The Impact of Incorporating the Air–Lake Interaction on Quantitative Precipitation Forecasts over Southern Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A short-range regional, two-way coupled atmosphere–ocean–ice model has been recently developed in an attempt to improve, among other things, quantitative precipitation forecasts (QPFs) over southern Ontario, Canada, by incorporating air–lake interaction over the Great Lakes region. Here, we attempt to 1) assess the impact of the air–lake coupling on daily QPFs, as verified against the Canadian Precipitation Analysis and independent observations, over southern Ontario during the period of June 2016–May 2017; and 2) diagnose major physical processes governing the QPF differences between the coupled and uncoupled models by relating precipitation to those processes at the air–water interface and above. Results indicate that the coupled model tends to reduce the area-averaged and monthly averaged daily QPF biases and standard deviations in 5 months of October, November, and December 2016, and April and May 2017, but increase and deteriorate precipitation biases during the summer months. Most of the deteriorations occur during the daytime, while improvements are observed during the nighttime (in 7 of 12 months). During the daytime, slight improvements appear in 2 months. A further diagnosis indicates that the daily QPF differences between the two models are highly correlated with the differences of their sensible and latent heat fluxes. The maximum (minimum) difference of sensible (latent) heat flux in August 2016 (December 2016) is in phase with the maximum (minimum) difference of the two-model daily QPFs. The daily QPF differences in the other months are also controlled by the differences of vertically integrated water vapor flux convergence, and surface temperature.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 it