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Record W4245657300 · doi:10.5194/amt-2017-38

Validation of the CrIS Fast Physical NH <sub>3</sub> Retrieval with ground-based FTIR

2017· preprint· en· W4245657300 on OpenAlexaff
Enrico Dammers, Mark W. Shephard, Mathias Palm, Karen Cady‐Pereira, Shannon L. Capps, Erik Lutsch, Kimberly Strong, James W. Hannigan, Iván Ortega, Geoffrey C. Toon, Wolfgang Stremme, Michel Grutter, Nicholas P. Jones, Dan Smale, Jacob Siemons, Kevin Hrpcek, Denis Tremblay, Martijn Schaap, Justus Notholt, Jan Willem Erisman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of TorontoEnvironment and Climate Change Canada
Fundersnot available
KeywordsEnvironmental scienceSatelliteFourier transform infrared spectroscopyChemistryAtmospheric sciencesRemote sensingMeteorologyPhysicsGeographyOptics

Abstract

fetched live from OpenAlex

Abstract. Global reactive nitrogen emissions into the air have increased to unprecedented levels. Limiting the loss of reactive nitrogen into the environment is one of the major challenges for humankind. At the current levels ammonia (NH3) is a threat to both the environment and human health. However, relatively little is known about the total nitrogen budget and distribution around the world, due in part to the sparseness of observations over most of the globe. Recent advances in the capabilities of measuring NH3 with satellite instruments have improved the situation with sensors such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) making twice daily observations with global coverage. However, these require validation to be truly useful, and one of the main challenges in the validation of the satellite NH3 profile and total column data products is the scarcity of measurements that can be directly compared. Presented here is the validation of the CrIS Fast Physical Retrieval (CFPR) NH3 column and profile measurements using ground-based Fourier Transform Infrared (FTIR) observations. We use the total columns and profiles from seven FTIR sites in the Network for the Detection of Atmospheric Composition Change (NDACC) to validate the satellite data products. The overall FTIR and CrIS total columns compare well with a correlation of r = 0.77 (N= 218) with very little bias (a slope of 1.02). Binning the comparisons by total column amounts, for concentrations larger than 1.0 x 1016 molecules cm−2, i.e. ranging from moderate to polluted conditions, the relative difference is on average ~ 0–5 % with a standard deviation of 25–50 %, which is comparable to the estimated retrieval uncertainties in both CrIS and the FTIR. For the smallest total column range where there are a large number of observations at or near the CrIS noise level (detection limit) and the FTIR total columns are smaller than 1.0 x 1016 molecules cm−2, the absolute differences between CrIS and the FTIR total columns are small with CrIS showing a slight positive column bias around +2.4 x 1015 (standard deviation = 5.5 x 1015) molecules cm−2, which corresponds to a relative difference of ~ +50 % (std = 100 %). The CrIS retrievals for these comparisons typically show good vertical sensitivity down to ~850 hPa, and at this level the retrieved profiles also compare well with the median absolute difference of 0.87 (±0.08) ppb and a corresponding median relative difference of 39 (±2) %. Most of the absolute and relative profile comparison differences are in the range of the estimated retrieval uncertainties. However, the CrIS retrieval does tend to overestimate the concentrations in the levels near the surface at low concentrations, most probably due to the detection limit of the instrument, and at higher concentrations shows more of an underestimation of the concentrations in these lower levels.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.211
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2017
Admission routes1
Has abstractyes

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