Advancing Meteorological Forecasting: AI-based Approach to Synoptic Weather Map Analysis

TL;DR

This study introduces a novel AI-based preprocessing and autoencoder approach to enhance the interpretation of synoptic weather maps, aiming to improve meteorological forecasting accuracy and efficiency.

Contribution

It develops a new method combining unsupervised and supervised learning models to identify similar historical weather patterns, advancing practical meteorological analysis.

Findings

Cosine similarity is the most effective metric for matching weather maps.

Models perform well but have limitations in identifying highly similar patterns.

The approach increases efficiency in meteorological tasks.

Abstract

As global warming increases the complexity of weather patterns; the precision of weather forecasting becomes increasingly important. Our study proposes a novel preprocessing method and convolutional autoencoder model developed to improve the interpretation of synoptic weather maps. These are critical for meteorologists seeking a thorough understanding of weather conditions. This model could recognize historical synoptic weather maps that nearly match current atmospheric conditions, marking a significant step forward in modern technology in meteorological forecasting. This comprises unsupervised learning models like VQ-VQE, as well as supervised learning models like VGG16, VGG19, Xception, InceptionV3, and ResNet50 trained on the ImageNet dataset, as well as research into newer models like EfficientNet and ConvNeXt. Our findings proved that, while these models perform well in various settings, their ability to identify comparable synoptic weather maps has certain limits. Our research, motivated by the primary goal of significantly increasing meteorologists' efficiency in labor-intensive tasks, discovered that cosine similarity is the most effective metric, as determined by a combination of quantitative and qualitative assessments to accurately identify relevant historical weather patterns. This study broadens our understanding by shifting the emphasis from numerical precision to practical application, ensuring that our model is effective in theory practical, and accessible in the complex and dynamic field of meteorology.