Data assimilation of CrIS NH <sub>3</sub> satellite observations for improving spatiotemporal NH <sub>3</sub> distributions in LOTOS-EUROS
Bibliographic record
Abstract
Atmospheric levels of ammonia (NH 3 ) have substantially increased during the last century, posing a hazard to both human health and environmental quality. The atmospheric budget of NH 3 , however, is still highly uncertain due to an overall lack of observations. Satellite observations of atmospheric NH 3 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 NH 3 . In this study, the CrIS NH 3 product is assimilated into the LOTOS-EUROS chemistry transport model using two different methods aimed at improving the modeled spatiotemporal NH 3 distributions. In the first method NH 3 surface concentrations from CrIS are used to fit spatially varying NH 3 emission time factors to redistribute model input NH 3 emissions over the year. The second method uses the CrIS NH 3 profile to adjust the NH 3 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 NH 3 total columns were up to a factor 2 higher than the simulated NH 3 columns between 2014 and 2018, which, after assimilating the CrIS NH 3 columns using the LETKF algorithm, led to an increase in the total NH 3 emissions of up to approximately 30 %. Our results illustrate that CrIS NH 3 observations can be used successfully to estimate spatially variable NH 3 time factors and improve NH 3 emission distributions temporally, especially in spring (March to May). Moreover, the use of the CrIS-based NH 3 time factors resulted in an improved comparison with the onset and duration of the NH 3 spring peak observed at observation sites at hourly resolution in the Netherlands. Assimilation of the CrIS NH 3 columns with the LETKF algorithm is mainly advantageous for improving the spatial concentration distribution of the modeled NH 3 fields. Compared to in situ observations, a combination of both methods led to the most significant improvements in modeled monthly NH 3 surface concentration and NH4+ wet deposition fields, illustrating the usefulness of the CrIS NH 3 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".