Zdeněk Pracný
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January 23, 2026

TrailMeteo: Hyperlocal Weather Intelligence for the Czech Outdoors

Building a specialized weather platform that combines official ČHMÚ Aladin model data with multiple international forecast sources to provide hyperlocal weather intelligence for outdoor enthusiasts in the Czech Republic.

The Problem

As a bikepacker and outdoor enthusiast in the Czech Republic, I've always struggled with weather forecasting. Generic weather apps give you city-level data, but when you're planning a multi-day bikepacking trip through varying terrain, you need to know: What's the temperature difference between valley and ridge? Will it rain in this specific area? What do different weather models predict?

Most importantly – I wanted access to the Czech Hydrometeorological Institute's (ČHMÚ) Aladin model, which is specifically tuned for Central European weather patterns but isn't easily accessible to the public in a user-friendly format.

The Solution: TrailMeteo

TrailMeteo was born from this need. It's a hyperlocal weather platform focused on the Příbram district (my home area), combining multiple data sources to give outdoor enthusiasts the most accurate weather intelligence possible.

Core Features

Interactive Weather Map

The centerpiece is an OpenStreetMap-based interface showing real-time temperature data from numerous weather stations across the region. Each marker displays current conditions, and the map updates automatically throughout the day.

What makes this powerful:

  • Instant visualization of temperature variations across the landscape
  • Identification of microclimates and local weather patterns
  • Quick comparison between valley bottoms and elevated areas

District Overview

A comprehensive dashboard showing:

  • Current conditions at all monitored locations
  • Temperature extremes (coldest/warmest spots)
  • Wind speeds across the district
  • Precipitation data
  • Historical temperature trends (72-hour graphs)

This overview helped me understand not just what the weather is, but how it's changing and varying across the region.

10-Day Forecast with ČHMÚ Aladin Integration

The forecast page is where the magic happens:

  • Hour-by-hour predictions for the next 24 hours
  • 10-day overview with detailed daily breakdowns
  • Toggle between Open-Meteo and ČHMÚ Aladin models
  • Temperature, precipitation, wind, and feel-like data

Having access to the Aladin model alongside Open-Meteo provides crucial validation – when models agree, confidence is high. When they diverge, I know to plan more conservatively.

Multi-Model Comparison

This is my favorite feature for serious trip planning. A side-by-side matrix comparing:

  • Best Match (ensemble average)
  • ECMWF (European model)
  • GFS (American model)
  • ICON (German model)
  • ČHMÚ Aladin (Czech national model)

Seeing five different models at once reveals forecast uncertainty. If all models agree it'll be sunny, I'm confident. If predictions vary wildly, I pack for all conditions.

35-Day Ensemble Forecast

For long-range planning, the ensemble view shows:

  • Median prediction (p50)
  • Confidence intervals (p10, p25, p75, p90)
  • Temperature and precipitation trends

The percentile bands visualize uncertainty beautifully. A narrow band means high confidence; a wide spread means the weather could go multiple directions.

Weather Notifications

Push notifications for:

  • Daily morning briefings (7 AM)
  • Severe weather alerts (extreme temperatures, heavy precipitation, strong winds)
  • Temperature inversions (important for planning mountain activities)

Technical Architecture

The Stack

  • Frontend: Next.js with React for a fast, responsive UI
  • Maps: OpenStreetMap with custom tile servers and Leaflet for interactivity
  • Database: Firebase/Firestore for real-time data storage
  • Hosting: Vercel for the frontend
  • Data Pipeline: Docker container running on a personal server

The Data Pipeline

This is where it gets interesting. The ČHMÚ Aladin data isn't available via a public API, so I built a custom scraper:

Docker Container Architecture:

Docker Container (Personal Server)
    ↓
Python Scraper
    ↓
ČHMÚ Aladin Model Data
    ↓
Data Transformation & Validation
    ↓
Firebase/Firestore
    ↓
Next.js Frontend (Vercel)

The scraper runs on a schedule, pulling the latest Aladin model runs, transforming the data into a usable format, and pushing it to Firebase. The frontend queries Firebase in real-time, blending Aladin data with Open-Meteo API responses.

