Build a Coastal Windstorm Parametric Uncertainty Analyzer
Evaluate parametric wind speed trigger ambiguity for coastal marine ports using ERA5 atmospheric reanalysis grids. Quantify trigger misclassification probability against synthetic station observations.
What you are building
The core problem, expected build, and operating context for this challenge.
Evaluate parametric wind speed trigger ambiguity for coastal marine ports using ERA5 atmospheric reanalysis grids. Quantify trigger misclassification probability against synthetic station observations.
Marine Terminal Risk Consultant
Define port asset coordinates and wind threshold triggers in meters per second.
Retrieve ERA5 10-meter wind components using Copernicus CDS API scripts.
Compute maximum sustained wind speeds and calculate spatial interpolation errors.
Quantify trigger uncertainty distributions and publish terminal risk memos.
Interactive Python CLI and terminal plot view summarizing wind uncertainty distributions.
Execute CDS API calls for reanalysis-era5-single-levels wind variables or use synthetic NetCDF grid samples. Starter examples below are synthetic. They are not downloaded records.
Copernicus Climate Data Store client Sets up cdsapi.Client() configuration and calls cds.retrieve for atmospheric variables with spatial bounding.
ray-serve, cdsapi, numpy, scipy. Ray Serve enables distributed parallel evaluation of parametric trigger simulations across thousands of asset coordinates. Distributed compute cluster or multi-core local host for numeric simulations.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Computes horizontal wind vector magnitude from u and v components accurately.
Applies parametric trigger criteria with explicit confidence intervals.
Logs trigger sensitivity variances across neighboring grid points.
```json [ { "input": { "port_code": "PORT-EAST-01", "u10_m_s": 24, "v10_m_s": 18, "trigger_threshold_m_s": 28 }, "expected_output": { "port_code": "PORT-EAST-01", "wind_speed_m_s": 30, "trigger_exceeded": true, "trigger_confidence": 0.94 } }, { "input": { "port_code": "PORT-EAST-02", "u10_m_s": 15, "v10_m_s": 12, "trigger_threshold_m_s": 28 }, "expected_output": { "port_code": "PORT-EAST-02", "wind_speed_m_s": 19.21, "trigger_exceeded": false, "trigger_confidence": 0.99 } } ] ```
Produces parametric simulation sensitivity metrics without issuing contractual settlement determinations. Use mock external writes. Require human review before any real deployment.
Participation guide
Produces parametric simulation sensitivity metrics without issuing contractual settlement determinations. Use mock external writes and human review.
How work is evaluated
Computes horizontal wind vector magnitude from u and v components accurately. Applies parametric trigger criteria with explicit confidence intervals. Logs trigger sensitivity variances across neighboring grid points.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Define port asset coordinates and wind threshold triggers in meters per second. Retrieve ERA5 10-meter wind components using Copernicus CDS API scripts. Compute maximum sustained wind speeds and calculate spatial interpolation errors. Quantify trigger uncertainty distributions and publish terminal risk memos.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Computes horizontal wind vector magnitude from u and v components accurately. Applies parametric trigger criteria with explicit confidence intervals. Logs trigger sensitivity variances across neighboring grid points.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Interactive Python CLI and terminal plot view summarizing wind uncertainty distributions.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Produces parametric simulation sensitivity metrics without issuing contractual settlement determinations.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Define port asset coordinates and wind threshold triggers in meters per second.
Retrieve ERA5 10-meter wind components using Copernicus CDS API scripts.
Compute maximum sustained wind speeds and calculate spatial interpolation errors.
Quantify trigger uncertainty distributions and publish terminal risk memos.
Reference links and supporting material
Execute CDS API calls for `reanalysis-era5-single-levels` wind variables or use synthetic NetCDF grid samples. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
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Ray Serve
Action Space in the agent workflow.
Evaluated output
No output checks are configured.
- Define port asset coordinates and wind threshold triggers in meters per second. Retrieve ERA5 10-meter wind...
- Computes horizontal wind vector magnitude from u and v components accurately. Applies parametric trigger cr...
- Interactive Python CLI and terminal plot view summarizing wind uncertainty distributions.
- 2 public reference cases
- 1 configured tool
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Ray Serve · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
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Recipe freeze marker
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