Build an ERA5 Drought Soil Moisture Parametric Simulator
Simulate parametric multi-layer soil moisture triggers for rainfed pastureland using ECMWF ERA5 reanalysis datasets. Model index basis risk and calculate payout frequencies under historical drought conditions.
What you are building
The core problem, expected build, and operating context for this challenge.
Simulate parametric multi-layer soil moisture triggers for rainfed pastureland using ECMWF ERA5 reanalysis datasets. Model index basis risk and calculate payout frequencies under historical drought conditions.
Agricultural Actuarial Modeler
Generate CDS API requests for volumetric soil water layer parameters across multiple agricultural seasons.
Ingest daily soil moisture time series and compute cumulative dry-anomaly duration indexes.
Model synthetic parametric trigger mechanics against historical regional crop yield losses.
Quantify trigger false positives and false negatives to evaluate contract basis risk.
JupyterLab dashboard and CLI exporting synthetic payout curves and trigger diagnostic tables.
Use ECMWF CDS API client sandbox with cached volumetric soil water reanalysis test records. Starter examples below are synthetic. They are not downloaded records.
Copernicus Climate Data Store client Structure cdsapi requests specifying reanalysis datasets, dates, and volumetric soil variables.
optuna, cdsapi, numpy, pandas. Optuna optimizes parametric trigger thresholds to minimize basis risk against loss indicators. Local Python script with LLM assistance for actuarial summary documentation.
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.
CDS API script contains valid variable and date range parameters.
Calculates cumulative soil moisture deficit across consecutive dry days.
Outputs optimized trigger threshold and simulated historical payout frequency.
```json [ { "input": { "zone_id": "KANSAS_ZONE_4", "target_moisture_m3_m3": 0.15, "consecutive_days_trigger": 14, "daily_soil_moisture": [ 0.18, 0.14, 0.13, 0.12, 0.11, 0.1, 0.12, 0.11, 0.13, 0.12, 0.11, 0.1, 0.09, 0.08, 0.07, 0.16 ] }, "expected_output": { "zone_id": "KANSAS_ZONE_4", "max_consecutive_dry_days": 14, "trigger_activated": true, "simulated_payout_rate": 1, "basis_risk_score": 0.12 } }, { "input": { "zone_id": "KANSAS_ZONE_5", "target_moisture_m3_m3": 0.15, "consecutive_days_trigger": 14, "daily_soil_moisture": [ 0.22, 0.2, 0.18, 0.17, 0.16, 0.15, 0.14, 0.16, 0.17, 0.18 ] }, "expected_output": { "zone_id": "KANSAS_ZONE_5", "max_consecutive_dry_days": 1, "trigger_activated": false, "simulated_payout_rate": 0, "basis_risk_score": 0.04 } } ] ```
Simulations are academic and actuarial models, not authorized insurance contract commitments. Use mock external writes. Require human review before any real deployment.
Participation guide
Simulations are academic and actuarial models, not authorized insurance contract commitments. Use mock external writes and human review.
How work is evaluated
CDS API script contains valid variable and date range parameters. Calculates cumulative soil moisture deficit across consecutive dry days. Outputs optimized trigger threshold and simulated historical payout frequency.
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
Generate CDS API requests for volumetric soil water layer parameters across multiple agricultural seasons. Ingest daily soil moisture time series and compute cumulative dry-anomaly duration indexes. Model synthetic parametric trigger mechanics against historical regional crop yield losses. Quantify trigger false positives and false negatives to evaluate contract basis risk.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
CDS API script contains valid variable and date range parameters. Calculates cumulative soil moisture deficit across consecutive dry days. Outputs optimized trigger threshold and simulated historical payout frequency.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
JupyterLab dashboard and CLI exporting synthetic payout curves and trigger diagnostic tables.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Simulations are academic and actuarial models, not authorized insurance contract commitments.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Generate CDS API requests for volumetric soil water layer parameters across multiple agricultural seasons.
Ingest daily soil moisture time series and compute cumulative dry-anomaly duration indexes.
Model synthetic parametric trigger mechanics against historical regional crop yield losses.
Quantify trigger false positives and false negatives to evaluate contract basis risk.
Reference links and supporting material
Use ECMWF CDS API client sandbox with cached volumetric soil water reanalysis test records. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
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Optuna
Hyperparameter Optimization
Evaluated output
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- Generate CDS API requests for volumetric soil water layer parameters across multiple agricultural seasons....
- CDS API script contains valid variable and date range parameters. Calculates cumulative soil moisture defic...
- JupyterLab dashboard and CLI exporting synthetic payout curves and trigger diagnostic tables.
- 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
- Optuna · 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
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