Adaptation of the Predicted Particles Properties (P3) Microphysics Scheme for Large-Scale Numerical Weather Prediction
Bibliographic record
Abstract
Abstract A parameterization for the subgrid-scale cloud and precipitation fractions has been incorporated into the Predicted Particle Properties (P3) microphysics scheme for use in atmospheric models with relatively coarse horizontal resolution. The modified scheme was tested in a simple 1D kinematic model and in the Canadian Global Environmental Multiscale (GEM) model using an operational global NWP configuration with a 25-km grid spacing. A series of 5-day forecast simulations was run using P3 and the much simpler operational Sundqvist condensation scheme as a benchmark for comparison. The effects of using P3 in a global GEM configuration, with and without the modifications, were explored through statistical metrics of common forecast fields against upper-air and surface observations. Diagnostics of state variable tendencies from various physics parameterizations were examined to identify possible sources of errors resulting from the use of the modified scheme. Sensitivity tests were performed on the coupling between the deep convection parameterization scheme and the microphysics, specifically regarding assumptions in the physical properties of detrained ice. It was found that even without recalibration of the suite of moist physical parameterizations, substituting the Sundqvist condensation scheme with the modified P3 microphysics resulted in some significant improvements to the temperature and geopotential height bias throughout the troposphere and out to day 5, but with degradation to error standard deviation toward the end of the integrations, as well as an increase in the positive bias of precipitation quantities. The modified P3 scheme was thus shown to hold promise for potential use in coarse-resolution NWP systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".