On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure
Bibliographic record
Abstract
Abstract Cloud-computing resources are increasingly used in atmospheric research and real-time weather forecasting. The aim of this study is to explore new ways to reduce cloud-computing costs for real-time numerical weather prediction (NWP). One way is to compress output files to reduce data egress costs. File compression techniques can reduce data egress costs by over 50%. Data egress costs can be further minimized by postprocessing in the cloud and then exporting the smaller resulting files while discarding the bulk of the raw NWP output. Another way to reduce costs is to use preemptible resources, which are virtual machines (VMs) on the Google Cloud Platform (GCP) that clients can use at an 80% discount (compared to nonpreemptible VMs), but which can be turned off by the GCP without warning. By leveraging the restart functionality in the Weather Research and Forecasting (WRF) Model, preemptible resources can be used to save 60%–70% in weather simulation costs without compromising output reliability. The potential cost savings are demonstrated in forecasts over the Canadian Arctic and in a case study of NWP runs for the West African monsoon (WAM) of 2017. The choice in model physics, VM specification, and use of the aforementioned cost-saving measures enable simulation costs to be low enough such that the cloud can be a viable platform for running short-range ensemble forecasts when compared to the cost of purchasing new computer hardware.
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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".