Key Challenges

1. ČHMÚ Data Access The biggest challenge was accessing Aladin model data. ČHMÚ doesn't provide a clean API, so I had to:

  • Analyze their website's structure
  • Identify data endpoints
  • Build a resilient scraper that handles format changes
  • Validate data integrity before storage

2. Data Synchronization Keeping multiple data sources synchronized required:

  • Timestamp normalization across different APIs
  • Handling missing data gracefully
  • Implementing retry logic for failed fetches
  • Caching strategies to reduce API calls

3. Real-time Updates Weather data becomes stale quickly. Firebase's real-time capabilities ensure the frontend always shows the latest data without constant polling.

4. Model Comparison Visualization Displaying five different models clearly without overwhelming users took multiple iterations. The final tabular approach with icons and temperature ranges provides quick scanning while preserving detail.

Lessons Learned

Start with Local, Expand Later

I focused on the Příbram district first. This narrow scope allowed me to:

  • Perfect the data pipeline
  • Optimize performance
  • Understand local weather patterns deeply
  • Build features that actually matter

Expansion to other regions is now straightforward because the foundation is solid.

Multiple Models > Single Source

No single weather model is perfect. Combining multiple sources and showing their agreement/disagreement is far more valuable than presenting one model's prediction as truth.

Historical Context Matters

The 72-hour temperature history graph provides context that standalone forecasts lack. Seeing "it'll be -9°C tomorrow" hits differently when you know it's been -3°C for the past day.

Performance is Critical

Weather apps compete with giants like Weather.com. If your site is slow, users won't return. Optimizations that made the biggest difference:

  • Image optimization for weather icons
  • Lazy loading for charts and graphs
  • Efficient Firebase queries
  • CDN usage for static assets

Docker Simplifies Deployment

Running the scraper in Docker means:

  • Consistent environment across development and production
  • Easy updates and rollbacks
  • Resource isolation
  • Simple orchestration with docker-compose

Real-World Impact

TrailMeteo has become my go-to tool before every outdoor adventure. Examples:

Bikepacking Trip Planning: I check the multi-model comparison for the duration of my trip. If all models predict clear weather, I pack light. If there's disagreement, I bring rain gear.

Microclimate Awareness: The district map revealed temperature differences I never knew existed. Certain valleys are consistently 3-4°C colder than surrounding areas – crucial for planning early morning starts.

Inversion Alerts: Temperature inversions create fascinating conditions (warm mountains, cold valleys). Notifications let me capture these moments for photography or plan hikes to escape valley fog.

What's Next

Immediate Roadmap

  • Expand coverage to neighboring districts
  • Add historical weather archives for pattern analysis
  • Integrate more data points (humidity, pressure, UV index)
  • Mobile app for offline access

Long-term Vision

  • Machine learning to create custom ensemble models
  • Integration with trail conditions (snow depth, trail status)
  • Community-contributed observations
  • API for other outdoor platforms to consume

Technical Takeaways for Fellow Developers

Web Scraping Done Right

  • Always have a fallback data source
  • Implement rate limiting to be respectful
  • Monitor for structure changes with alerts
  • Document the scraping logic thoroughly

Firebase + Vercel = Great Combo

  • Real-time updates without WebSocket complexity
  • Vercel's edge network makes the site fast globally
  • Firebase's free tier is generous for side projects
  • Seamless authentication if you add user features

Docker for Data Pipelines

  • Isolate your scraper from your main app
  • Use docker-compose for local development
  • Environment variables for configuration
  • Health checks to monitor scraper status

Reflection

Building TrailMeteo taught me that the best projects solve your own problems. I use this app multiple times per week, and each use reveals new features to build or improvements to make.

The technical challenge of integrating ČHMÚ Aladin data pushed me to learn web scraping at a deeper level. The multi-model comparison feature came from frustration with single-source forecasts. The district map emerged from wanting to visualize temperature variations I knew existed but couldn't see.

On Data Liberation

Weather data, especially from government-funded services like ČHMÚ, should be accessible to everyone. While ČHMÚ provides valuable forecasts, the data isn't easily consumable by developers or third-party apps. Projects like TrailMeteo help bridge this gap, making public data truly public.

TrailMeteo is live at trailmeteo.pracny.app. It's currently focused on the Příbram district but expansion is planned. If you're in the Czech Republic and interested in hyperlocal weather data for your area, feel free to reach out.