1. What is Weather Forecasting?

  • Definition: Weather forecasting is the science of predicting atmospheric conditions at a specific location for a future time.
  • Purpose: Helps people prepare for weather events such as rain, storms, heatwaves, or snow.

2. History of Weather Forecasting

Ancient Times

  • Observation-based: Early humans used clouds, wind direction, and animal behavior to predict weather.
  • Proverbs: Sayings like “Red sky at night, shepherd’s delight” were common.

19th Century

  • Telegraph: Allowed sharing weather observations over long distances.
  • First Forecast: Admiral Robert FitzRoy issued the first daily weather forecasts in the 1860s in England.

Early 20th Century

  • Synoptic Charts: Scientists started drawing weather maps using pressure, temperature, and wind data.
  • Norwegian Cyclone Model: Developed by Vilhelm Bjerknes and team, explaining how storms form and move.

3. Key Experiments and Milestones

Lewis Fry Richardson (1922)

  • Numerical Weather Prediction: Proposed using mathematics and physics to forecast weather.
  • First Attempt: Calculated a six-hour forecast by hand, which took weeks.

ENIAC Experiment (1950)

  • First Computer Forecast: Scientists used the ENIAC computer to produce a weather forecast in 24 hours.
  • Result: Proved computers could help forecast weather faster and more accurately.

Radiosonde Launches (1930s–Present)

  • Radiosondes: Balloon-borne instruments that measure temperature, humidity, and pressure at different altitudes.
  • Impact: Provided 3D data about the atmosphere, improving forecasts.

4. Modern Applications

Numerical Weather Prediction (NWP)

  • Supercomputers: Run complex models using data from satellites, radar, and weather stations.
  • Global and Regional Models: Examples include ECMWF (Europe) and GFS (USA).

Remote Sensing

  • Satellites: Monitor clouds, storms, and temperature from space.
  • Radar: Detects precipitation and tracks severe weather like tornadoes.

Everyday Uses

  • Agriculture: Farmers plan planting and harvesting based on weather forecasts.
  • Aviation: Pilots use forecasts to avoid turbulence and storms.
  • Disaster Preparedness: Helps communities prepare for hurricanes, floods, and heatwaves.

5. Recent Breakthroughs

Artificial Intelligence (AI) and Machine Learning

  • AI Models: Google’s “GraphCast” (2023) uses deep learning to predict global weather patterns faster than traditional models.
  • Benefits: Provides accurate forecasts in minutes instead of hours.

High-Resolution Forecasting

  • Local Models: New models can predict weather for small areas (as small as 1 km²).
  • Impact: Better warnings for flash floods, thunderstorms, and urban weather.

Data Integration

  • Crowdsourced Data: Smartphones and cars provide temperature and pressure readings, improving model accuracy.
  • IoT Devices: Weather sensors on buildings, ships, and drones add more real-time data.

Recent Study

  • Reference: “A next-generation operational global weather model” (Nature, 2023) describes how machine learning models are now rivaling traditional physics-based models in accuracy and speed.
  • Link: Nature Article

6. Comparison with Another Field: Climate Science

Weather Forecasting Climate Science
Short-term (hours to days) Long-term (years to centuries)
Predicts daily weather Studies average weather patterns
Uses real-time observations Uses historical data and models
Rapid updates (every hour) Slow updates (seasonal/yearly)
  • Connection: Both use similar models and data, but for different time scales and purposes.

7. Connection to Technology

  • Computers: Enabled complex calculations and simulations.
  • Satellites and Radar: Provide real-time, global coverage.
  • Internet: Delivers instant forecasts to billions of people.
  • Mobile Apps: Personalized weather alerts and warnings.
  • Big Data: Integrates vast amounts of information for better predictions.
  • Machine Learning: Improves forecast speed and accuracy.

8. Weather Forecasting and Bioluminescence

  • Example Connection: Weather forecasts help predict ocean conditions that affect bioluminescent organisms (like plankton blooms).
  • Glowing Waves: Calm, warm nights after certain weather patterns can lead to visible bioluminescence along coastlines.

9. Summary

  • Weather forecasting has evolved from simple observations to high-tech, computer-driven science.
  • Key experiments, like the first computer forecasts, changed how predictions are made.
  • Modern breakthroughs include AI-based models, high-resolution forecasting, and real-time data from new sources.
  • Weather forecasting is closely linked to technology and benefits many fields, from agriculture to disaster response.
  • Compared to climate science, weather forecasting focuses on short-term predictions.
  • Recent research shows AI can produce forecasts as accurate as traditional models, often much faster.
  • Understanding weather forecasting helps people make safer, smarter decisions every day.

Key Fact:
AI-powered weather models, like Google’s GraphCast, can now deliver global forecasts in under a minute, marking a major leap forward in meteorology (Nature, 2023).