The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains
Bibliographic record
Abstract
Machine learning (ML) techniques have been extensively applied in the field of atmospheric science. It provides an efficient way of integrating data and predicting atmospheric compositions. However, whether ML predictions can be extrapolated to different domains with significant spatial and temporal discrepancies is still unclear. Here we explore the answer to this question by presenting a comparative analysis of surface carbon monoxide (CO) and ozone (O3) predictions by integrating deep learning (DL) and chemical transport model (CTM) methods. The DL model trained with surface CO observations in China in 2015-2018 exhibited good spatial and temporal extrapolation capabilities, i.e., good surface daily CO predictions in China in 2019-2020 and over 10% independent observation stations in China in 2015-2020. The spatial and temporal extrapolation capabilities of DL model are further evaluated by predicting hourly surface O3 concentrations in China, the United States (US) and Europe in 2015-2022 with a DL model trained with surface O3 observations in China and the US in 2015-2018. Compared to baseline O3 simulations using GEOS-Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 µg/m3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 µg/m3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015-2018 and 2019-2022, respectively. This analysis indicates the potential of DL to make reliable atmospheric composition predictions over spatial and temporal domains where a wealth of local observations for training is not available.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".