The operational Near-Real-Time Total Ozone Retrieval Algorithm for GOME-2 on MetOp-A & MetOp-B and perspectives for TROPOMI/S5P
Bibliographic record
Abstract
The Montreal Protocol and its amendments were designed to reduce the production and consumption of ozone depleting substances and thereby introducing l a gradual recovery of the Earth´s fragile ozone layer. However, the exact timing of the expected ozone recovery and the inter-relationship between the ozone layer and on-going climate change are still under investigation. Therefore, long-term satellite total ozone datasets of high accuracy and stability are essential for monitoring the evolution of the stratospheric ozone layer. \nThe TROPOMI (Tropospheric Monitoring Instrument) is the payload instrument for the Sentinel 5 Precursor (S5P) mission which will provide atmospheric composition products including ozone during the time frame from 2016 to 2022. It will consequently extend the data record initiated with GOME/ERS-2 and continued with the SCIAMACHY/ENVISAT, OMI/AURA and GOME-2/MetOp missions. \nHere we present the Near-Real-Time (NRT) TROPOMI/S5P total ozone retrieval algorithm which is based on the "DOAS-style" GOME Data Processor (GDP) algorithm Version 4.x. The DOAS technique for total ozone retrieval was deployed from the start of the GOME/ERS-2 mission in 1995 and is currently being used for the generation of the ESA SCIAMACHY and EUMETSAT O3M-SAF GOME-2 operational products. The enhancements in GDP 4.8 (the latest version of the GDP 4.x algorithm) are described first, and then we present the Global validation results for GOME-2/MetOp-A (GOME-2A) and GOME-2/MetOp-B (GOME-2B) total ozone measurements using Brewer and Dobson measurements as references. It is concluded that total ozone columns (TOCs) retrieved from GOME-2 using GDP 4.8 show very good agreement with ground-based measurements. TOCs from GOME-2A and GOME-2B are consistent with each other and may be used simultaneously without introducing trends or other systematic effects. \n \nOne of the major challenges for the operational processing of TROPOMI/S5P measurements is the high data rate - two orders of magnitude more data than that from GOME-2. Here we discuss performance enhancements of the retrieval algorithms such as the development of an acceleration method for Radiative Transfer Model (RTM) simulations. \nGOME-2A and GOME-2B total ozone data have been used operationally in the Copernicus atmospheric service project MACC-II/III (Monitoring Atmospheric Composition and Climate - Interim Implementation) NRT system since October 2013 and May 2014 respectively. It is expected that the follow-on Copernicus Atmosphere Monitoring Service (CAMS) project will use NRT TROPOMI/S5P in addition to GOME-2 total ozone data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".