Abstract:
The state of Earth’s air at a given time is called weather. Science and technology anticipate the future atmosphere for a certain location, which is vital to human survival. Today, weather predictions use quantitative data about the atmosphere and scientific understanding of atmospheric processes to predict its evolution.
Because the atmosphere is chaotic, solving the equations that describe it requires vast processing power. Due to a lack of understanding of atmospheric processes, projections grow less accurate as the period between now and the forecast rises. Weather prediction is difficult because it is continuous, data-intensive, complex, dynamic, and chaotic. Two methodologies are commonly employed for weather forecasting: empirical and dynamical.
Meteorologists call the first method analog forecasting. If data is abundant, this method can predict local weather. The second method, computer modeling, uses equations and atmospheric forward models. The dynamical technique is only suitable for modeling large-scale weather occurrences and may not predict short-term weather.
Most weather prediction systems combine empirical and dynamical methods. Artificial Neural Networks (ANN) tackle numerous nonlinear issues that traditional methods cannot. Most meteorological processes vary temporally and spatially. They face physical process nonlinearity, spatial and temporal scale conflicts, and parameter estimation uncertainty.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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