Simulation of Wet Snow during Winter Orographic Precipitation Using the Predicted Particle Properties (P3) Microphysics Scheme
Bibliographic record
Abstract
Abstract Forecasting winter precipitation types and their transitions is challenging because the type can vary (e.g., rain, snow, freezing rain, ice pellets, wet snow, graupel) and multiple phases can be involved. In this study, simulations of snow, including wet snow, using the predicted particle properties (P3) bulk microphysics scheme that can predict the evolution of mixed-phase particles through the representation of the bulk liquid mass fraction, are analyzed and compared with observational data from the International Collaborative Experiments for Pyeongchang 2017–18 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign that took place in South Korea. Simulations with and without predicted liquid fraction are compared. Predicting the liquid fraction improves the representation of precipitation phases by shifting liquid (rain) to mixed (wet snow) in all six cases where wet snow was observed. Mean simulated liquid mass fractions are higher than the mean retrieved values, while the snow densities are smaller. However, both simulated quantities improve in simulations using the predicted liquid fraction. The trend obtained in the retrievals consisting of higher densities by a factor of 2–3 between the supersites near the coast and the supersites located at higher elevation is well captured by the simulations using the predicted liquid fraction. Differences in the representation of melting with and without the predicted liquid fraction are responsible for the changes in particle densities and the precipitation phase at the surface. This study demonstrates the capability of the P3 scheme with the prediction of liquid fraction to forecast events with dense, wet snow. Significance Statement Wet snow, consisting of particles comprising a liquid and ice mixture, can accumulate quickly on surfaces and can cause extensive damage (e.g., power line damage, vegetation loss, and transportation disruptions). Some properties that characterize wet snow, such as particle density and the liquid mass fraction, have been derived from observations collected during the ICE-POP 2018 field campaign for the 2017–18 Winter Olympics over the east coast of South Korea. Numerical weather model simulations of these wet snow cases, with and without explicitly predicting mixed-phase particles, are examined and compared to the observations. The results show significant improvements in simulating wet snow properties when mixed-phase particles are considered in the 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".