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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

fetched live from OpenAlex

Abstract. 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 NH3 with larger spatial coverage. These observations may be used to improve the quantification of NH3 deposition. In this paper, we use a relatively simple, data-driven method to derive dry deposition fluxes and surface concentrations of NH3 for Europe and for the Netherlands. The aim of this paper is to determine the applicability and the limitations of this method for NH3. Space-born observations of the Infrared Atmospheric Sounding Interferometer (IASI) and the LOTOS-EUROS atmospheric transport model are used. The original modelled dry NH3 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 NH3 concentrations are higher than the modelled ones, and in Switzerland, northern Italy (Po Valley) and southern Turkey, where the modelled NH3 concentrations are higher than the satellite-observed ones. A sensitivity analysis of eight model input parameters important for NH3 dry deposition modelling showed that the IASI-derived dry NH3 deposition fluxes may vary from ∼ 20 % up to ∼50 % throughout Europe. Variations in the NH3 dry deposition velocity led to the largest deviations in the IASI-derived dry NH3 deposition flux and should be focused on in the future. A comparison of NH3 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 NH3 surface concentrations compared to the originally modelled NH3 surface concentrations from LOTOS-EUROS. It is concluded that the IASI-derived NH3 deposition fluxes do not show strong improvements compared to modelled NH3 deposition fluxes and there is a future need for better, more robust, methods to derive NH3 dry deposition fluxes.

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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 categoriesMeta-epidemiology (narrow)
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 score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.196
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 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

Citations19
Published2018
Admission routes1
Has abstractyes

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