Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction
Bibliographic record
Abstract
The Kalman filter (KF) is a recursive algorithm to estimate a signal from noisy measurements. In this study it is tested in predictor mode, to postprocess ozone forecasts to remove systematic errors. The recent past forecasts and observations are used by the KF to estimate the future bias. This bias correction is calculated separately for, and applied to, 12 different air quality (AQ) forecasts for the period 11–15 August 2004, over five monitoring stations in the Lower Fraser Valley, British Columbia, Canada, a population center in a complex coastal mountain setting. The 12 AQ forecasts are obtained by driving an AQ Model (CMAQ) with two mesoscale meteorological models (each run at two resolutions) and for three emission scenarios (Delle Monache et al., 2006). From the 12 KF AQ forecasts an ensemble mean is calculated (EK). This ensemble mean is also KF bias corrected, resulting in a high‐quality estimate (KEK) of the short‐term (1‐ to 2‐day) ozone forecast. The Kalman filter predictor bias‐corrected ensemble forecasts have better forecast skill than the raw forecasts for the locations and days used here. The corrected forecasts are improved for correlation, gross error, root mean square error, and unpaired peak prediction accuracy. KEK is the best and EK is the second best forecast overall when compared with the other 12 forecasts. The reason for the success of EK and KEK is that both the systematic and unsystematic errors are reduced, the first by Kalman filtering and the second by ensemble averaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".