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Challenge

Build an ERA5 Wildfire Atmospheric Drying Index Assessor

Model severe wildfire weather potential by querying ERA5 temperature and relative humidity pressure level grids. Detect persistent atmospheric drying windows to quantify parametric forest protection trigger uncertainty.

Data ScienceHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Model severe wildfire weather potential by querying ERA5 temperature and relative humidity pressure level grids. Detect persistent atmospheric drying windows to quantify parametric forest protection trigger uncertainty.

Wildfire Parametric Underwriter

Formulate cdsapi retrieve parameters for ERA5 atmospheric temperature and relative humidity.

Ingest simulated pressure level grids and calculate Vapor Pressure Deficit (VPD) metrics.

Track cumulative days with extreme drying anomalies exceeding regional wildfire danger thresholds.

Evaluate trigger sensitivity and basis risk against synthetic timberland fire damage inventories.

Streamlit analytical app plotting time-series VPD curves, reanalysis grids, and trigger activations.

Execute cdsapi test retrieval scripts configured with simulated local NetCDF or GRIB files. Starter examples below are synthetic. They are not downloaded records.

Copernicus Climate Data Store client Specify ERA5 pressure-levels dataset parameters in cdsapi retrieve calls per README.

mlflow, cdsapi, xarray, scipy. MLflow tracks parametric model experiments, trigger threshold parameters, and basis risk metrics. Local computation environment with API access to hosted LLM for model explanation.

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.

Constructs CDS API calls with pressure level and product type fields.

Calculates Vapor Pressure Deficit within 0.1 kPa numerical precision.

Tracks parametric index simulation metrics and run parameters in MLflow.

```json [ { "input": { "forest_tract_id": "TIMBER-OR-7", "vpd_threshold_kpa": 2.8, "min_consecutive_days": 5, "daily_vpd_series": [ 1.5, 2.9, 3.1, 3, 3.2, 3.4, 2.1, 1.8 ] }, "expected_output": { "forest_tract_id": "TIMBER-OR-7", "max_drying_streak_days": 5, "parametric_trigger_fired": true, "payout_tier_percentage": 75, "experiment_logged": true } }, { "input": { "forest_tract_id": "TIMBER-WA-3", "vpd_threshold_kpa": 2.8, "min_consecutive_days": 5, "daily_vpd_series": [ 2.9, 3, 2.2, 3.1, 3.2, 2.5 ] }, "expected_output": { "forest_tract_id": "TIMBER-WA-3", "max_drying_streak_days": 2, "parametric_trigger_fired": false, "payout_tier_percentage": 0, "experiment_logged": true } } ] ```

Index assessments serve only as actuarial simulations without initiating financial capital movements. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Index assessments serve only as actuarial simulations without initiating financial capital movements. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Constructs CDS API calls with pressure level and product type fields. Calculates Vapor Pressure Deficit within 0.1 kPa numerical precision. Tracks parametric index simulation metrics and run parameters in MLflow.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Formulate cdsapi retrieve parameters for ERA5 atmospheric temperature and relative humidity. Ingest simulated pressure level grids and calculate Vapor Pressure Deficit (VPD) metrics. Track cumulative days with extreme drying anomalies exceeding regional wildfire danger thresholds. Evaluate trigger sensitivity and basis risk against synthetic timberland fire damage inventories.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Constructs CDS API calls with pressure level and product type fields. Calculates Vapor Pressure Deficit within 0.1 kPa numerical precision. Tracks parametric index simulation metrics and run parameters in MLflow.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Streamlit analytical app plotting time-series VPD curves, reanalysis grids, and trigger activations.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Index assessments serve only as actuarial simulations without initiating financial capital movements.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Formulate cdsapi retrieve parameters for ERA5 atmospheric temperature and relative humidity.

  • Ingest simulated pressure level grids and calculate Vapor Pressure Deficit (VPD) metrics.

  • Track cumulative days with extreme drying anomalies exceeding regional wildfire danger thresholds.

  • Evaluate trigger sensitivity and basis risk against synthetic timberland fire damage inventories.

Resources and assets

Reference links and supporting material

Dataset notes

Execute cdsapi test retrieval scripts configured with simulated local NetCDF or GRIB files. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

MLflow

MLOps

Evaluated output

No output checks are configured.

Evaluation checks
  • Formulate cdsapi retrieve parameters for ERA5 atmospheric temperature and relative humidity. Ingest simulat...
  • Constructs CDS API calls with pressure level and product type fields. Calculates Vapor Pressure Deficit wit...
  • Streamlit analytical app plotting time-series VPD curves, reanalysis grids, and trigger activations.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 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.

View technical recipe

Configured tools

Action Space
  • MLflow · 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

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build an ERA5 Wildfire Atmospheric Drying Index Assessor