Dynamical Random-Set Modeling of Concentrated Precipitation in North America
Why this work is in the frame
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Bibliographic record
Abstract
In order to study climate at scales where policy decisions can be made, regional climate models (RCMs) have been developed with much finer resolution (∼50 km) than the ∼500 km resolution of atmosphere-ocean general circulation models (AOGCMs). The North American Regional Climate Change Assessment Program (NARCCAP) is an international program that provides 50-km resolution climate output for the United States, Canada, and northern Mexico. In Phase I, there are six RCMs, from which we choose one to illustrate our methodology. The RCMs are updated every 3 hours and contain a number of variables, including temperature, precipitation, wind speed, wind direction, and air pressure; output is available from the years 1968–2000 and from the years 2038–2070. Precipitation is of particular interest to climate scientists, but it can be difficult to study because of its patchy nature: At hourly-up-to-monthly time scales, there are generally many zeroes over the precipitation field. In this research, we study sets of concentrated precipitation (i.e., the union of RCM pixels whose precipitation is above a given threshold), where we are interested in the way these sets evolve from one 3-hour period to the next. Assuming the sets are a realization of a time series of random sets, we are able to build dynamical models for the passage of rainfall fronts over 1-2 days. The dynamics are characterized by a growth/recession model for a time series of random sets, with several parameters that control how the concentrated precipitation changes over time.
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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 | 0.000 | 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 it