Maesters - Numercial Weather Prediction

A package focus on fecth open-source global numerical weather prediction product in a elegant way.

The following data sources are supported.

✔︎ Deutscher Wetterdientst - ICON

✔︎ European Centre for Medium-Range Weather Forecasts - OPER / ENFO

✔︎ Canadian Meteorological Center - GEM / GEPS

The following data sources support is coming. 🚀🚀🚀

❏ National Oceanic and Atmospheric Adminstration - GFS

❏ Met Office - MOGREPS (not open-source anymore)

How to install

maesters-nwp depends on cdo. And as cdo is not supported on Windows platform, maesters-nwp fail to install on Windows.

Instal via conda (Recommended)

conda install -c conda-forge maesters-nwp

Install via pip

1. Install dependence cdo,curl (install cdo, curl)

conda install -c conda-forge cdo curl

2. Install maesters-nwp

pip install maesters-nwp

Usage

from maesters import Maester

# instantiate with source, product, batch (batch start time), hour (predict hour from bacth start time)
ec = Maester(source='ecmwf', product='oper', batch='2022-08-22 12:00',hour=[6,30],varname='TP_SFC')

# or instantiate with source, product, date (predict time),  batch (use newest bacth if batch is not given)
ec = Maester(source='ecmwf', product='oper', date='2022-08-23 18:00', varname='TMP_SFC')


# get xarray object
ec.xarray()

# or only download (if lcoal_dir is not given, default download to current dir)
ec.download(local_dir='./') 

# or operation download all data of the newest batch, default download to $HOME/data/{source}/{product}/{batch:%Y%m%d%H0000}
ec.operation(local_dir='./')

# more usage examples in example/example.ipynb

Variable Name

Source	Variable
ecmwf	variables
dwd	variables
cmc	variables

Problem List

P1: pyporj instal fail on M1 chip

S1:

brew install proj
pip install pyproj

Citation

If this package give helps to your research or work, it will be a enjoyable thing to the contributors of this package. And if you are willing to cite the contribution of this package in your publication, you can find the DOI information at https://doi.org/10.5281/zenodo.6796046.