) mixing in chemical transport models (CTMs). Here we compare HCHO diurnal variations from Pandora Global Network (PGN), GEOS-CF (0.25°x0.25°) and GEOS-Chem (2°x2.5°) CTMs at 55 sites, to characterize the HCHO diurnal patterns in urban and rural sites over North America (NA), Europe (EU) and East Asia (AS) in 2021-2022 summers. We find that HCHO total column (HCHOcol) from GEOS-CF model shows a comparable stronger diurnal variability (quantified by relative amplitude) with that from PGN measurements, which is lower in GEOS-Chem (10-200% bias in late afternoon). While models and PGN show comparable HCHOcol at rural sites (e.g., ChapelHillNC and DearbornMI), PGN shows significantly higher (a factor of 2 - 3) local noon HCHOcol in some urban areas (e.g., Busan and Bangkok), suggesting missing Volatile Organic Compounds (VOCs) emissions in the models. We further examine the relationship between HCHOcol and HCHO near-surface concentration (HCHOsurf). While both model and PGN show a linear relationship (p

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0020.000

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 head0.073
GPT teacher head0.308
Teacher spread0.234 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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