Weather Forecasting
Overview
Weather Forecasting is a scientific process that observes the current state of the atmosphere and predicts future meteorological changes through numerical models based on physical laws. It provides essential information for various fields such as daily life, agriculture, aviation, maritime, and disaster preparedness, playing a key role in the safety and economic activities of modern society. It is a representative applied science field that integrates meteorology, atmospheric physics, and computer science, with continuous research and technological development worldwide to improve accuracy.
Main Content
1. Observation Systems
The first step in weather forecasting is accurate observation. Various observation equipment, including ground stations, radiosondes (weather balloons), aircraft, ships, buoys, and satellites, operate globally. In particular, geostationary and polar-orbiting meteorological satellites provide real-time data on clouds, water vapor, sea surface temperature, and wind over a wide area. Radar detects precipitation amount and type, and recently, data collected from civilian aircraft and car sensors is also being increasingly utilized.
2. Numerical Weather Prediction Models
Observation data is used as initial conditions for Numerical Weather Prediction (NWP) models run on supercomputers. Representative global models include the IFS from the European Centre for Medium-Range Weather Forecasts (ECMWF), the GFS from the U.S. National Centers for Environmental Prediction (NCEP), the UM from the UK Met Office, and the GDAPS from the Korea Meteorological Administration. These models divide the atmosphere into a three-dimensional grid and numerically solve the Navier-Stokes equations, thermodynamic laws, and radiative transfer equations to predict future states. Higher model resolution (grid spacing) allows for more detailed forecasts but significantly increases computational costs.
3. Ensemble Forecasting
To compensate for the uncertainty of a single model, ensemble forecast systems are used. By introducing slight variations to the initial conditions, multiple predictions are generated, and the statistical distribution of the results is analyzed to quantify forecast confidence. Representative systems include the ECMWF's EPS (Ensemble Prediction System) and NCEP's GEFS (Global Ensemble Forecast System). Ensemble forecasting is particularly useful for preemptively identifying the likelihood of hazardous weather events such as typhoon tracks, heavy rainfall, and cold waves.
4. Forecast Post-Processing and Application
Since numerical model outputs often contain systematic errors, they are processed into final forecasts through statistical methods like Model Output Statistics (MOS) or machine learning techniques. Forecasts are categorized into short-term (0–3 days), medium-term (4–10 days), long-term (11–30 days), and seasonal forecasts, covering various elements such as temperature, precipitation, wind, humidity, and cloud cover. Recently, 'Nowcasting' (0–6 hours) technology using deep learning has advanced, enabling faster detection of sudden heavy rain or localized weather changes.
5. Limitations and Challenges
Due to chaos theory, the accuracy of weather forecasts drops sharply beyond 10 days. Additionally, local effects such as mountainous terrain, coastlines, and urban heat islands are difficult for models to fully capture. As climate change increases the frequency and intensity of extreme weather, the reliability of traditional statistical forecasts is also changing. Consequently, new approaches utilizing artificial intelligence and big data are gaining attention.
Latest Trends
As of 2024–2025, the following major changes and trends are observed in the field of weather forecasting.
- Rise of AI-based Forecast Models: Data-driven deep learning models such as Google DeepMind's GraphCast, NVIDIA's FourCastNet, and Huawei's Pangu-Weather are attracting attention for showing performance comparable to or, in some areas, better than traditional numerical models. These models can generate predictions within seconds by learning from millions of weather data points, dramatically improving disaster response speed.
- Ultra-High-Resolution Local Models: Regional models with grid resolutions of 1 km or less at the city level are being experimentally operated. For example, the Korea Meteorological Administration has developed an 'ultra-high-resolution urban weather model' to more precisely predict heat island phenomena and localized heavy rain in large cities like Seoul.
- Integration of Satellite and IoT Data: High-resolution imagery from next-generation geostationary satellites (GEO-KOMPSAT-2A, GOES-16, etc.) and data from Internet of Things (IoT) sensors (car, drone, smartphone barometers) are being integrated into forecast systems in real time. This helps reduce observation gaps and improve initial forecast conditions.
- Climate Change-Adaptive Forecasting: In response to the increasing frequency of extreme weather (heatwaves, cold waves, heavy rain), the linkage between long-term forecasts and climate models is being strengthened. For instance, the accuracy of seasonal forecasts utilizing teleconnections like El Niño-La Niña is improving.
- Open Data and Collaboration: Led by the World Meteorological Organization (WMO), the sharing of meteorological data among member countries is expanding. ECMWF and NCEP provide ensemble forecast data for free, supporting the enhancement of forecasting capabilities in developing countries.
Related Topics
- [[Meteorology]]
- [[Numerical Weather Prediction]]
- [[Climate Change]]
- [[Artificial Intelligence]]
- [[Supercomputer]]
- [[Typhoon]]
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