Build a Parametric Crop Drought Trigger Simulator and Basis Risk Auditor

Simulate historical payouts for agricultural parametric drought contracts using ERA5 reanalysis precipitation time series. Audit contract basis risk against reported agricultural loss declaration logs.

Parametric Product Structurer

Configure parametric contract terms specifying cumulative precipitation triggers and payout schedules.

Retrieve ERA5 historical total precipitation grids across multi-year growing cycles using cdsapi.

Execute synthetic trigger settlements over historic seasons to quantify false positive and false negative payouts.

Produce a basis risk audit dossier highlighting geographic contract trigger mismatches.

Analytical workbench with interactive cumulative rainfall curves, trigger strike lines, and historical basis risk heatmaps.

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.

Copernicus Climate Data Store client Implements the cdsapi.Client retrieve method for 'reanalysis-era5-single-levels' selecting total_precipitation.

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.

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.

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.

```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." } } } ] ```

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.