WEATHER PREDICTION MODELS & THEIR APPLICATIONS

Over the years, meteorologists built numerical models to solve the equation that describe the spate-temporal evolution of the atmosphere quantitatively. The most familiar form of the model is the Numerical Weather Prediction (NWP) model which has been the backbone of operational weather prediction since the 1980s. 

The NWP model uses current weather observations to forecast feature weather. It is done by using a mathematical equation that describes virtually all dynamic and physical processes in the atmosphere and ocean using numerical procedures and computational techniques.

The emphasis of numerical weather forecasting models has naturally been on achieving the most reliable forecast product with the available computation resources. These models are very important and are widely used nowadays in weather forecasting service because it allows the regional model to resolve explicitly smaller-scale meteorological phenomena that cannot be represented on the coarser grid of a global model.

Here are some areas where Numerical weather prediction models can be applied.

  • Climate forecasting
  • State of ocean surface
  • Wildfire modelling
  • Air quality modelling
  • Tropical cyclone forecasting

Climate Forecasting Model

The climate models are significant tools for expanding our understanding and predictability of climate behaviour on seasonal, annual, decadal, and centennial time scales. These models allow scientists to understand the complex Earth’s system, allowing them to test hypotheses and draw conclusions from the past and make predictions for future climate systems.

These models investigate and help determine the degree to which observed climate change, abnormal weather events may occur due to natural climate variability, human activity, or a combination of both.

The climate model works by identifying and quantifying the physical Earth system processes, representing them with mathematical equations, setting variables to represent initial conditions and subsequent changes in climate, and repeatedly solving the equations using powerful supercomputers. Examples of these models are GCM- General Circulation model and AGCM- Atmospheric General Circulation Model.

A General Circulation Model is a mathematical model that can be used in computer simulations of the global circulation of a planetary atmosphere or ocean. 

Accompanied with a piece of sea ice and land-surface components, AGCMs and oceanic GCMs are key components of global climate models and are widely used for understanding the climate and projecting climate change. For aspects of climate change, a range of man-made chemical emission scenarios can be fed into the climate models to see how an enhanced greenhouse effect would modify the Earth’s climate.

Ocean Surface Model

The transfer of energy between the wind blowing over the surface of an ocean and the ocean’s upper layer is an important element in wave dynamics. The spectral wave transport equation is used to describe the change in wave spectrum over changing topography. 

Since surface winds are the primary forcing mechanism in the spectral wave transport equation, ocean wave models use information produced by numerical weather prediction models as inputs to determine how much energy is transferred from the atmosphere into the layer at the surface of the ocean. Along with dissipation of energy through whitecaps and resonance between waves, surface winds from numerical weather models allow for more accurate predictions of the state of the sea surface.

Tropical Cyclone Forecasting Model

Tropical cyclones’ TCs are intense atmospheric vortices that form over warm ocean waters. A tropical cyclone forecast model is a computer program that uses meteorological data to predict aspects of the future state of a tropical cyclone. They rely on data gotten from a numerical weather model. There are three main types of this model

  • Statistical model
  • Dynamic model
  • Statistical-dynamical model

Statistical models forecast the evolution of a tropical cyclone based on an analysis of storm behaviour using climatology and correlate a storm’s position and date to produce a forecast that is not based on the physics of the atmosphere at the time. 

Dynamic models are numerical models that solve the governing equations of fluid flow in the atmosphere. These models are based on the same principles as other limited-area numerical weather prediction models but may include special computational techniques such as refined spatial domains that move along with the cyclone.

The statistical-dynamical model uses elements of both the statistical and dynamic approaches to forecasting.

There are four primary types of forecast that exist for tropical cyclones: track, intensity, storm surge and rainfall. An example of the tropical cyclone forecasting model is the MFM-Movable Fine-mesh, which was the first hurricane-tracking model based on atmospheric dynamics in 1978.

Air Quality Modelling

Air quality forecasting attempts to predict when the concentrations of pollutants will attain levels that are hazardous to public health. In the last decade, there have been noted progress in numerical weather prediction (NWP) modelling and studies of atmospheric processes for providing meteorological data for air pollution forecasting. 

The amount of pollutants in the atmosphere is determined by their mean velocity of movement through the atmosphere, their diffusion, chemical transformation, and ground deposition. In addition to this pollutant source, these models require data related to the state of the fluid flow in the atmosphere to determine its transport and diffusion. 

Meteorological conditions such as thermal inversions can prevent surface air from rising thereby trapping pollutants near the surface, which makes accurate forecasts of such events crucial for air quality modelling.

The most widely used Numerical Weather Prediction (NWP) models that are used for the forecast of air quality are UAM- Urban Airshed Modelling system and System Application International SAI.

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