项目介绍

jwagemann/era5_in_gee 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,目前获得 92 个 Star、23 次 Fork。

仓库全名
jwagemann/era5_in_gee
Star 数
92
Fork 数
23
未关闭 Issue
1

README 摘要

ERA5 reanalysis data in Google Earth Engine This repository contains a set of functions and example scripts to ingest Copernicus ERA5 reanalysis data into Google Earth Engine based on manifest uploads. The functions were developed during the process of making a subset of the ERA5 reanalysis data available in Google Earth Engine. Workflow overview The workflow consists of six major steps: 1. Downloading hourly data…

完整内容见 GitHub 原始 README 或本页 。

常见问题

era5_in_gee 用什么语言开发?

主要使用 Python 编写,目前有 92 个 Star。

如何获取 era5_in_gee 的源码?

可以直接克隆仓库:git clone https://github.com/jwagemann/era5_in_gee.git,默认分支为 master;安装与运行步骤见仓库 README。

era5_in_gee 最近还在维护吗?

仓库最近一次更新为 2021年7月26日,当前有 1 个未关闭 Issue;Star 数为 92,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

Functions and Python scripts to ingest ERA5 data into Google Earth Engine

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
141
价值分
62
活跃状态
活跃

语言占比

Jupyter Notebook
Python

README

ERA5 reanalysis data in Google Earth Engine

This repository contains a set of functions and example scripts to ingest Copernicus ERA5 reanalysis data into Google Earth Engine based on manifest uploads.

The functions were developed during the process of making a subset of the ERA5 reanalysis data available in Google Earth Engine.


Workflow overview

The workflow consists of six major steps:

  1. Downloading hourly data as daily files or monthly aggregates in NetCDF format from the Climate Data Store with cdsapi
  2. Aggregating hourly files to daily means or sums (total precipitation) with xarray
  3. Converting NetCDF data files to GeoTiff with gdal
  4. Uploading hourly, daily and monthly GeoTiff files to Google Cloud Platform (GCP) with google-cloud-storage Python API
  5. Creating image manifests (JSON-based files) describing the metadata and band names of the resulting Earth Engine asset
  6. Ingesting data files uploaded to GCP as assets into Earth Engine with earthengine-api and manifest uploads


Repository content


Python packages required


Naming convention and folder structure

The scripts base on the following naming convention and folder_structure:

Naming Convention

Data is downloaded from the Climate Data Store with the following syntax:

  • era5_ + name_of_variable_ + year_ + month_ + day + .nc
    • Example for a file of 2m dewpoint temperature from 1 January 1980: era5_2m_dewpoint_temperature_1980_01_01.nc

Folder Structure:

(example for 2m air temperature)

  • era5_t2m
    • nc
      • hourly
        • year (e.g. 1979)
      • daily
        • year (e.g. 1979)
      • monthly
        • year (e.g. 1979)
    • tiff
      • hourly
        • year (e.g. 1979)
      • daily
        • year (e.g. 1979)
      • monthly
        • year (e.g. 1979)
  • manifests
    • era5_hourly
      • year (e.g. 1979)
    • era5_daily
      • year (e.g. 1979)
    • era5_monthly
      • year (e.g. 1979)
    • manifest_template.json

References


License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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