Comment on acp-2021-473
Bibliographic record
Abstract
Abstract. Atmospheric levels of ammonia (NH3) have substantially increased during the last century, posing a hazard to both human health and environmental quality. The atmospheric budget of NH3, however, is still highly uncertain due to an overall lack of observations. Satellite observations of atmospheric NH3 may help us in the current observational and knowledge gaps. Recent observations of the Cross-track Infrared Sounder (CrIS) provide us with daily, global distributions of NH3. In this study, the CrIS NH3 product is assimilated into the LOTOS-EUROS chemistry transport model using two different methods aimed at improving the modeled spatiotemporal NH3 distributions. In the first method NH3 surface concentrations from CrIS are used to fit spatially varying NH3 emission time factors to redistribute model input NH3 emissions over the year. The second method uses the CrIS NH3 profile to adjust the NH3 emissions using a local ensemble transform Kalman filter (LETKF) in a top-down approach. The two methods are tested separately and combined, focusing on a region in western Europe (Germany, Belgium and the Netherlands). In this region, the mean CrIS NH3 total columns were up to a factor 2 higher than the simulated NH3 columns between 2014 and 2018, which, after assimilating the CrIS NH3 columns using the LETKF algorithm, led to an increase in the total NH3 emissions of up to approximately 30 %. Our results illustrate that CrIS NH3 observations can be used successfully to estimate spatially variable NH3 time factors and improve NH3 emission distributions temporally, especially in spring (March to May). Moreover, the use of the CrIS-based NH3 time factors resulted in an improved comparison with the onset and duration of the NH3 spring peak observed at observation sites at hourly resolution in the Netherlands. Assimilation of the CrIS NH3 columns with the LETKF algorithm is mainly advantageous for improving the spatial concentration distribution of the modeled NH3 fields. Compared to in situ observations, a combination of both methods led to the most significant improvements in modeled monthly NH3 surface concentration and NH4+ wet deposition fields, illustrating the usefulness of the CrIS NH3 products to improve the temporal representativity of the model and better constrain the budget in agricultural areas.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.013 |
| Insufficient payload (model declined to judge) | 0.313 | 0.264 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".