GFS Bias Correction using ML

This project implements a deep learning model in PyTorch for bias correction of GFS (Global Forecast System) temperature forecasts using ERA5 data (ground truth) as a reference. The goal is to reduce forecast biases in GFS 2m temperature forecasts by training a neural network to predict unbiased GFS data based on ERA5 observations.

Table of Contents

• Background
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Data Preparation
  • Training
  • Bias Correction
• Model Architecture
• Contributing
• License

Background

The GFS is a widely used weather forecasting system, but it may contain biases in temperature predictions. Bias correction is essential to improve the accuracy of these forecasts. This project presents a PyTorch-based bias correction model that learns to predict unbiased GFS temperature forecasts by comparing them with ERA5 data, which serves as the ground truth.

Getting Started

Prerequisites

Before using this project, make sure you have a workspace with the following libraries installed:

• Python 3.x
• PyTorch
• NumPy
• Xarray
• Other required libraries (e.g., Matplotlib, Pandas)