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Linked challenge: Build a Parametric Crop Drought Trigger Simulator and Basis Risk Auditor
Format
Code-aware
Lines
111
Sections
11
Linked challenge
Build a Parametric Crop Drought Trigger Simulator and Basis Risk Auditor
Prompt source
Original prompt text with formatting preserved for inspection.
111 lines
11 sections
No variables
1 code block
## Build
Simulate historical payouts for agricultural parametric drought contracts using ERA5 reanalysis precipitation time series. Audit contract basis risk against reported agricultural loss declaration logs.
## User
Parametric Product Structurer
## Workflow
1. Configure parametric contract terms specifying cumulative precipitation triggers and payout schedules.
2. Retrieve ERA5 historical total precipitation grids across multi-year growing cycles using cdsapi.
3. Execute synthetic trigger settlements over historic seasons to quantify false positive and false negative payouts.
4. Produce a basis risk audit dossier highlighting geographic contract trigger mismatches.
## Interface
Analytical workbench with interactive cumulative rainfall curves, trigger strike lines, and historical basis risk heatmaps.
## Data
Fetch ERA5 reanalysis datasets via cdsapi for target agricultural regions; cache normalized monthly rainfall totals in parquet files.
Starter examples below are synthetic. They are not downloaded records.
## Source
[Copernicus Climate Data Store client](https://raw.githubusercontent.com/ecmwf/cdsapi/master/README.rst)
Implements the cdsapi.Client retrieve method for 'reanalysis-era5-single-levels' selecting total_precipitation.
## Tools
evidently-ai, cdsapi, numpy, scipy.
Evaluates data drift and trigger misclassification distributions across distinct historical climate epochs.
Local or hosted LLM to interpret basis risk statistical metrics into actuarial design summaries.
## Deliverables
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.
## Acceptance
- Simulates annual parametric trigger payouts accurately based on ERA5 precipitation thresholds.
- Quantifies basis risk rates by identifying seasons with simulated drought declarations but no parametric breach.
- Generates an actionable contract recalibration report for human underwriting review.
## Starter Examples
```json
[
{
"input": {
"contract_id": "PARAM-DROUGHT-WHEAT-01",
"strike_threshold_mm": 120,
"seasons": [
{
"year": 2021,
"measured_precip_mm": 95,
"actual_loss_declared": true
},
{
"year": 2022,
"measured_precip_mm": 140,
"actual_loss_declared": false
},
{
"year": 2023,
"measured_precip_mm": 130,
"actual_loss_declared": true
}
]
},
"expected_output": {
"contract_id": "PARAM-DROUGHT-WHEAT-01",
"simulated_triggers": [
{
"year": 2021,
"triggered": true,
"basis_mismatch": false
},
{
"year": 2022,
"triggered": false,
"basis_mismatch": false
},
{
"year": 2023,
"triggered": false,
"basis_mismatch": true
}
],
"basis_risk_summary": {
"false_negative_rate": 0.5,
"false_positive_rate": 0,
"recalibration_advice": "Trigger threshold too restrictive; consider adjusting strike point to 135 mm."
}
}
},
{
"input": {
"contract_id": "PARAM-DROUGHT-SOY-02",
"strike_threshold_mm": 100,
"seasons": [
{
"year": 2023,
"measured_precip_mm": 80,
"actual_loss_declared": true
}
]
},
"expected_output": {
"contract_id": "PARAM-DROUGHT-SOY-02",
"simulated_triggers": [
{
"year": 2023,
"triggered": true,
"basis_mismatch": false
}
],
"basis_risk_summary": {
"false_negative_rate": 0,
"false_positive_rate": 0,
"recalibration_advice": "Current strike threshold exhibits zero basis mismatch on evaluated seasons."
}
}
}
]
```
## Safety
Calculates parametric scenario simulations only; no financial capital is allocated or contractual payout executed.
Use mock external writes. Require human review before any real deployment.Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.