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Record W2890397451 · doi:10.5194/acp-18-13173-2018

Technical note: How are NH <sub>3</sub> dry deposition estimates affected by combining the LOTOS-EUROS model with IASI-NH <sub>3</sub> satellite observations?

2018· article· en· W2890397451 on OpenAlexaff
Shelley van der Graaf, Enrico Dammers, Martijn Schaap, Jan Willem Erisman

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersRijksinstituut voor Volksgezondheid en MilieuEuropean Organization for the Exploitation of Meteorological SatellitesNederlandse Organisatie voor Wetenschappelijk OnderzoekCentre National d’Etudes Spatiales
KeywordsDeposition (geology)Flux (metallurgy)Environmental scienceReactive nitrogenAtmospheric sciencesNitrogenSatelliteEutrophicationEurosEnvironmental chemistryChemistryPhysicsNutrientGeologyStructural basinGeomorphology

Abstract

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Atmospheric levels of reactive nitrogen have increased substantially during the last century resulting in increased nitrogen deposition to ecosystems, causing harmful effects such as soil acidification, reduction in plant biodiversity and eutrophication in lakes and the ocean. Recent developments in the use of atmospheric remote sensing enabled us to resolve concentration fields of NH 3 with larger spatial coverage. These observations may be used to improve the quantification of NH 3 deposition. In this paper, we use a relatively simple, data-driven method to derive dry deposition fluxes and surface concentrations of NH 3 for Europe and for the Netherlands. The aim of this paper is to determine the applicability and the limitations of this method for NH 3 . Space-born observations of the Infrared Atmospheric Sounding Interferometer (IASI) and the LOTOS-EUROS atmospheric transport model are used. The original modelled dry NH 3 deposition flux from LOTOS-EUROS and the flux inferred from IASI are compared to indicate areas with large discrepancies between the two. In these areas, potential model or emission improvements are needed. The largest differences in derived dry deposition fluxes occur in large parts of central Europe, where the satellite-observed NH 3 concentrations are higher than the modelled ones, and in Switzerland, northern Italy (Po Valley) and southern Turkey, where the modelled NH 3 concentrations are higher than the satellite-observed ones. A sensitivity analysis of eight model input parameters important for NH 3 dry deposition modelling showed that the IASI-derived dry NH 3 deposition fluxes may vary from ∼ 20 % up to ∼50 % throughout Europe. Variations in the NH 3 dry deposition velocity led to the largest deviations in the IASI-derived dry NH 3 deposition flux and should be focused on in the future. A comparison of NH 3 surface concentrations with in situ measurements of several established networks – the European Monitoring and Evaluation Programme (EMEP), Meetnet Ammoniak in Natuurgebieden (MAN) and Landelijk Meetnet Luchtkwaliteit (LML) – showed no significant or consistent improvement in the IASI-derived NH 3 surface concentrations compared to the originally modelled NH 3 surface concentrations from LOTOS-EUROS. It is concluded that the IASI-derived NH 3 deposition fluxes do not show strong improvements compared to modelled NH 3 deposition fluxes and there is a future need for better, more robust, methods to derive NH 3 dry deposition fluxes.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations19
Published2018
Admission routes1
Has abstractyes

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