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

arXiv: https://arxiv.org/abs/2411.05384 · HTML: https://mlsanity.com/paper/2411.05384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [Recent Advances in Autoencoder-Based Representation Learning](https://mlsanity.com/paper/1812.05069/) (2018)
- [KARINA: An Efficient Deep Learning Model for Global Weather Forecast](https://mlsanity.com/paper/2403.10555/) (2024)
- [Inductive biases in deep learning models for weather prediction](https://mlsanity.com/paper/2304.04664/) (2023)

## Built on by (most cited first, 1 of 1)

- [Adaptive Spatio-Temporal Graphs with Self-Supervised Pretraining for Multi-Horizon Weather Forecasting](https://mlsanity.com/paper/2511.00049/) (2025)

## Abstract (arXiv)

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.