An open educational approach to teaching convection-resolving modeling with Cloud Model 1 (CM1)
Bibliographic record
Abstract
We present our experiences from a two-week educational block course, first conducted at the University of Bonn during the 2023 winter semester, which introduced students to the nonhydrostatic, time-dependent, convection-resolving Cloud Model 1 (CM1). The course provided hands-on training in configuring and running CM1 simulations on a high-performance computing cluster, offering participants practical experience in the numerical modeling of moist convection. An introduction to three-dimensional visualization tools enabled students to transform simulation output into graphical representations, facilitating the interpretation of cloud dynamics.Pre- and post-course surveys demonstrated significant gains in students’ understanding of atmospheric processes and in transferable skills such as high-performance computing and data visualization. The course was structured in two parts: the first covered core concepts, while the second allowed students to apply their knowledge to independent research projects. Initially designed for meteorology students with a strong background in atmospheric science, the course is now being adapted for physics students at Western University. As part of this transition, new learning materials are being developed, and preliminary outcomes will be presented.The course has already led to several bachelor’s and master’s thesis projects, as well as undergraduate research experiences focused on severe convective storms in Canada. Many of the students involved in these projects have contributed as co-authors to the present work. All course materials are available as open educational resources (Schielicke, L., January 2024: Cloud Model 1 & Visualization-A Block course. ResearchGate, http://dx.doi.org/10.13140/RG.2.2.30017.12642).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".