meteosatpy 0.0.2

Library designed for downloading and managing meteorological data sourced from satellites and global models.

MeteoSatPy

What is it?

MeteoSatPy is a Python library designed for downloading and managing hydro-meteorological data sourced from satellites and global models. It offers users efficient access to near-real-time and historical weather conditions globally. With features for data acquisition, processing, and analysis. MeteoSatPy is a versatile tool for meteorological research, forecasting, and decision-making across various sectors.

Where to get it?

The source code is currently hosted on GitHub at: https://github.com/jusethCS/meteosatpy

Binary installers for the latest released version are available at the Python Package Index (PyPI)

# PyPI
pip install meteosatpy

Dependencies

• fiona: Streams simple feature data to and from GIS formats like GeoPackage and Shapefile.
• shapely: Manipulation and analysis of geometric objects in the Cartesian plane.
• geopandas: Analysis and manipulation of geographical data.
• rasterio: Reads and writes raster formats based on Numpy N-dimensional arrays.
• xarray: Works with labelled multi-dimensional arrays simple and efficient.
• netcdf4: Reads and writes netCDF files compatible with older versions of the library.
• h5netcdf: A Python interface for the netCDF4 file-format that reads and writes local or remote HDF5 files.

Prior to installing MeteoSatPy using PyPi, we recommend creating a new conda environment with dependencies:

# Conda
conda create -n [env_name] fiona shapely geopandas rasterio netcdf4 h5netcdf xarray

If you need to download "MSWEP" data, you'll need to install Rclone.

# Conda
conda install conda-forge::rclone

To set up Rclone with a Google Drive account, we recommend watching this tutorial. Note the MSWEP data are store on this Google Drive repository.

Examples

import datetime as dt
from meteosatpy import *

# Target date
date = dt.datetime(2020, 1, 1) # year, month, day

# Download CHIRPS data
ch = CHIRPS()
ch.download(
    date=date, 
    timestep="daily", 
    outpath=date.strftime("chirps_%Y-%m-%d.tif")
)

# Download CMORPH data
cm = CMORPH()
cm.download(
    date=date, 
    timestep="daily", 
    outpath=date.strftime("cmorph_%Y-%m-%d.tif")
)

# Download MSWEP data
mw = MSWEP()
mw.download(
    date=dates[i], 
    timestep="daily", 
    dataset="Past",
    outpath=date.strftime("mswep_%Y-%m-%d.tif"))