The Cloud Resolving Model Radar Simulator (CR-SIM) Version 3.2:Description and Applications of a Virtual Observatory
Bibliographic record
Abstract
Abstract. Ground-based observatories use multi-sensor observations to characterize cloud and precipitation properties. A challenge is how to design strategies to best use these observations to understand the atmosphere and evaluate atmospheric numerical prediction models. This paper introduces the Cloud resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution atmospheric models to emulate multi-wavelength, zenith-pointing, and scanning radar observables and multi-sensor (multi-radar and radar-lidar) integrated products. CR-SIM allows comparisons of the same variables between an atmospheric model simulation and remote sensing products using a forward modeling framework consistent with the microphysical assumptions used in the numerical model simulations. In this paper, we present several applications of CR-SIM for evaluation of a numerical model, quantification of retrieval uncertainty, and optimization of radar sampling strategy using observing system simulation experiments. These applications demonstrate that the application of CR-SIM as a virtual observatory operator on high-resolution model output helps interpret the differences between model results and observations and also improve under-standing of the representativeness errors due to the sampling limitations of the ground-based observatories. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are freely available to scientific community.
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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 | 0.000 | 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) | 0.001 | 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 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".