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Record W4391359480 · doi:10.5194/egusphere-2023-3129

Technical Note: Evaluation of the Copernicus Atmosphere Monitoring Service Cy48R1 upgrade of June 2023

2024· preprint· en· W4391359480 on OpenAlexfundno aff
Henk Eskes, Athanasios Tsikerdekis, Melanie Ades, Mihai Alexe, Anna Benedictow, Y. Bennouna, Lewis R. Blake, Idir Bouarar, Simon Chabrillat, Richard Engelen, Quentin Errera, Johannes Flemming, Sébastien Garrigues, Jan Griesfeller, Vincent Huijnen, Luka Ilić, Antje Inness, John Kapsomenakis, Zak Kipling, Bavo Langerock, Augustin Mortier, Mark Parrington, Isabelle Pison, Mikko R. A. Pitkänen, Samuel Rémy, Andreas Richter, Anja Schoenhardt, Michael Schulz, V. Thouret, Thorsten Warneke, Christos Zerefos, Vincent‐Henri Peuch

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersGoddard Space Flight CenterNational Oceanic and Atmospheric AdministrationEuropean Organization for the Exploitation of Meteorological SatellitesEuropean CommissionNetherlands Space OfficeLangley Research CenterNational Aeronautics and Space AdministrationEuropean Space AgencyUniversity of Waterloo
KeywordsCopernicusUpgradeAtmosphere (unit)Environmental scienceService (business)MeteorologyBusinessAstrobiologyComputer sciencePhysicsMarketing

Abstract

fetched live from OpenAlex

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) is providing daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3, CO, NO2, HCHO, SO2, aerosol species and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecast System (IFS) for numerical weather prediction (NWP), and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for COMPO and for NWP. The main COMPO innovations include the introduction of full stratospheric chemistry, a major update of the emissions, of the aerosol model, including the representation of secondary organic aerosol, several updates of the dust life cycle and optics, inorganic chemistry in the troposphere, and the assimilation of VIIRS AOD and TROPOMI CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023 during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower-middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with ACE-FTS observations between 10–200 hPa, with larger deviations between 1–10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3, NO2, and SO2. The CO column assimilation is now anchored by IASI instead of MOPITT which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a score card, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade.

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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.003

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 head0.034
GPT teacher head0.287
Teacher spread0.253 · 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
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

Citations3
Published2024
Admission routes1
Has abstractyes

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