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Record W2808589385 · doi:10.5194/amt-11-6271-2018

Stratosphere–troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

2018· article· en· W2808589385 on OpenAlexafffund
Jeffrey A. Geddes, Randall V. Martin, E. J. Bucsela, C. A. McLinden, Daniel Cunningham

Bibliographic record

VenueAtmospheric measurement techniques · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change CanadaDalhousie University
FundersDalhousie UniversityEnvironment and Climate Change CanadaNational Aeronautics and Space Administration
KeywordsGeostationary orbitTroposphereStratosphereEnvironmental scienceContext (archaeology)SatelliteAtmospheric sciencesMeteorologyRemote sensingGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO 2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere–troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO 2 compared to stratospheric NO 2 . These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO 2 column densities ( R 2 =0.999, slope=1.009 for July and R 2 =0.998, slope=0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R 2 =0.995, slope=1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected ( R 2 =0.924 and slope=0.973 for July and R 2 =0.996 and slope=1.008 for January), with 90 % of the pixels having differences of less than ±0.2×1015 molecules cm −2 between the TEMPO tropospheric NO 2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO 2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO 2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.237
Teacher spread0.214 · 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 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

Citations18
Published2018
Admission routes2
Has abstractyes

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