A multi-sensor upper tropospheric ozone product (MUTOP) based on TES ozone and GOES water vapor: validation with ozonesondes
Bibliographic record
Abstract
Abstract. Accurate representation of ozone in the extratropical upper troposphere (UT) remains a challenge. However, the implementation of hyper-spectral remote sensing using satellite instruments such as the Tropospheric Emission Spectrometer (TES) provides an avenue for mapping ozone in this region, from 500 to 300 hPa. As a polar orbiting satellite TES observations are limited, but in this paper they are combined with geostationary satellite observations of water vapor. This paper describes a validation of the Multi-sensor UT Ozone Product (MUTOP). MUTOP, based on a statistical retrieval method, is an image product derived from the multiple regression of remotely sensed TES ozone, against geostationary (GOES) specific humidity (remotely sensed) and potential vorticity (a modeled dynamical tracer in the UT). These TES-derived UT ozone mixing ratios are compared to coincident ozonesonde measurements of layer-average UT ozone mixing ratios made during the NASA INTEX/B field campaign in the spring of 2006; the region for this study is effectively the GOES west domain covering the eastern North Pacific Ocean and the western United States. This intercomparison evaluates MUTOP skill at representing ozone magnitude and variability in this region of complex dynamics. In total, 11 ozonesonde launch sites were available for this study, providing 127 individual sondes for comparison; the overall mean ozone of the 500–300 hPa layer for these sondes was 78.0 ppbv. MUTOP reproduces in~situ measurements reasonably well, producing an UT mean of 82.3 ppbv, with a mean absolute error of 12.2 ppbv and a root mean square error of 16.4 ppbv relative to ozonesondes across all sites. An overall UT mean bias of 4.3 ppbv relative to sondes was determined for MUTOP. Considered in the context of past TES validation studies, these results illustrate that MUTOP is able to maintain accuracy similar to TES while expanding coverage to the entire GOES-West satellite domain. In addition MUTOP provides six-hour temporal resolution throughout the INTEX-B study period, making the visualization of UT ozone dynamics possible. This paper presents the overall statistical validation as well as a selection of ozonesonde case studies. The case studies illustrate that error may not always represent a lack of TES-derived product skill, but often results from discrepancies driven by observations made in the presence of strong meteorological gradients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 source (direct Gemma or distilled Codex), 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".