The Weather Says Clear Skies. So Why Is the Trail a Swamp?

I love mountain biking. I love skateboarding. I love pretty much anything that involves wheels and dirt and concrete and the outdoors. What I don’t love is driving 30 minutes to a trailhead only to find the trail is a rutted, muddy mess, despite the forecast showing nothing but sunshine.

This is sadly not an isolated occurrence.

The Gap Between Weather and Reality

Weather apps are great at telling you what the weather is and often what it will be. Temperature, wind, humidity, chance of rain, it’s all there. What they don’t tell you is what last night’s rain did to the ground beneath your tires. That half inch at 2am? On a south-facing dirt trail with good drainage, it might be bone dry by noon. On a shaded clay singletrack in the woods? Come back Thursday. Or what about the concrete skatepark in direct sunlight vs. the shaded halfpipe layered in Skatelite? Not the same.

For years I did what every rider or skater does. I’d check the radar, look at the hourly history, maybe glance at the 48-hour precipitation total, and make a gut call. Sometimes I’d text a buddy: “You think Warner is rideable?” And they’d text back something equally scientific: “Probably?”

The variables are too fuzzy. How much did it rain? When did it stop? What’s the temperature now? Is the wind helping things dry? Is the trail in full sun or deep shade? Is the surface dirt, gravel, concrete, wood? Did it freeze overnight and is now thawing into a soupy mess? Each of these factors interacts with the others in ways that are genuinely hard to hold in your head all at once.

So I Built an App

I’m a developer, so naturally my response to this frustration was to write software. What started as a personal side project, just a quick tool to check if my local spots were rideable, turned into something much bigger once I realized how many people share this exact problem.

The app pulls real weather data, looks at recent precipitation, drying conditions, temperature trends, freeze-thaw cycles, surface type, sun exposure, all of it, and synthesizes it into a simple answer: Yes, Maybe, or No. It tells you whether conditions are likely suitable for riding right now, and gives you a seven-day outlook so you can plan ahead.

Building it forced me to formalize all of those gut-feel heuristics I’d been running in my head for years. How much rain is too much? How long does it take to dry? Does Ramp Armor dry faster than wood or metal? When does a freeze actually matter? Translating intuition into code is humbling, because it forces you to confront how much of your “expertise” is just vibes.

It’s Still an Inexact Science

Here’s the thing I want to be honest about: even with a carefully crafted and dedicated prediction engine running real weather data through a multi-phase algorithm, this is still an inexact science. Ground conditions are hyperlocal. Two trails a mile apart can behave completely differently based on soil composition, tree cover, elevation, and drainage. No amount of weather data can perfectly capture what’s happening at a specific spot on the earth’s surface.

The app gives you a well-informed estimate, and in my experience, it’s right far more often than my old gut-check method. But it’s not infallible, and I never want to pretend it is.

That honesty is actually baked into the product. Users can submit feedback when a verdict doesn’t match what they found on the ground. And behind the scenes, I use an LLM to process those reports, analyzing the weather conditions, the surface type, the user’s description of what they actually encountered, and then use that to identify patterns where the engine’s assumptions might be off. It’s a feedback loop that lets the prediction model evolve based on real-world ground truth from actual riders at actual spots.

I think that’s what makes this problem so interesting. It sits right at the intersection of atmospheric data, soil science, and lived experience. No model will ever be perfect, but a model that learns from the people using it every day can keep getting better.

What’s Next

In a future post, I plan to pull back the curtain on the prediction engine itself, how it evaluates moisture, what the drying model looks like, how freeze-thaw cycles are handled, and why certain surfaces behave so differently from others. It’s a surprisingly deep rabbit hole, and I think anyone who’s ever stared at a weather app trying to decide “is it rideable?” will find it interesting.

Until then, check the app, trust the verdict, and if it’s wrong, tell me. That’s how we make it better.

Ridewise is available now on the App Store, built by Stalefish Labs.