A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange: evaluation during a balloon sonde campaign in eastern Canada
Bibliographic record
Abstract
Abstract. A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange (STE) developed for Environment Canada (EC) has been delivering daily archived data since July 2010. The STE calculations are performed following the Lagrangian approach proposed in Bourqui (2006) using medium-range, high-resolution operational global weather forecasts. Following every weather forecast, trajectories are started from a dense three-dimensional grid covering the globe, and are calculated forward in time for six days of the forecast. All trajectories crossing either the dynamical tropopause (±2 PVU) or the 380 K isentrope and having a residence time greater than 12 h are archived, and also used to calculate several diagnostics. This system provides daily global STE forecasts that can be used to guide field campaigns, among other applications. The archived data set offers unique high-resolution information on transport across the tropopause for both extra-tropical hemispheres and the tropics. This will be useful for improving our understanding of STE globally, and as a reference for the evaluation of lower-resolution models. This new data set is evaluated here against measurements taken during a balloon sonde campaign with daily launches from three stations in eastern Canada (Montreal, Egbert, and Walsingham) for the period 12 July to 4 August 2010. The campaign found an unexpectedly high number of observed stratospheric intrusions: 79% (38%) of the profiles appear to show the presence of stratospheric air below than 500 hPa (700 hPa). An objective identification algorithm developed for this study is used to identify layers in the balloon-sonde profiles affected by stratospheric air and to evaluate the Lagrangian STE forecasts. We find that the predictive skill for the overall intrusion depth is very good for intrusions penetrating down to 300 and 500 hPa, while it becomes negligible for intrusions penetrating below 700 hPa. Nevertheless, the statistical representation of these deep intrusions is reasonable, with an average bias of 24%. Evaluation of the skill at representing the detailed structures of the stratospheric intrusions shows good predictive skill down to 500 hPa, reduced predictive skill between 500 and 700 hPa, and none below. A significant low statistical bias of about 30% is found in the layer between 500 to 700 hPa. However, analysis of missed events at one site, Montreal, shows that 70% of them coincide with candidate clusters of trajectories that pass through Montreal, but that are too dispersed to be detected in the close neighbourhood of the station. Within the limits of this study, this allows us to expect a negligible bias throughout the troposphere in the spatially averaged STE frequency derived from this data set, for example in climatological maps of STE mass fluxes. This first evaluation is limited to eastern Canada in one summer month with a high frequency of stratospheric intrusions, and further work is needed to evaluate this STE data set in other months and locations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".