Airport weather stations
≈950 U.S. sites · 24/7Wind, temperature, dew point, pressure, visibility, clouds and precipitation at airports.
Source ↗☀
FROM SENSORS TO A FORECAST
Measure one connected Earth. Run many futures.
Weather prediction already mixes specialized sensor networks, classical physics, machine learning and human judgment. The missing piece is not one magic sensor. It is better fusion and honest uncertainty.
THIS STATIC GUIDE USESFixed, cited figuresNo live forecast service is called on this page. Each figure links to the public page it came from, as read on 3 October 2026.
THIS DEMO DOES NOT DIRECTLY USEIoT, radar, satellites, AI models or a QPUThose systems may contribute upstream to professional forecasting, but this page does not ingest their live feeds.
01 · WHAT IS MEASURING EARTH NOW?“IoT” here mostly means purpose-built scientific instruments, not smart-home gadgets.
Doppler weather radar
158 operational U.S. radar systemsMaps precipitation and wind. Dual polarization helps identify precipitation type, intensity and tornado debris.
Source ↗Soil-moisture satellite
Global soil moisture · every 2–3 daysMeasures water in the surface soil, the land reservoir that controls evaporation and plant water supply.
Source ↗Farm soil stations
200+ agricultural stationsSoil moisture at several depths plus temperature, humidity, sun, wind, rain and pressure.
Source ↗Upper air + ocean
Balloons · aircraft · buoys · shipsProfiles winds and humidity above us while ocean platforms track the heat and moisture below storms.
AI used operationally
One single run + one ensembleA European forecast centre runs machine-learning forecasts four times daily, in 6-hour steps, out to 15 days on an approximately 0.25° grid.
Source ↗02 · HOW TODAY’S FORECAST FLOWSObservations correct the model; the model fills the gaps between observations.
Collect + quality check
Reject broken sensors, align timestamps and assign confidence from health, recency and location.
Assimilate
Fuse observations with the last forecast to estimate one 3D atmosphere, land and ocean starting state.
Run two engines
Physics models solve fluid and energy equations. AI models learn how weather fields evolve from past data.
Create an ensemble
Perturb the starting state and model assumptions. The spread measures forecast uncertainty.
Calibrate + publish
Correct local bias, compare with recent outcomes, then issue probabilities, alerts and explanations.
03 · YES, PLANTS CHANGE THE FORECAST
Water moves through soil, roots, leaves and air.
A wet landscape spends more solar energy evaporating water and can cool/moisten the lower atmosphere. A dry landscape sends more energy into heating the air. The exact storm response depends on winds, stability and the larger weather pattern.
🌧️Rainreaches land
→🟫Soilstores moisture
→🌿Rootsabsorb water
→💨Leavestranspire vapor
→☁️Airhumidity + clouds
→🌧️Next rainreturns water
↘ runoff feeds rivers + ocean↗ evaporation also comes from soil + water↺ vegetation changes with season, drought and fire
04 · PROPOSED ALGORITHM
Blend and score
A credible “best possible” forecast is not one model claiming certainty. It is a continuously scored team of physics, AI and local observations. Only independent backtesting can prove whether it wins.
FORECASTCalibrate [ Σ physics ensembles + Σ AI ensembles + local correction + land–plant feedback ]
For every observation, compute q = health × recency × spatial relevance. Bad or stale signals lose influence.
Assimilate atmosphere, ocean, soil moisture, snow, vegetation and urban heat across space and time.
Use multiple physics and AI models, then perturb initial conditions and uncertain processes, not just one “favorite” model.
Estimate evapotranspiration from soil moisture, leaf area, solar energy, humidity and wind; feed that vapor and cooling back into the atmosphere.
Train corrections separately by place, lead time and weather regime. A coastal thunderstorm should not share one bias rule with inland winter fog.
Choose model weights from recent Brier score/CRPS skill, normalize them, and publish threshold chances plus 10th–90th percentile ranges.
OBSERVED OUTCOME→SCORE EACH MODEL→UPDATE WEIGHTS→NEXT FORECAST↺
No “best ever” promise.This method would have to beat strong baselines on years of unseen weather, extreme events, reliability, compute cost and regional fairness before operational use.
USE NOWSensor networks + data assimilation + physics supercomputers + operational AI + ensembles + meteorologists
QUANTUM LATER, IF BENCHMARKS WINPotentially accelerate a narrow assimilation, sampling or optimization step; never replace the entire forecasting chain.