A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone
Bibliographic record
Abstract
Kalman filtering (KF) is used to estimate systematic errors in surface ozone forecasts. The KF updates its estimate of future ozone-concentration bias using past forecasts and observations. The optimum filter parameter is estimated via sensitivity analysis. KF performance is tested for deterministic, ensemble-averaged and probabilistic forecasts. Eight simulations were run for 56 d during summer 2004 over northeastern USA and southern Canada, with 358 ozone surface stations.KF improves forecasts of ozone-concentration magnitude (measured by root mean square error) and the ability to predict rare events (measured by the critical success index), for deterministic and ensemble-averaged forecasts. It improves the 24-h maximum ozone-concentration prediction (measured by the unpaired peak prediction accuracy), and improves the linear dependency and timing of forecasted and observed ozone concentration peaks (measured by a lead/lag correlation). KF also improves the predictive skill of probabilistic forecasts of concentration greater than thresholds of 10–50 ppbv, but degrades it for thresholds of 70–90 ppbv. KF reduces probabilistic forecast bias. The combination of KF and ensemble averaging presents a significant improvement for real-time ozone forecasting because KF reduces systematic errors while ensemble-averaging reduces random errors. When combined, they produce the best overall ozone forecast.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".