Impact of energetic particle precipitation on stratospheric polar constituents: an assessment using monitoring and assimilation of operational MIPAS data
Bibliographic record
Abstract
Abstract. In 2003, strong energetic particle precipitation (EPP) events occurred producing massive amounts of ionization which affected the polar region significantly perturbing its chemical state down to the middle stratosphere. These events and their effects are generally left unaccounted for in current models of stratospheric chemistry and large differences between observations and models are then noted. In this study, we use a coupled 3-D stratospheric dynamical-chemical model and assimilation system to ingest MIPAS temperature and chemical observations. The goal is to gain further understanding of assimilation and monitoring processes during EPP events and their impacts on the stratospheric polar chemistry. Moreover, we investigate the feasibility of assimilating valid "outlier" observations associated with such events. We use OmF (Observation minus Forecast) residuals as they filter out phenomena well reproduced by the model (such as gas phase chemistry, transport, diurnal and seasonal cycles) thus revealing a clear trace of the EPP. Inspection of OmF statistics in both passive (without chemical assimilation) and active (with chemical assimilation) cases altogether provides a powerful diagnostic tool to assess the model and assimilation system. We also show that passive OmF can permit a satisfactory evaluation of the ozone partial column loss due to EPP effects. Results suggest a small but significant loss of 5–6 DU (Dobson Units) during an EPP-IE (EPP Indirect Effects) event in the Antarctic winter of 2003, and about only 1 DU for the SPE (Solar Proton Event) of October/November 2003. Despite large differences between the model and MIPAS chemical observations (NO2, HNO3, CH4 and O3), we demonstrate that a careful assimilation with only gas phase chemistry included in the model (i.e. no provision for EPP) and with relaxed quality control nearly eliminated the short-term bias and significantly reduced the standard deviation error of the constituents below 1 hPa.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".