Weather forecasting combines science and art to predict atmospheric conditions. It relies on numerical models, statistical methods, and real-time observations to create short-term to long-range forecasts for various purposes.

Forecasters use surface, upper-air, radar, and satellite data to understand current conditions. Despite advanced technology, chaos theory and model uncertainties limit long-term predictability, highlighting the importance of clear communication and understanding forecast limitations.

Fundamentals of Weather Forecasting

Principles of weather forecasting

• Numerical Weather Prediction (NWP)
  • Mathematical models of atmosphere and oceans predict weather based on current conditions
  • Solves complex equations describing motion of air and water
  • Examples: Global Forecast System (GFS), European Centre for Medium-Range Weather Forecasts (ECMWF)
• Statistical methods
  • Historical data and patterns predict future weather conditions
  • Examples: Analog forecasting, regression analysis, machine learning techniques (neural networks, decision trees)
• Ensemble forecasting
  • Multiple model runs with slightly different initial conditions create range of possible outcomes
  • Quantifies uncertainty in forecast and improves overall accuracy
  • Combines results from various models to create probabilistic forecast
• Nowcasting
  • Short-term forecasting (0-6 hours) based on current observations and trends
  • Radar, satellite, and surface observations extrapolate near-term changes in weather
  • Useful for severe weather warnings and short-term decision making (airport operations, outdoor events)

Types of weather forecasts

• Short-range forecasts (0-3 days)
  • Daily planning, severe weather warnings, emergency management
  • Examples: local weather reports, aviation forecasts (TAFs), marine forecasts (coastal waters, high seas)
• Medium-range forecasts (3-10 days)
  • Agricultural planning (planting, harvesting), energy demand forecasting, long-term emergency preparedness
  • Example: extended weather outlooks (6-10 day, 8-14 day)
• Long-range forecasts (beyond 10 days)
  • Seasonal planning for crop planting, water resource management, energy supply
  • Examples: monthly and seasonal outlooks (1-month, 3-month), climate predictions (El Niño/La Niña)
• Specialized forecasts
  • Tailored to specific industries or user needs
  • Examples: air quality forecasts (ozone, particulate matter), fire weather forecasts (wildfire risk), space weather forecasts (solar flares, geomagnetic storms)

Atmospheric data in forecasting

• Surface observations
  • Weather stations, buoys, ships provide data on temperature, pressure, humidity, wind, precipitation
  • Automated stations (ASOS, AWOS) and manual observations (SYNOP, METAR)
• Upper-air observations
  • Weather balloons (radiosondes), aircraft, wind profilers provide data on temperature, humidity, wind at various altitudes
  • Soundings and wind profiles crucial for understanding vertical structure of atmosphere
• Radar observations
  • Radio waves detect precipitation and wind patterns
  • Identify and track severe weather (thunderstorms, tornadoes, hail)
  • Examples: Doppler radar (WSR-88D), dual-polarization radar
• Satellite observations
  • Global coverage of weather patterns, cloud cover, surface conditions
  • Monitor large-scale features (hurricanes, frontal systems, jet streams)
  • Examples: geostationary satellites (GOES, Himawari), polar-orbiting satellites (NOAA, MetOp)
• Data assimilation
  • Incorporates observations into numerical weather models
  • Improves accuracy of initial conditions and model forecasts
  • Techniques: 3D-Var, 4D-Var, Ensemble Kalman Filter (EnKF)

Limitations of weather predictions

• Chaos theory and the "butterfly effect"
  • Small changes in initial conditions lead to large differences in forecast over time
  • Limits predictability of weather beyond 10-14 days
  • Lorenz's famous example: a butterfly flapping its wings can influence weather patterns weeks later
• Model uncertainties
  • Imperfect representation of atmospheric processes and interactions in numerical models
  • Simplified parameterizations of sub-grid scale phenomena (cloud formation, turbulence)
  • Assumptions and approximations in model physics and dynamics
• Observation limitations
  • Incomplete spatial and temporal coverage of observations
  • Measurement errors and biases in observing systems (instrument calibration, site location)
  • Data gaps in remote areas (oceans, polar regions, developing countries)
• Forecast interpretation and communication
  • Challenges in conveying probabilistic information and uncertainty to users
  • Misinterpretation or misuse of forecast information by decision-makers and the public
  • Need for clear, concise, and actionable communication of weather risks and impacts