Simulation of an Orographic Precipitation Event during IMPROVE-2. Part I: Evaluation of the Control Run Using a Triple-Moment Bulk Microphysics Scheme
Bibliographic record
Abstract
This paper reports the first evaluation of the Milbrandt–Yau multimoment bulk microphysics scheme against in situ microphysical measurements. The full triple-moment version of the scheme was used to simulate a case of orographically enhanced precipitation with a 3D mesoscale model at high resolution (4- and 1-km grid spacings). The simulations described in this paper also serve as the control runs for the sensitivity experiments that will be examined in Part II of this series. The 13–14 December 2001 case of heavy orographically enhanced precipitation, which occurred over the Oregon Cascades, was selected since it was well observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) observational campaign. The simulated fields were compared with observed radar reflectivity, vertical velocity, precipitation quantities from rain gauges, and microphysical quantities measured in situ by two instrumented aircraft. The simulated reflectivity structure and values compared favorably to radar observations during the various precipitation stages of the event. The vertical motion field in the simulations corresponded reasonably well to the mountain-wave pattern obtained from in situ and dual-Doppler radar inferred measurements, indicating that biases in the simulations can be attributed in part to the microphysics scheme. The patterns of 18-h accumulated precipitation showed that the model correctly simulated the bulk of the precipitation to accumulate along the coastal mountains and along the windward slope of the Cascades, with reduced precipitation on the lee side of the crest. However, both the 4- and 1-km simulations exhibited a general overprediction of precipitation quantities. The model also exhibited a distinct bias toward overprediction of the snow mass concentration aloft and underprediction of the mass and vertical extent of the pockets of cloud liquid water on the windward side of the Cascades. Nevertheless, the overall spatial distribution of the hydrometeor fields was simulated realistically, including the mean-mass particle diameters for each category and the observed trend of larger snow sizes to be located at lower altitudes.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".