WRF‐simulated sensitivity to land surface schemes in short and medium ranges for a high‐temperature event in <scp>E</scp>ast <scp>C</scp>hina: A comparative study
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Bibliographic record
Abstract
Abstract We designed simulations for the high‐temperature event that occurred on 23 July 2003 in East China using a series of forecast lead times, from short‐range to medium‐range, and four land surface schemes (LSSs) (i.e., SLAB, NOAH, RUC, and PX) in the Weather Research and Forecasting Model (WRF), Version 3. The sensitivities of short and medium‐range simulations to the LSSs systematically varied with the lead times. In general, the model reproduced short‐range, high‐temperature distributions. The simulated weather was sensitive to the LSSs, and the LSS‐induced sensitivity was higher in the medium range than in the short‐range. Furthermore, the LSS performances were complex, i.e., the PX errors apparently increased in the medium range (longer than 6 days), RUC produced the maximum errors, and SLAB and NOAH had approximately equivalent errors that slightly increased. Additional sensitivity simulations revealed that the WRF modeling system assigns relatively low initial soil moisture for RUC and that soil moisture initialization plays an important role that is comparable to the LSS choice in the simulations. LSS‐induced negative feedback between surface air temperature (SAT) and atmospheric circulation in the lower atmosphere was found in the medium range. These sensitivities were mainly caused by the LSS‐induced differences in surface sensible heat flux and by errors associated with the lead times. Using the SAT equation, further diagnostic analyses revealed LSS deficiencies in simulating surface fluxes and physical processes that modify the SAT and indicated the main reasons for these deficiencies. These results have implications for model improvement and application.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it