How Hail Strike Uses Weather Data: Inside Our Proprietary Data Pipeline

From Raw Weather Data to Actionable Hail Intelligence

When a severe thunderstorm rolls across a metropolitan area, it generates an enormous volume of weather data. Radar stations pulse microwave energy into the atmosphere every few seconds. Satellites capture images in multiple spectral bands every minute. Surface stations record temperature, wind, and precipitation readings. Storm spotters file hail reports. Social media lights up with photos of hailstones.

The challenge is not a lack of data -- it is transforming that raw, disparate information into something a roofing contractor, insurance adjuster, or homeowner can actually use. That is exactly what Hail Strike's proprietary data pipeline was built to do.

In this article, we pull back the curtain on how Hail Strike processes weather data, from initial ingestion through quality control, hail sizing, swath mapping, and ultimately the generation of property-level StormClaim Scores. Understanding this pipeline helps our users appreciate the confidence and precision behind every data point they see on the platform.

Data Ingestion: Casting a Wide Net

NEXRAD Radar: The Primary Source

The foundation of our data pipeline is NEXRAD dual-polarization radar. Hail Strike ingests Level II base data from all 160 NEXRAD stations across the continental United States, Hawaii, and Alaska. This raw data includes:

• Reflectivity (Z): Intensity of the radar return, correlating with precipitation size and concentration.
• Differential Reflectivity (ZDR): Particle shape information, critical for distinguishing hail from rain.
• Correlation Coefficient (CC): Signal consistency, which drops in the presence of mixed-phase precipitation like hail.
• Specific Differential Phase (KDP): Differential phase shift, useful for rain rate estimation and hail discrimination.
• Radial Velocity (V): Wind speed toward or away from the radar, indicating storm rotation and updraft strength.
• Spectrum Width (SW): Variability in radial velocity, correlating with turbulence and wind shear.

We receive this data via NOAA's real-time data distribution feeds, with typical latency of 1-3 minutes from the time the radar completes a volume scan.

Satellite Imagery

Hail Strike integrates imagery from the GOES-16 and GOES-18 geostationary satellites, which provide continuous coverage of the continental U.S. in 16 spectral bands. Key products we use include:

• Visible and infrared imagery for storm structure analysis and overshooting top detection.
• Day/night microphysics composites for identifying ice-phase cloud tops associated with hail-producing storms.
• Mesoscale sector scans at 1-minute temporal resolution during severe weather events.

After a storm passes, we also ingest high-resolution satellite and aerial imagery for direct damage assessment, which we cover in a separate article.

Surface Observations and Storm Reports

To calibrate and validate our radar-derived products, we ingest:

• NWS Local Storm Reports (LSRs): Trained spotter observations of hail size, timing, and location.
• ASOS/AWOS stations: Automated surface weather observations including hail occurrence flags.
• mPING crowdsourced reports: Public reports of hail from the NOAA Meteorological Phenomena Identification Near the Ground program.
• Commercial hail sensor networks: Proprietary ground-based sensors that measure hail kinetic energy.

Property and Parcel Data

Weather data alone does not answer the question contractors and adjusters care about most: which specific properties were affected? Hail Strike maintains a continuously updated database of property records, including:

• Parcel boundaries and addresses
• Roof type, material, and age
• Building footprint geometry
• Historical claim data where available

This property layer is essential for translating weather data into property-level intelligence.

Data Processing: The Pipeline in Action

Stage 1: Quality Control

Raw radar data contains numerous artifacts that must be removed before analysis:

• Ground clutter: Reflections from buildings, terrain, and other stationary objects near the radar.
• Anomalous propagation (AP): Caused by temperature inversions that bend the radar beam toward the surface.
• Velocity dealiasing: Correcting for the Nyquist velocity limitation when winds exceed the radar's maximum unambiguous velocity.
• Range folding: Removing echoes from beyond the radar's unambiguous range that wrap into closer ranges.

Our QC algorithms use a combination of texture analysis, dual-pol signatures, and spatial consistency checks to identify and remove these artifacts. This step is critical -- ground clutter can produce reflectivity values exceeding 60 dBZ, mimicking severe hail signatures.

Stage 2: Multi-Radar Mosaicking

Individual NEXRAD stations have coverage gaps, especially at low altitudes in mountainous terrain and in areas between stations. Hail Strike merges data from overlapping radars into a seamless national mosaic using:

• Distance-weighted blending in overlap zones to smoothly combine data from multiple stations.
• Vertical interpolation to create consistent altitude slices across the merged domain.
• Temporal synchronization to align volume scans that start and end at slightly different times.

The resulting mosaic provides uniform, high-quality coverage that no single radar station can achieve alone.

Want to see Hail Strike's data pipeline in action for your service area? Create your free account and explore real-time hail swath maps, property-level damage scores, and storm history for any address in the U.S.

