Weather forecast models play a critical role in meteorological science for predicting short-term and long-term weather events. Today, these forecasts are obtained through advanced weather prediction models. These models consist of systems that express physical processes in the atmosphere through mathematical equations and are solved with the help of supercomputers. Processing millions of data points to predict the future state of the atmosphere, these systems are important not only for daily weather forecasts but also for aviation, maritime, agriculture, and disaster management. Weather forecast models are generally divided into two main categories: deterministic and probabilistic models. Deterministic models produce a single outcome based on a fixed initial condition, whereas probabilistic models create multiple possible scenarios using different initial conditions. Each approach has its advantages and limitations. The model’s success depends on factors such as the quality of the initial data used, the resolution level, and how realistically physical processes are represented within the model.
The effectiveness of a weather forecast model is not limited to forming the correct physical equations; these equations also need to be solved correctly and quickly. Therefore, high computational capacity supercomputers are required to run the models. Additionally, models divide the atmosphere into thousands of cells both horizontally and vertically, continuously calculating properties like temperature, pressure, wind direction, and humidity for these cells. In this way, the complex dynamics of the atmosphere are reflected as accurately as possible.
Types of Weather Forecast Models
Weather forecast models are generally classified as global, regional, and local models. Global models perform large-scale calculations covering the entire world; regional models focus on a specific continent, country, or region. Local models produce high-resolution and short-term forecasts for a smaller area. These models differ in resolution, forecast duration, and usage purposes.
An example of a global model is the ECMWF (European Centre for Medium-Range Weather Forecasts) model. Based in Europe, this model provides high-accuracy forecasts at a global scale. The American-origin GFS (Global Forecast System) model is another global model and provides initial data for many local and regional models. Among regional models, the WRF (Weather Research and Forecasting) model stands out and is widely preferred in academic research and applied weather forecasting projects.
One fundamental difference between these models is their resolution level. Global models generally operate at a lower resolution to simulate large-scale systems and the general dynamics of the atmosphere. Local models, on the other hand, focus on smaller areas, taking into account topography and local interactions to produce more precise forecasts. For example, local models give more accurate results for predicting local rainfall in mountainous regions. Additionally, hybrid models have been developed. These models combine data from both global and local models to provide multi-layered and more balanced forecasts. Thus, global models determine the general tendencies of the atmosphere while local models refine the local details. This approach aims to increase accuracy and reliability in weather forecasting.
Types of Weather Forecast Models (Generated with AI)
Numerical Weather Prediction and Computation Process
Numerical Weather Prediction (NWP) is based on solving differential equations that describe the physical properties of the atmosphere on computers. These equations include fundamental principles such as energy, momentum, mass conservation, and moisture transport. The solution process begins by dividing the atmosphere into three-dimensional cells at specific intervals (e.g., every 10-15 km). For each of these cells, values such as temperature, pressure, wind direction, and humidity are continuously calculated. High-performance computing (HPC) infrastructure is required for these calculations. Supercomputers complete the computations needed by the model within hours and predict the future atmospheric state.
Numerical forecast models cannot function without initial data. This data is provided by meteorological observation stations, weather balloons, radar, and satellite systems. All collected data give the model an "initial state," and the model attempts to project this state into the future. However, since the atmosphere is a chaotic system, even small errors in initial data can cause significant deviations in forecasts. Therefore, data assimilation and uncertainty calculations are quite important in numerical weather prediction.
Probabilistic Forecast Models
Probabilistic models enable forecasting weather events with multiple scenarios. In these models, the same physical model is run multiple times using different initial data. Each run creates a "scenario," and these scenarios are statistically evaluated. This approach is particularly effective in predicting sudden events such as storms, hail, or abrupt temperature changes. Because relying on the single output of deterministic models is insufficient for risk management, probabilistic forecasts provide decision-makers with probability-supported predictions. For example, a forecast indicating a 70% chance of heavy rain in a region allows operational planning for sectors such as agriculture or transportation. Thus, decision-makers move beyond the dilemma of “will it happen or not” and can develop more flexible strategies. The accuracy of probabilistic forecasts depends on the number of generated scenarios and the quality of initial data. Moreover, weighted averages of scenarios can produce a more balanced general forecast.
Challenges in Weather Forecasting
Despite the advancement of weather forecast models, some direct obstacles remain. These include the chaotic nature of the atmosphere, lack of initial data, complexity of surface features, and limited computing power. Since the atmosphere is a chaotic system, very small changes can lead to large differences. Therefore, deviations in long-term weather forecasts are inevitable. Reliability decreases rapidly, especially beyond forecasts of 7 days. This creates uncertainty in everyday planning. Another important challenge is data scarcity. Meteorological observation networks are sparse, especially over oceans and mountainous regions. This can cause errors in forming the initial state of models. Additionally, modeling surface features (such as forests, cities, and agricultural lands) requires numerous parameters. Computing power is also a major constraint. Running high-resolution models requires enormous energy and time. Therefore, some countries have advantages over others in accessing high-resolution forecasts. In developing countries, forecast accuracy may be lower due to these limitations.
Future Weather Forecasting Technologies
Weather forecasting systems will continue to develop in the future. New technologies such as artificial intelligence (AI), machine learning, and big data analytics are being integrated into this field. Some models now combine traditional physics-based calculations with AI-supported systems. For instance, IBM’s GRAF model can perform millions of operations per second, providing hyper-local forecasts. Also, satellite images are directly fed into AI systems for faster data analysis, offering significant advantages in the early prediction of rapidly developing weather events. AI systems can analyze historical data to determine future trends. This method complements physics-based models in areas where they fall short. These hybrid systems are especially valuable for modeling the impacts of climate change. In the future, personalized meteorological services will become widespread through individual weather forecasting devices, mobile applications, and IoT-supported sensors. Thus, special models will be implemented for professions such as farmers, sailors, or pilots.
