Verification of high‐speed solar wind stream forecasts using operational solar wind models
Bibliographic record
Abstract
Abstract High‐speed solar wind streams emanating from coronal holes are frequently impinging on the Earth's magnetosphere causing recurrent, medium‐level geomagnetic storm activity. Modeling high‐speed solar wind streams is thus an essential element of successful space weather forecasting. Here we evaluate high‐speed stream forecasts made by the empirical solar wind forecast (ESWF) and the semiempirical Wang‐Sheeley‐Arge (WSA) model based on the in situ plasma measurements from the Advanced Composition Explorer (ACE) spacecraft for the years 2011 to 2014. While the ESWF makes use of an empirical relation between the coronal hole area observed in Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images and solar wind properties at the near‐Earth environment, the WSA model establishes a link between properties of the open magnetic field lines extending from the photosphere to the corona and the background solar wind conditions. We found that both solar wind models are capable of predicting the large‐scale features of the observed solar wind speed (root‐mean‐square error, RMSE ≈100 km/s) but tend to either overestimate (ESWF) or underestimate (WSA) the number of high‐speed solar wind streams (threat score, TS ≈ 0.37). The predicted high‐speed streams show typical uncertainties in the arrival time of about 1 day and uncertainties in the speed of about 100 km/s. General advantages and disadvantages of the investigated solar wind models are diagnosed and outlined.
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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 | 0.003 |
| 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 | 0.000 |
| 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".