Accelerating research through community open source software for a standardized file format to improve process representation in numerical weather prediction models
Bibliographic record
Abstract
Abstract. Improvements in process representation in numerical weather prediction (NWP) models requires informed collaboration between scientists making research-grade observations and scientist developing state-of-the-art NWP models. As a result, progress in model quality relies heavily on the ability to efficiently evaluate and reliably reconcile these two sources of information. To facilitate such progress, with focus on enhanced model skill in polar regions, the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) community defined the Merged Data File (MDF) format. The file format is designed for high temporal and spatial resolution data for direct comparison between observations and model output to assess parameterized processes under various conditions. A broad overview of the MDF format is provided along with supporting use-cases defined by the research community, and present a set of free, open-source, computational tools for creating and utilizing this standardized format. Two free open source Python packages are discussed: 1) “The MDF toolkit", a data processing library for the creation of standardized datasets, and 2) "MDF visualization", a set of Python codes in notebook format that accelerate model evaluation and climate process research utilizing the MDF format. The benefits of such tools that may help unite diverse groups of researchers through a common data-format language are also discussed.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.023 |
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".