Analysis and forecasting of extreme temperature and precipitation across the complex terrain of British Columbia
Bibliographic record
Abstract
The ability to forecast extreme temperature and precipitation events not only helps satisfy the public’s desire to better prepare for such events, but can also provide valuable information about the future risks of such events to emergency managers, regional planners, and policy-makers at all levels of government. This dissertation advances extreme weather forecasting over the complex topography of British Columbia (BC) while accounting for changes in intensity and frequency of extreme events due to nonstationarity. First the problem of finding a dataset to provide climatological distributions is addressed. Weather station data coverage, quality, and temporal completeness across BC degrade outside of population centres, and as one goes back in time. This data paucity motivates the search for the best reanalysis to serve as a climatological reference dataset. The 2-m temperature and daily accumulated precipitation of the reanalyses are compared with observations from meteorological stations distributed over the complex terrain of British Columbia. Upon thorough evaluation, the Japanese 55-year Reanalysis (JRA-55) was found to be best. The second component of this works combines, downscales and bias corrects the best performing reanalysis using the high-spatial-resolution Parameter-Elevation Regressions on Independent Slopes Model (PRISM) dataset and using surface weather station observations. This results in a high-resolution, long-term gridded dataset that is spatially and temporally complete, yielding a very-high-resolution surface analysis (VHRSA). Next, this dataset is used to create a high-resolution, bias-corrected ensemble forecast using the North American Ensemble Forecast System (NAEFS). The post-processed NAEFS is more skillful than the raw NAEFS forecast out to a forecast lead time of 10 days for both 2-m temperature, and daily accumulated precipitation. Statistical temporal stationarity of extreme values of precipitation and temperature are assessed for the 60-year VHRSA period. It is determined that nonstationary distributions should be used to represent annual minima values of daily minimum 2-m temperature during summer months and late winter. Finally, an extreme, or situational awareness index is presented: the Parametric Extreme Index (PEI). It can be used to alert forecasters and other end users of future extreme temperature and precipitation events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".