12.2 Numerical weather prediction models and techniques

Numerical Weather Prediction models are the backbone of modern forecasting. They use complex math and current observations to simulate future weather. These models break down the atmosphere into grid points, solving equations for each to predict how conditions will change.

NWP models handle various physical processes, from air movement to cloud formation. They use simplified equations and clever tricks to represent small-scale events they can't directly simulate. Data assimilation helps improve forecasts by blending real-world observations with model predictions.

Fundamentals of Numerical Weather Prediction (NWP) Models

Concepts of NWP models

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• NWP models simulate future atmospheric conditions using computer simulations
  • Utilize current weather observations and mathematical equations representing physical processes (Navier-Stokes equations, thermodynamic equations)
• Key components of NWP models include:
  • Dynamical core solves equations of motion governing atmospheric flow (primitive equations)
  • Physical parameterizations represent sub-grid scale processes (radiation, convection, turbulence)
  • Data assimilation incorporates observations to improve initial conditions (3D-Var, 4D-Var, Ensemble Kalman Filter)
• NWP models discretize the atmosphere into grid points with:
  • Horizontal resolution specifying distance between grid points (1 km, 10 km)
  • Vertical resolution defining number of layers in the atmosphere
• Boundary conditions specify the atmospheric state at the edges of the model domain
• Initial conditions represent the current atmospheric state used to start the model (generated from data assimilation)

Physical processes in NWP

• Equations of motion describe the movement of air in the atmosphere:
  • Navier-Stokes equations represent conservation of momentum
  • Continuity equation represents conservation of mass
  • Thermodynamic equation represents conservation of energy
• Primitive equations simplify the equations of motion used in NWP models by:
  • Assuming hydrostatic balance where vertical pressure gradient force balances gravity
  • Neglecting small-scale turbulent motions
• Parameterization schemes represent sub-grid scale processes:
  • Radiation transfers energy through electromagnetic waves
  • Microphysics involves formation and evolution of cloud droplets and precipitation
  • Convection transports heat and moisture vertically due to buoyancy
  • Turbulence encompasses small-scale, chaotic motions that mix the atmosphere
  • Surface processes involve interactions between the atmosphere and Earth's surface (land, ocean)

Data Assimilation and Model Performance

Data assimilation for NWP

• Data assimilation combines observations with model predictions to improve initial conditions
  • Observations include measurements of atmospheric variables (temperature, pressure, wind)
  • Model predictions estimate atmospheric state based on previous model run
• Data assimilation is important for:
  1. Reducing errors in initial conditions, leading to more accurate forecasts
  2. Incorporating real-world observations into the model
  3. Correcting model biases and drift
• Common data assimilation techniques:
  • 3D-Var minimizes the difference between observations and model state at a single time
  • 4D-Var minimizes the difference between observations and model state over a time window
  • Ensemble Kalman Filter uses an ensemble of model states to estimate optimal initial conditions
• Observation types assimilated include satellite data, radar, weather balloons, surface stations, and aircraft measurements

NWP models vs techniques

• Global models (GFS, ECMWF) provide:
  • Advantages: Forecasts for the entire Earth, capturing large-scale atmospheric patterns
  • Limitations: Coarser resolution, may not resolve small-scale features
• Regional models (WRF, HRRR) offer:
  • Advantages: Higher resolution, better representation of local weather phenomena
  • Limitations: Limited area coverage, require boundary conditions from global models
• Ensemble forecasting:
  • Advantages: Provides probabilistic forecasts, accounts for uncertainty in initial conditions and model physics
  • Limitations: Computationally expensive, requires interpretation of multiple scenarios
• Coupled models (atmosphere-ocean, atmosphere-chemistry) have:
  • Advantages: Represent interactions between different Earth system components
  • Limitations: Increased complexity, higher computational costs, require additional data and parameterizations
• Machine learning and AI techniques can:
  • Advantages: Improve model performance, efficiently process large amounts of data
  • Limitations: Require extensive training data, may not capture physical processes explicitly, face interpretability challenges