Weather Perils
Weather Parametric Insurance can cover a wide range of perils, including but not limited to:
- Precipitation: This peril covers excessive rainfall, droughts, and other precipitation-related events. Indices used for precipitation can include rainfall levels, rainfall duration, or accumulated precipitation over a specific period.
- Temperature: Temperature-related perils encompass extreme heatwaves, cold snaps, or temperature anomalies. Indices for temperature can include average temperature, maximum or minimum temperature thresholds, or degree days.
- Wind: Wind-related perils cover events such as hurricanes, tornadoes, or strong gusts. Indices for wind can include wind speed thresholds, wind direction, or accumulated wind energy.
- Snow: Snow-related perils protect against heavy snowfall, blizzards, or snowstorms. Indices for snow can include snow depth, snowfall intensity, or snow cover duration.
Weather & Climate Data Sources
Riskwolf uses a variety of high-quality weather data sources to ensure accurate parametric insurance triggers and payouts. For complete technical documentation on each data source, please visit our dedicated reference pages:
- NOAA GHCN - Global Historical Climatology Network daily data
- NOAA IBTrACS - International Best Track Archive for Climate Stewardship
- IMD Gridded Rainfall - India Meteorological Department Gridded Rainfall Data Set
- IMD Gridded Temperature - India Meteorological Department Gridded Temperature Data
- ECMWF ERA5 Land - Hourly Reanalysis Dataset from European Centre for Medium-Range Weather Forecasts
- ECMWF IFS Forecast - Integrated Forecast System
- CHC CHIRPS - Climate Hazards Group InfraRed Precipitation with Station data
- USGS EHP - United States Geological Survey Earthquake Hazards Program
- Copernicus GDACS - Global Disaster Alert and Coordination System
For an overview of all data sources and their applications, see our Data Sources Overview.
See also our Common Weather Indices Reference for detailed information on how these data sources are used to create weather indices for parametric insurance products.
Weather Indices
Riskwolf's parametric insurance products utilize a variety of specialized weather indices tailored to specific agricultural needs. Each index is carefully calibrated to correlate with crop yield impacts and financial losses. For detailed technical specifications of all indices, see our Common Weather Indices Reference.
Below are key indices used in cropINSURE solutions with real-world application scenarios:
1. Aggregate of rainfall over respective phases
Description: Measures the total accumulated rainfall during a defined period, ideal for addressing seasonal drought or excess rainfall risks.
Example Scenario: A sugarcane farmer in Solapur, Maharashtra insures the monsoon growth phase (June–September). If total rainfall is below 600 mm, the coverage triggers. For instance, if only 420 mm is received, the farmer receives up to ₹60,000 to offset poor cane growth.
2. Highest of 4 consecutive days cumulative rainfall in respective phases
Description: Identifies short-term intense rainfall events that can damage crops through flooding, waterlogging, or physical damage.
Example Scenario: An orange grower in Nagpur sets coverage for fruit development in August. If the highest 4-day cumulative rainfall exceeds 150 mm, say at 160 mm, the farmer receives a ₹13,000 payout for delayed coloring and fruit rot.
3. Number of days in a spell of consecutive dry day (< 2.5 mm rainfall per day)
Description: Tracks extended dry periods that can stress plants and reduce yields, particularly during critical growth phases.
Example Scenario: A tea farmer in Assam insures for the monsoon flush (July–September). If there is a 14-day dry spell, exceeding the 10-day trigger, the payout of ₹40,000 is issued to mitigate loss of flush quality.
4. Sum of upward deviation of daily Max. Temperature and downward deviation of daily Min. Temperature
Description: Captures temperature volatility and extremes that can stress plants through both hot days and cold nights.
Example Scenario: An apple orchard in Kashmir insures against temperature stress in March. If the cumulative deviation exceeds 5°C due to cold nights and hot days, the farmer receives a ₹6,400 payout for reduced flowering and fruit set.
5. Count of Rainy days
Description: Monitors the frequency of rainfall events above a defined threshold, useful for crops sensitive to persistent wet conditions.
Example Scenario: A coffee grower in Tamil Nadu covers the monsoon months (June–September). If more than 10 days have >25 mm rainfall, the policy activates. A count of 15 such days leads to a payout of ₹20,000.
6. Cumulative upward deviation of daily Maximum wind speed from the trigger
Description: Measures excess wind conditions that can physically damage crops, cause lodging, or increase evapotranspiration.
Example Scenario: A mango grower in Visakhapatnam monitors wind during April–May. If wind speed deviations exceed 25 km/h over multiple days, the payout reaches up to ₹1,150 per tree to cover fallen fruits and tree damage.
7. Sum of deviations in daily minimum temperature below the trigger
Description: Identifies cold stress periods that can damage temperature-sensitive crops, particularly during flowering or fruiting.
Example Scenario: A cashew farmer in Tamil Nadu insures the flowering season (Dec–Feb). If minimum temperatures fall below 20°C for over 10 days, they receive ₹20,000 to address loss of bloom and nut formation.
8. Cumulative deviations of mean temperature more than 3°C from trigger
Description: Tracks persistent temperature anomalies that can affect crop development rates and quality.
Example Scenario: An organic cotton farmer in Maharashtra activates this index during August–September. If the mean temperature deviation exceeds 3°C, it causes flower drop and the farmer receives a ₹15,000 payout.
9. Cumulative daily upward deviation of Max temperature from respective fortnightly trigger
Description: Monitors heat stress conditions relative to expected seasonal norms during specific growth phases.
Example Scenario: A mango orchard in Telangana sets coverage for February flowering. If daily maximum temperatures consistently exceed fortnight norms, payouts of ₹19 per km/h equivalent deviation compensate for increased pest risks.
10. Consecutive days with daily rainfall > threshold
Description: Identifies prolonged wet periods that can prevent field operations, increase disease pressure, or cause waterlogging.
Example Scenario: A banana farmer in Jalgaon, Maharashtra insures for heavy spells during July–September. If 3 or more days in a row each exceed 70 mm rainfall, the index triggers a payout of up to ₹35,000.
11. Cumulative daily downward deviation of Minimum Temperature from fortnightly trigger
Description: Tracks cold stress relative to seasonal expectations, useful for temperature-sensitive development phases.
Example Scenario: A grape grower in Nashik covers Nov–Jan berry formation phase. If minimum temperatures fall below the 15°C trigger across multiple nights, a payout protects against cold stress and delayed ripening.
12. Count of days having daily Minimum temperature greater than 21°C
Description: Monitors high nighttime temperatures that can increase respiration and reduce yield or quality in certain crops.
Example Scenario: A tea grower in the Nilgiris monitors Feb–April first flush. If minimum temperatures exceed 21°C on more than 7 days, affecting leaf quality and fungal risk, a ₹40,000 payout is issued.
13. Cumulative daily upward deviation of T Max from threshold
Description: Measures accumulated heat stress that can affect pollination, fruit development, or increase water requirements.
Example Scenario: A coffee farmer in Periyakulam monitors Aug–Sep period. If Tmax consistently exceeds 36°C and the cumulative upward deviation grows beyond the 10-day exit threshold, the ₹10,000 payout applies.