Evaluation of WRF-Forecasts Over Siberia: Air Mass Formation, Clouds and Precipitation
Bibliographic record
Abstract
The Weather Research and Forecasting (WRF) model was run as a regional model without data assimilation or nudging (31 36h-simulations) for July and December 2005 over a limited area domain covering Siberia to examine weather formation in an air-mass source region. The WRF-results were compared to NCEP1/NCAR-reanalysis, International Satellite Cloud Climatology Project, Global Precipitation Climatology Centre and Canadian Meteorological Centre data to assess model performance and identify shortcomings. WRF is capable of predicting air-mass formation. Simulation errors are within the error range of other models. The timing of best/worst agreement differs among quantities depending on their sensitivity to systematic (model deficiencies) and/or unsystematic errors (e.g. initial conditions). Overall, the WRF-results agree better with reanalysis for July than December. WRF-results and reanalysis agree best under persistent high pressure and worst during frontal passages and transition from one pressure regime to another. In July, WRF provides smaller diurnal amplitudes of 2m-temperature with up to 5.4 K lower, and 3.5 K higher values at 0000 and 1200 UTC than the reanalysis. In December, WRF overestimates 2m-temperature by 1.4 K. WRF-temperatures excellently agree with the reanalysis from 700 hPa to 300 hPa. Except during frontal passages, wind-speed shows positive bias. Typically root-mean-square errors and standard deviation of errors of wind-speed (temperature) increase (decrease) with height. In December, WRF has difficulty predicting the position and strength of the polar jet. WRF underestimates cloudiness and snow-depth, but overestimates precipitation. In July, predicted convective precipitation is related strongly to boundaries between different land-cover. WRF-predicted snow-depth strongly correlates with terrain and misses the observed fine features.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".