项目介绍

akash-met/Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python 是一个托管在 GitHub 上的开源项目,主要使用 Jupyter Notebook 编写,目前获得 19 个 Star、1 次 Fork。

仓库全名
akash-met/Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python
Star 数
19
Fork 数
1

README 摘要

Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python Download any data from https://cds.climate.copernicus.eu/datasets faster using parallel processing. Climate Data Store/ Copernicus website/ Climate change service dataset. This article introduces a parallelized Python workflow that allows you to download ERA5 data significantly faster and HPC-compatible. Find the scrip here…

完整内容见 GitHub 原始 README 或本页 。

常见问题

Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python 用什么语言开发?

主要使用 Jupyter Notebook 编写,目前有 19 个 Star。

如何获取 Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python 的源码?

可以直接克隆仓库:git clone https://github.com/akash-met/Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python.git,默认分支为 main;安装与运行步骤见仓库 README。

Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python 最近还在维护吗?

仓库最近一次更新为 1月10日,当前没有未关闭的 Issue;Star 数为 19,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

Download any data from https://cds.climate.copernicus.eu/datasets faster using parallel processing. Climate Data Store/ Copernicus website/ Climate change service dataset. This article introduces a parallelized Python workflow that allows you to download ERA5 data significantly faster and HPC-compatible.

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

简要分析

热度分
22
价值分
11
活跃状态
活跃

语言占比

Jupyter Notebook

贡献者

README

Downloading-ERA5-Data-Faster-Using-Parallel-Processing-in-Python

Download any data from https://cds.climate.copernicus.eu/datasets faster using parallel processing. Climate Data Store/ Copernicus website/ Climate change service dataset. This article introduces a parallelized Python workflow that allows you to download ERA5 data significantly faster and HPC-compatible.

Find the scrip here parallel-ERA5-data-download.ipynb

Find the same and more articles on my Medium https://medium.com/@akash.pathaikara

Contact me on Linkdin https://www.linkedin.com/in/akash-pathaikara-752b0bb1/

Why This Repository?

Downloading ERA5 data serially can be extremely slow when working with:

  • Long time periods
  • Multiple variables
  • Large spatial domains

This workflow solves that problem by:

  • Parallelizing independent CDS API requests
  • Maximizing I/O throughput
  • Maintaining a clean, reproducible research workflow

Key Features

  • 🚀 Faster downloads using parallel processing
  • 🔁 Multiple CDS requests sent simultaneously
  • 🖥️ HPC-compatible (SLURM / PBS / compute nodes)
  • ⚙️ Uses the official CDS API
  • 📦 Modular, readable, and easy to extend

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