On the scale-dependence of the predictability of precipitation patterns by numerical weather prediction models
Bibliographic record
Abstract
Despite the increased realism of convective-allowing Numerical Weather Prediction (NWP) forecasts, their accuracy has not improved as expected, especially for quantitative precipitation forecasting (QPF).Many factors can account for the poor QPF skill, from intrinsic predictability limitations to shortcomings of the evaluation method.Given the complexity of precipitation processes, more effort is needed to understand their predictability.In this thesis, data from NOAA's Hazardous Weather Testbed (HWT) forecasting Spring Experiments of 2008-2013 are analyzed to study precipitation predictability at the mesoscale.This unique data set, consisting of high-resolution (4-km grid spacing) After I got a summer job with Prof. Isztar Zawadzki, I wanted to find out more about him, so I googled him.And I came across the Who we are section of the Radar observatory's webpage, where under Isztar's name was written Unlimited interests.Thank you Isztar for having unlimited interests and for inspiring me to learn and to discover.You are much more than a supervisor for me, you are a mentor.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".