Building a Weather Prediction Model with Convoluted Neural Networks: A Technical Walkthrough

Introduction

Weather forecasting has always been at the forefront of scientific and technological advances and is almost always wrong. With the increasing utility of deep learning, our capabilities in this field have seen significant leaps and bounds.

Convolutional Neural Networks (CNNs), in particular, have shown great promise in interpreting and predicting complex patterns in meteorological data. In this article, we’ll begin a technical journey to create a CNN-based weather prediction model using PyTorch, jumping into the scientific principles and mathematical underpinnings that make our cyber brain possible.

The Science of Weather Prediction

Weather prediction is fundamentally about comprehending and forecasting the state of the atmosphere at a given location and time. This involves analyzing various meteorological variables such as temperature, pressure, humidity, and wind speed, which are influenced by the complex dance of atmospheric conditions, Earth’s rotation, and solar radiation.

In the past, weather forecasting relied on numerical weather prediction (NWP) models that solve the physical equations governing atmospheric dynamics. However, these models…