Evaluation of Polar WRF forecasts on the Arctic System Reanalysis Domain: 2. Atmospheric hydrologic cycle
Bibliographic record
Abstract
The forecast atmospheric hydrologic cycle of the Polar version 3.1.1 of the Weather Research and Forecasting model (WRF) is examined for December 2006 – November 2007. The domain is similar to the Arctic System Reanalysis (ASR), an assimilation of model fields and Arctic observations being conducted partly by the Byrd Polar Research Center. Simulations are performed in 48 h increments initialized daily at 0000 UTC, with the first 24 h discarded for model spin‐up of the hydrologic cycle and boundary layer processes. Precipitation analysis reveals a negative annual mean bias (−9.4%) in the polar region, with particularly dry station biases reflected in the Canadian Archipelago. Annual mean bias for the midlatitudes is small and positive (4.6%), attributed to excessive precipitation during spring and summer when convective precipitation is dominant. An examination of precipitation within four major Arctic river basins shows large positive biases due to excessive convective precipitation in summer as well, but highlights the Arctic climate's strong dependence on midlatitude precipitation. Nudging the model's boundary layer moisture toward drier conditions decreases convective precipitation improving the prediction. Cloud fraction analysis shows too little cloud cover, supported by an excess in incident shortwave radiation and a deficit in downwelling longwave radiation throughout the domain. The longwave bias is present regardless of the amount of cloud water or cloud ice, demonstrating a need to improve cloud effects on radiation in Polar WRF. This examination provides a benchmark of the forecast atmospheric hydrological cycle of Polar WRF and its use as ASR's primary model.
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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 | 0.015 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".