Stage 3: Hail Detection and Sizing

With clean, mosaicked data in hand, our pipeline runs Hail Strike's proprietary hail detection and sizing algorithms. These go beyond the standard MESH (Maximum Estimated Size of Hail) approach used by the National Weather Service:

1. Dual-pol hail classification: Using ZDR, CC, and KDP alongside reflectivity, we classify each radar sample volume as hail, rain, mixed-phase, or other hydrometeor type.
2. ML-enhanced hail sizing: A gradient-boosted ensemble model trained on over 5 million paired radar-observation records estimates surface hail diameter with 15-30% greater accuracy than standard MESH.
3. Hail probability mapping: For each grid cell, we calculate the probability that hail reached the surface and the probability that hail exceeded 1.0-inch and 2.0-inch thresholds.
4. Uncertainty quantification: Every hail size estimate includes a confidence interval, giving users a realistic range rather than a false sense of precision.

Our AI and machine learning models are retrained quarterly as new ground-truth data accumulates, ensuring continuous improvement.

Stage 4: Swath Construction and Tracking

Hailstorms are not stationary -- they move, often at 30-60 mph. Our pipeline tracks hail cores from scan to scan, constructing hail swath maps that show the geographic footprint of damaging hail as it moved across the landscape. Key steps include:

• Storm cell identification and tracking using reflectivity centroid and velocity data.
• Temporal interpolation between volume scans (every 4-6 minutes) to fill gaps in the swath.
• Surface projection to map the radar-estimated hail from its atmospheric observation height to the ground level, accounting for wind drift and melting.

The resulting swath maps display the maximum estimated hail size at each location, with spatial resolution of approximately 250 meters.

Stage 5: Property-Level Attribution

The final processing stage intersects hail swath maps with property parcel data to generate per-address hail impact assessments. For each property in an affected area, the pipeline determines:

• Maximum estimated hail size at the property location
• Duration of hail exposure
• Estimated hail kinetic energy (a function of size, density, and terminal velocity)
• Wind speed and direction during hail (which affects the angle of impact on roof surfaces)

This property-level attribution is what makes Hail Strike uniquely valuable compared to raw radar data or simple hail swath maps. It answers the specific question: "Was this property hit by damaging hail, and how severe was it?"

Data Delivery: Getting Intelligence to Users

Real-Time Storm Alerts

When our pipeline detects significant hail, subscribers receive real-time alerts via push notification, email, and SMS. Alerts include the estimated hail size, affected area, and a direct link to the interactive swath map on the Hail Strike platform.

Interactive Swath Maps

Our web platform and mobile app display hail swath maps overlaid on street-level mapping, allowing users to zoom from regional overview down to individual properties. Color coding indicates hail severity, and clicking any property reveals its detailed hail impact assessment.

StormClaim Scores

For each affected property, Hail Strike generates a StormClaim Score -- a composite metric that combines radar-derived hail data with roof vulnerability factors, satellite imagery analysis, and historical claim patterns. This score gives contractors and adjusters a pre-inspection estimate of damage likelihood and severity.

API Access

For enterprise customers and technology partners, Hail Strike offers RESTful API access to all pipeline outputs, enabling integration with CRM systems, claims management platforms, and custom analytics tools.

Accuracy and Validation

We take validation seriously. Hail Strike maintains an ongoing validation program that compares our pipeline outputs against:

• NWS Local Storm Reports from trained weather spotters.
• Insurance claim data from carrier partners (anonymized and aggregated).
• Post-storm ground surveys conducted by our field verification team.
• Roof inspection results from roofing contractor partners.

Our most recent validation study, covering the 2025 storm season, found that Hail Strike's hail size estimates correlated with ground-truth measurements at r = 0.87, compared to r = 0.72 for standard MESH. Property-level damage likelihood predictions achieved an AUC of 0.91, meaning the platform correctly ranks higher-damage properties above lower-damage ones 91% of the time.

Why a Multi-Source Approach Matters

No single data source is perfect. Radar has range and resolution limitations. Storm reports are sparse and geographically biased. Satellite imagery can be obscured by cloud cover. By fusing multiple independent data sources, Hail Strike's pipeline achieves robustness that no individual source can provide.

When radar suggests hail fell in an area, storm reports confirm it on the ground, and satellite imagery shows consistent roof-level impacts, the confidence in our assessment is high. When sources disagree, our algorithms flag the discrepancy and adjust confidence intervals accordingly.

This multi-source philosophy is central to Hail Strike's mission: delivering trustworthy, defensible hail damage intelligence that professionals can rely on for insurance claims, contractor operations, and business decisions.

Conclusion

Hail Strike's data pipeline transforms the overwhelming volume of raw weather data generated by every storm into precise, property-level hail damage intelligence. From NEXRAD radar ingestion through quality control, multi-radar mosaicking, ML-enhanced hail sizing, swath construction, and property attribution, every stage of the pipeline is designed for accuracy, speed, and actionability.

The result is a platform that does not just tell you a storm happened -- it tells you exactly which properties were impacted, how severely, and with what level of confidence. That is the difference between raw weather data and true hail damage intelligence.

Ready to experience Hail Strike's data pipeline for yourself? Sign up today and get instant access to hail swath maps, property-level damage scores, and real-time storm alerts for your service area.