MetOfficeDataHub
Python wrapper around MetOffice Atmospheric Model Data REST API
MetOfficeDataHub is a simple wrapper for the API provided by the British Met Office known as Weather DataHub. It can be used to retrieve weather observations and forecasts. The aim is to focus on the AMD - Atmosphere Model Data REST API https://metoffice.apiconnect.ibmcloud.com/metoffice/production/product/17502/api/16908
This wrapper currently only downloads the latest results. No historic data can be fetched. The timestamp can be seen in the grib/xarray files.
⚠️ This (unofficial) library has been built and is maintained by Open Climate Fix, not the UK MetOffice.
Access
You need to register with the Weather DataHub and obtain an API key and secret. These should be placed in environment variables
as API_KEY and API_SECRET.
Python
Installation
Install directly from pypi using
pip install metoffice-weather-datahub
Example
from metofficedatahub.multiple_files import MetOfficeDataHub
# 1. Get data from API, download grib files
datahub = MetOfficeDataHub(client_id="fake", client_secret="fake")
datahub.download_all_files(order_ids=["test_order_id"])
# 2. load grib files to one Xarray Dataset
data = datahub.load_all_files()
CLI
You can run the script directly as a CLI using:
python3 metofficedatahub/app.py --save-dir="s3://bucket/folder"
which will download all the files from Weather DataHub, join them together into a xarray dataset, and then save them.
You can set the environmental variable LOG_LEVEL to define what log level you would like.
It may also be worth setting 'RAW_DIR' so that the raw files are saved to a certain folder, and not downloded again if they are already there.
Docker
The application can be run using docker
You can pull the production docker image from docker hub using
docker pull openclimatefix/metoffice_weather_datahub
local
You can also build your own docker image locally using
docker build -t metofficedatahub -f infrastructure/docker/Dockerfile .
and then to run the docker file use
docker run -it -e API_KEY=change -e API_SECRET=change -e SAVE_DIR='save_dir' -e ORDER_IDS='id1 id2 id3' metofficedatahub
Data variables
When the data is loaded they are given a short name. Here is are some common examples:
- lcc : Low-level cloud cover in %.
- mcc : Medium-level cloud cover in %.
- hcc : High-level cloud cover in %.
- sde : Snow depth in meters.
- dlwrf : Downward longwave radiation flux - ground
- t : Air temperature at 1 meter above surface in Kelvin.
- r : Relative humidty in %.
- vis : Visibility in meters.
- si10 : Wind speed in meters per second, 10 meters above surface.
- prate : Precipitation rate at the surface in kg/m^2/s.
Contributors ✨
Thanks goes to these wonderful people (emoji key):
 Peter Dudfield 💻 |
 Flo 💻 |
 Jack Kelly 👀 |
 Jacob Bieker 👀 |
This project follows the all-contributors specification. Contributions of any kind welcome!
1 maintainer
Release files for metoffice-weather-datahub 1.2.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| metoffice-weather-datahub-1.2.3.tar.gz | 14.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| metoffice_weather_datahub-1.2.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 29.7 kB
Release files / metoffice-weather-datahub-1.2.3.tar.gz
| Download URL | metoffice-weather-datahub-1.2.3.tar.gz |
|---|---|
| Size | 14.7 kB |
| Tags | Source |
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Release files / metoffice_weather_datahub-1.2.3-py3-none-any.whl
| Download URL | metoffice_weather_datahub-1.2.3-py3-none-any.whl |
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| Tags | Python 3 |
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