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Record W4226147367 · doi:10.5194/amt-2022-90

Differences in MOPITT surface-level CO retrievals and trends from Level 2 and Level 3 products in coastal grid boxes

2022· preprint· en· W4226147367 on OpenAlexafffund
Ian Ashpole, Aldona Wiacek

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSaint Mary's University
FundersCanadian Space Agency
KeywordsEnvironmental scienceGridMeteorologyRemote sensingAtmospheric sciencesClimatologyGeographyGeologyGeodesy

Abstract

fetched live from OpenAlex

Abstract. MOPITT retrievals are more sensitive to near-surface CO when performed over land than water. Data users are therefore advised to discard retrievals performed over water from analyses to limit the a priori influence on results. Level 3 (L3) products are a 1° x 1° gridded average of finer resolution Level 2 (L2) retrievals. For coastal grid boxes, these are retrievals that are either performed over land, water, or a combination of the two, on any given day. L3 data users therefore have limited ability to filter for retrievals performed over water for these grid boxes. The consequences that this has on retrievals and their temporal trends in “as-downloaded” L3 data (L3O) are examined in this paper, for all coastal L3 MOPITT grid boxes (n = 4299), by comparison to separate land- and water-only grid box averaged L2 retrievals (L3L and L3W, respectively). First, it is established that mean retrieved VMRs in L3L and L3W differ by over 10 ppbv, significant (p < 0.1) at 60 % of the coastal grid boxes. Trends are also stronger in L3L (mean difference between 0.28 ppbv y-1 and 0.43 ppbv y-1), with the L3L – L3W trend difference significant at 36 % of grid boxes. These L3L-L3W differences are clearly linked to retrieval sensitivity differences, with L3W being more heavily tied to the a priori CO profiles used in the retrieval, which is a model-derived monthly mean climatology. On days when L3O is created from the averaging together of L2 retrievals over both land and water (L3OM), the result is VMRs that are significantly different to L3L for 75 % of grid boxes where the L3L – L3W difference is also significant, 45 % of all coastal grid boxes. Just under half of the grid boxes that featured a significant L3L – L3W trend difference also see trends differing significantly between L3L and L3OM. Factors that determine significance of difference between L3OM and L3L include proportion of the surface covered by land/water, and the magnitude of sensitivity contrast. Comparing the full L3O dataset to L3L, it is shown that if L3O is filtered so that only retrievals over land (L3OL) are analysed, there is a huge loss of days with data. This is because L2 retrievals over land are routinely discarded during the L3O creation process, for coastal grid boxes. The problem can be lessened by also retaining L3OM retrievals, but the resulting L3O “land or mixed” (L3OLM) subset still has less data days than L3L for 61 % of coastal grid boxes. Moreover, as already shown, these additional days with data feature some influence from retrievals made over water that can affect results. Coastal L3 grid boxes contain 33 of the 100 largest coastal cities in the world, by population. Focusing on the L3 grid boxes containing these cities, it is shown that mean VMRs in L3OL and L3L differ significantly for 11 of the 27 cities that can be compared (there are no L3OL data for 6 of the cities). The L3L – L3OLM mean VMR difference exceeds 10 (22) ppbv for 11 (3) of the 33 cities, significant in 13 cases. 9 of the 18 cities where WLS analysis can be performed in L3OL feature a trend that is significantly different to L3L. The trends in L3OLM and L3L differ significantly for 5 of the 33 cities. It is concluded that a L3 product based only on L2 retrievals over land would be of benefit to MOPITT data users, given the clear and sometimes significant differences in mean CO VMRs and trends that can be obtained for coastal grid boxes using L2 products in which retrievals performed over water can be more easily discarded.

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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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.053
GPT teacher head0.244
Teacher spread0.191 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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