GraphCast: an efficient tool for learning and forecasting medium-term global weather forecasts based on diffusion models

General Introduction

GraphCast is an advanced weather forecasting tool developed by Google DeepMind that aims to improve the accuracy of medium-term global weather forecasts through deep learning techniques. The project provides a wide range of pre-trained models and sample code that users can utilize to train and run weather models.GraphCast is particularly suited for research and applications that require high-resolution and multi-level weather data, is capable of processing ERA5 data from 1979 to 2017, and supports model training and forecasting on Google Cloud.

Function List

• Pre-trained models: High- and low-resolution pre-trained models are available for different computational resources and requirements.
• sample code (computing): Includes detailed sample code to help users get started quickly with model training and prediction.
• Data-processing tools: Provide data preprocessing, normalization and conversion tools to support multiple meteorological data formats.
• model training: Supports large-scale model training on Google Cloud, providing a detailed guide to cloud setup.
• Predictive Functions: Ability to generate medium-term weather forecasts, supporting multiple forecast models and parameter tuning.
• Model Evaluation: Provide model evaluation tools to help users analyze the accuracy and reliability of forecast results.

Using Help

Installation and Setup

1. environmental preparation: Ensure that Python 3.7 or above is installed and the necessary dependency libraries such as JAX, xarray, etc. are installed.
2. cloning project: Clone the GraphCast project by running the following command in a terminal:

   git clone https://github.com/google-deepmind/graphcast.git
cd graphcast

3. Installation of dependencies: Run the following command to install the project dependencies:

   pip install -r requirements.txt

usage example

1. Load data: Opengraphcast_demo.ipynb, follow the sample code to load ERA5 data.
2. Generating forecasts: Generate weather forecasts using a pre-trained model, sample code is shown below:

   from graphcast import GraphCast
model = GraphCast.load_pretrained('graphcast_operational')
predictions = model.predict(input_data)

3. assessment model: Analyze the predicted results using the assessment tool provided, an example of which is shown below:

   from graphcast import evaluate
results = evaluate(predictions, true_data)
print(results)

Detailed Function Operation

• Data preprocessing: Usedata_utils.pyPerform data preprocessing, including normalization and conversion.
• model training: To set up a TPU VM on Google Cloud, run thegencast_demo_cloud_vm.ipynbPerform large-scale model training.
• Prediction generation: Usegraphcast.pyThe method in generates medium-term weather forecasts that support multiple parameter tuning and model selection.
• Model Evaluation: Uselosses.pycap (a poem)evaluate.pyPerform model evaluations to analyze the accuracy and reliability of the predicted results.

With the above steps, users can quickly get started with GraphCast for medium-term global weather forecasting research and applications. Detailed example code and pre-trained models make the tool promising for a wide range of applications in the field of meteorological research.

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