Most weather apps aren't built for cyclists — especially when your ride takes you from downtown into the foothills or deep into the mountains. A forecast for Denver or Boulder doesn't tell the whole story just a few miles away, and that's where my tools come in.
I'm building weather tools designed to get straight to the point. You don't have to plan a full route just to check if an area looks good — you can quickly see if where you want to ride has favorable weather. If you do have a route in mind, you can upload it to get a custom forecast based on your unique riding thresholds, like temperature, wind, or rain preferences.
Because weather changes fast, especially during long rides, real-time alerting will give you peace of mind. If conditions start turning bad, you'll get a simple, useful text message — not confusing radar images — so you can adjust quickly and stay safe while logging big miles.
The interactive maps use high-resolution data, letting you zoom in and get localized forecasts tailored to exactly where you're headed — not just a generic city forecast.
The goal is simple: to create cycling weather tools that are fast, accurate, and easy to use, so you can spend less time worrying and more time riding.
Most popular weather apps — like Apple Weather or The Weather Channel — are built around black-box AI systems. They're designed by machine learning engineers who focus on squeezing out small improvements in accuracy scores, like predicting a high temperature one or two degrees closer to reality.
But when it comes to the weather that actually matters — thunderstorms, snow squalls, high winds — these apps often fail. That's because their systems don't truly understand the atmosphere; they're just chasing better statistics.
I do things differently. I know exactly which forecast models are used for each weather parameter. At NOAA, there are many specialized models, each designed for different situations — thunderstorms, snowstorms, wind events, and more. I've selected the best model for each parameter, based on its strengths.
These models aren't random guesses. They were developed by some of the top meteorologist data scientists in the country, and are used every day by professional forecasters — from local National Weather Service offices to national centers like the SPC (Storm Prediction Center).
For example, my precipitation forecasts and thunderstorm probabilities come directly from the HREF, a high-resolution ensemble model built specifically for severe weather forecasting. It's the same data that supports severe weather alerts issued by the SPC.
The live conditions map uses NOAA's best gridded real-time dataset, combined with the latest radar data. It also taps into every available weather sensor in the region to constantly improve the data, giving you a much more accurate and reliable view of what’s happening right now.
In short: instead of relying on black-box AI trying to guess what might happen, I use the best specialized tools available — the same tools trusted by meteorologists who forecast for a living.
