Swiss Weather Intelligence System by Bias & Variance

Inspiration

Switzerland faces rapidly evolving weather extremes (heat waves, flash floods, severe storms) that demand timely, clear, and actionable intelligence.
Emergency coordinators and the public need a single pane of glass: live conditions, short-term forecasts, and scenario-driven risk guidance.
We aimed to blend solid data visualization with ML-driven, scenario-aware predictions to support fast, informed decisions.

Real-time dashboard with interactive charts for temperature, humidity, wind, precipitation, pressure, and visibility.
Emergency scenario simulator (Heat Wave, Severe Storm, Flash Flood) with accelerated time to stress-test response flows.
Short-term (next 2 hours) ML-style forecasts with transparent comparison against past forecast runs.
Smart alerting panel that highlights emerging risks and actionable recommendations.
Lightweight personalization by user background (e.g., farmer, logistics, outdoor recreation) for relevant tips.
Polished UX: unified hover, spikelines, grouped legends, forecast window shading, and optional forecast-run timestamps

Python + Streamlit for the web UI, Pandas/Numpy for data handling, Plotly for interactive charts.
Scenario-aware data generator with 5-minute resolution and realistic physical constraints.
Trend-based forecast engine that blends short/medium/long-term slopes and recent changes; scenario influence is applied carefully per parameter.
Forecast snapshots stored in 5-minute buckets with generated_at timestamps; past predictions shown only when actuals exist for fair comparison.
UI details: Normalization for continuity, optional hover info for past-run generation times.
One-click Windows launcher (start_app.bat) and Docker setup for portable runs.

Making precipitation bars legible (overlap/opacity/width) while preserving temporal fidelity.
Avoiding UX clutter (removed range slider; kept unified hover, spikelines).
Ensuring fair past-vs-future comparisons: time-bucketed forecast runs, generated_at filtering, and pruning that doesn’t delete recent scenario runs.
Managing Streamlit session state for accelerated time, reproducible historical data, and scenario switches without flicker.
Keeping predictions realistic under scenario stress while respecting physical bounds.

A smooth, modern dashboard that’s demo-friendly and informative at a glance.
Scenario-aware short-term forecasts that evolve over time, not just static “futures.”
Transparent evaluation of past forecasts vs actuals, with optional tooltips showing forecast generation time.
Robust, reproducible historical data generation that looks and “feels” realistic in 5-minute increments.
Simple setup: one-click batch script on Windows and Docker support.

Transparent forecasting (time-bucketed snapshots, no hindsight) builds trust and is crucial for real operations.
Small UX tweaks (unified hover, legend grouping, spikelines) dramatically improve interpretability.
Streamlit session state orchestration is key to smooth simulations and consistent data pipelines.

Integrate live data sources (e.g., MeteoSwiss/Open-Meteo) and add map overlays for regional insights.
Upgrade the forecast engine with trained models (Prophet/LSTM/Ensembles) and probabilistic outputs.
Persistent storage (SQLite/Postgres) for historical runs, accuracy tracking, and backtesting.
Alert delivery channels (email/SMS/webhooks) and role-based dashboards for agencies and the public.
Mobile-friendly layout, accessibility improvements, and multi-location monitoring.
Deeper anomaly detection and early-warning analytics (multi-sensor fusion, confidence intervals).