A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network

Area of Science:

• Meteorology
• Artificial Intelligence
• Data Science

Background:

• Accurate weather forecasting is vital for transportation and daily activities.
• Numerical weather models rely on sensor data from automatic weather stations.
• Existing models like ECMWF have limitations in resolution and data accessibility compared to regional models.

Purpose of the Study:

• To develop a novel hybrid method for improving weather forecast accuracy.
• To enhance the output of the European Centre for Medium-Range Weather Forecasts (ECMWF) model.
• To achieve real-time, high-accuracy weather predictions comparable to regional models.

Main Methods:

• Utilized a hybrid approach combining machine learning with existing weather models.
• Integrated sensor data and numerical weather prediction inputs.
• Developed a neural network architecture for forecast improvement.

Main Results:

• The proposed method significantly increases the accuracy of ECMWF forecast outputs.
• Achieved accuracy levels comparable to higher-resolution limited area (regional) models.
• Enabled real-time, more accurate weather predictions.

Conclusions:

• The hybrid machine learning method offers a viable solution for enhancing medium-range weather forecasts.
• This approach bridges the gap between global and regional model accuracy.
• Provides a more user-friendly and accurate weather prediction service.