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Record W2888014864 · doi:10.5194/acp-18-12289-2018

Identification of topographic features influencing aerosol observations at high altitude stations

2018· article· en· W2888014864 on OpenAlexaff
Martine Collaud Coen, Elisabeth Andrews, Diego Aliaga, Marcos Andrade, Hristo Angelov, Nicolas Bukowiecki, Marina Ealo, Paulo Fialho, H. Flentje, A. Gannet Hallar, Rakesh K. Hooda, Ivo Kalapov, Radovan Krejčí, Neng-Huei Lin, Angela Marinoni, Jing Ming, Nhat Anh Nguyen, Marco Pandolfi, Véronique Pont, Ludwig Ries, Sergio Rodrı́guez, Gerhard Schauer, Karine Sellegri, Sangeeta Sharma, Junying Sun, Peter Tunved, Patricio Velasquez, Dominique Ruffieux

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersFundação para a Ciência e a TecnologiaDirektion für Entwicklung und ZusammenarbeitNorsk PolarinstituttVetenskapsrådetKnut och Alice Wallenbergs StiftelseSvenska Forskningsrådet FormasMinistero dell’Istruzione, dell’Università e della RicercaAcademy of FinlandUlkoministeriöNaturvårdsverketPaul Scherrer InstitutSwedish Foundation for International Cooperation in Research and Higher EducationEuropean Regional Development FundUmweltbundesamtTechnische Universität WienEuropean CommissionDesert Research InstituteMinisterio de Economía y CompetitividadNational Science Foundation
KeywordsAltitude (triangle)Effects of high altitude on humansElevation (ballistics)Environmental sciencePlanetary boundary layerTroposphereStructural basinMeteorologyLapse rateBoundary layerDigital elevation modelGeologyAtmospheric sciencesRemote sensingGeographyGeomorphologyGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract. High altitude stations are often emphasized as free tropospheric measuring sites but they remain influenced by atmospheric boundary layer (ABL) air masses due to convective transport processes. The local and meso-scale topographical features around the station are involved in the convective boundary layer development and in the formation of thermally induced winds leading to ABL air lifting. The station altitude alone is not a sufficient parameter to characterize the ABL influence. In this study, a topography analysis is performed allowing calculation of a newly defined index called ABL-TopoIndex. The ABL-TopoIndex is constructed in order to correlate with the ABL influence at the high altitude stations and long-term aerosol time series are used to assess its validity. Topography data from the global digital elevation model GTopo30 were used to calculate five parameters for 43 high and 3 middle altitude stations situated on five continents. The geometric mean of these five parameters determines a topography based index called ABL-TopoIndex, which can be used to rank the high altitude stations as a function of the ABL influence. To construct the ABL-TopoIndex, we rely on the criteria that the ABL influence will be low if the station is one of the highest points in the mountainous massif, if there is a large altitude difference between the station and the valleys or high plains, if the slopes around the station are steep, and finally if the inverse drainage basin potentially reflecting the source area for thermally lifted pollutants to reach the site is small. All stations on volcanic islands exhibit a low ABL-TopoIndex, whereas stations in the Himalayas and the Tibetan Plateau have high ABL-TopoIndex values. Spearman's rank correlation between aerosol optical properties and number concentration from 28 stations and the ABL-TopoIndex, the altitude and the latitude are used to validate this topographical approach. Statistically significant (SS) correlations are found between the 5th and 50th percentiles of all aerosol parameters and the ABL-TopoIndex, whereas no SS correlation is found with the station altitude. The diurnal cycles of aerosol parameters seem to be best explained by the station latitude although a SS correlation is found between the amplitude of the diurnal cycles of the absorption coefficient and the ABL-TopoIndex.

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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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.999

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 studies0.0000.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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 designBench or experimental
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

Citations108
Published2018
Admission routes1
Has abstractyes

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