Science & acknowledgments

The science behind Catch a Rainbow.

Catch a Rainbow brings atmospheric optics, observer-centered geometry, live weather data, field observations, and ongoing testing together to help identify when rainbow conditions may be lining up.

Sunlight + droplets + observer geometry

How predictions work

How the app predicts rainbows.

What catch a rainbow checks together before it says conditions look promising

The prediction system combines rainbow geometry, current weather evidence, and ongoing testing.

The Big Picture

This page explains how Catch a Rainbow makes a prediction. The app does not simply look for rain and sunshine in a forecast. It checks rainbow geometry, compares several kinds of weather evidence, and looks for the specific conditions that would make a rainbow visible from your location. Direct sunlight, liquid water droplets, observer-specific geometry, timing, line of sight, and enough contrast against the background sky all have to line up.

Sun Angle and Rainbow Geometry

First, Catch a Rainbow calculates where the Sun is relative to you. A primary rainbow is centered on the antisolar point, the direction directly opposite the Sun, and the bow appears roughly 42 degrees from that point. The app uses your location and the Sun's angle to determine which part of the surrounding sky could contain a visible rainbow. For most everyday ground-level situations, lower Sun angles provide the usable geometry. Observer height and the local horizon can change what is visible.

Rain in the Right Direction

Catch a Rainbow looks for liquid precipitation in the specific directions where a rainbow could appear from your point of view. It compares directional weather data with radar evidence, distance, timing, and precipitation movement to judge whether useful droplets may be present in the rainbow zone. Radar is important evidence, but it is not treated as proof that the exact rainbow-producing droplets are there.

The Sunlight Path

Sunlight matters just as much as rain. Catch a Rainbow uses cloud layers, direct and diffuse solar radiation, visibility, cloud height, Sun position, and nearby terrain to estimate whether useful direct sunlight may be reaching the right part of the sky. These measurements are clues, not perfect proof. Clouds can contain small openings, and sunlight measured at the observer’s location does not always show whether a rain shaft several miles away is illuminated. A more detailed 3D sunlight-path model is being researched and validated separately.

Multiple Data Sources

Rather than relying on a single forecast or provider, Catch a Rainbow cross-checks multiple weather and environmental data sources. These can provide forecast guidance, precipitation evidence, cloud and sunlight information, airport weather observations, solar radiation measurements, and elevation or terrain context. Each source answers a different part of the rainbow question. When sources disagree or data is missing, that uncertainty stays separate instead of automatically being treated as good or bad rainbow conditions.

National Weather and Aviation Tools

For U.S. precipitation evidence, Catch a Rainbow uses radar information from the National Oceanic and Atmospheric Administration, usually called NOAA. NOAA is the United States science agency that studies weather, oceans, and the atmosphere.

One NOAA system under study is MRMS, short for Multi-Radar Multi-Sensor. MRMS combines information from many weather radars and other observations to estimate precipitation across the country. Catch a Rainbow currently uses NOAA radar information in production and is separately testing official MRMS precipitation-rate and precipitation-classification products as a possible improvement. That newer path does not yet affect rainbow alerts.

Catch a Rainbow also checks METAR airport weather reports used by pilots and meteorologists. They include real observations such as visibility, cloud height, wind, and current weather.

No single source can show the whole atmosphere perfectly. Catch a Rainbow compares different kinds of evidence, keeps disagreement and missing data visible as uncertainty, and asks whether the pieces support the physical conditions needed for a visible rainbow.

Testing Against Real Results

Catch a Rainbow is tested against real rainbow sightings, uploaded photos, sky reports, radar history, and times when the app gets conditions wrong. Those misses matter because they help show which part of the prediction needs to improve.

Weather is incredibly local. A small shower, a narrow break in the clouds, or sunlight reaching one neighborhood but not the next can make all the difference. Catch a Rainbow cannot promise that a rainbow will appear. The prediction is based on real physics, multiple sources of weather evidence, and ongoing testing.

Catch a Rainbow does not currently claim one validated accuracy percentage. The goal is to measure successful alerts, missed rainbows, false alerts, and uncertain cases as more real-world outcomes are collected.

Nature gets the final vote. Predictions are estimates. Catch a Rainbow must not replace official weather information or be used for safety, emergency, driving, or travel decisions.

Acknowledgments

Acknowledging pioneering work in rainbow forecasting

Catch a Rainbow follows in the footsteps of researchers and developers who have explored how atmospheric science can help people find rainbows.

Special acknowledgment goes to Dr. Steven Businger of the University of Hawaiʻi at Mānoa and the RainbowChase project, which helped pioneer the idea of combining an observer’s location, the position of the Sun, and weather radar to identify promising rainbow conditions.

Dr. Businger’s research on rainbow geometry, climatology, weather patterns, and practical rainbow chasing has contributed to the broader scientific foundation for understanding when and where rainbows occur.

The remarkable work of researchers has created a strong scientific foundation for understanding rainbows, atmospheric optics, and weather. Catch a Rainbow was developed independently, using that broader body of science to create its own prediction system around weather data, optical geometry, field observations, and ongoing validation against real rainbow events.