Calibration of sea ice drift forecasts using random forest algorithms
Bibliographic record
Abstract
There is a growing demand for accurate sea-ice forecasts in the Arctic due to increasing maritime traffic. Although the capabilities of numerical models steadily improve, sea-ice forecasts produced by numerical prediction systems are affected by biases. In order to reduce forecast errors, statistical methods can be used for calibration. In this study, two calibration methods have been developed for calibrating sea-ice drift forecasts from an operational prediction system (TOPAZ4) in the Arctic. These methods are based on random forest algorithms, a machine learning technique suitable for assessing non-linear relationships between a set of predictors and a target variable. While all the algorithms developed in this study use the same set of predictors, two set of algorithms have been developed using either buoy or synthetic-aperture radar (SAR) observations for the target variable. Furthermore, different algorithms have been developed for predicting the direction and the speed of sea-ice drift, as well as for different lead times. The random forest algorithms use predictor variables from sea-ice concentration observations during the initialization of the forecasts, sea-ice forecasts from the TOPAZ4 prediction system, wind forecasts from the European Centre for Medium-Range Weather Forecasts, and some geographical information. The performances of the calibrated forecasts have been evaluated and compared to those from the TOPAZ4 forecasts using buoy observations from the International Arctic Buoy Programme. Depending on the calibration method, the mean absolute error is reduced, on average, between 5.9 % and 8.1 % for the direction, and between 7.1 % and 9.6 % for the speed of sea-ice drift. However, there is a large spatial variability in the performances of these algorithms, and the random forest algorithms have particularly poor performances in the Canadian Archipelago, an area characterized by narrow channels and the presence of landfast ice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".