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Precipitation Python Packages

Python packages with the GitHub topic precipitation. Sorted by relevance, with stars and monthly downloads.
pySTEPS
pysteps

Python framework for short-term ensemble prediction systems.

5K 561 187
ShervanGharari
easymore

EASYMORE; EArth SYstem MOdeling REmapper

2K 26 24
thomasjkeel
rainfallqc

Quality control for rainfall data

2K 0 0
ltelab
disdrodb

An open-source python software for standardized processing, sharing, and analysis of disdrometer data

1K 27 13
MarkusPic
idf-analysis

Heavy rainfall intensity as a function of duration and return period is defined according to DWA-A 531 (2012). This program reads rainfall measurement data and calculates the distribution of design rainfall as a function of both return period and duration, for durations up to 12 hours (and beyond) and return periods in the range 0.5 a ≤ Tₙ ≤ 100 a.

1K 51 16
MarkusPic
ehyd-tools

Various tools for exporting and analyzing hydro(geo)logic time-series from the ehyd.gv.at platform of the Austian government.

797 9 4
ghiggi
gpm-api

Python Package for the Global Precipitation Measurement (GPM) Mission Data Archive

649 74 11
timcera
mettoolbox

mettoolbox is set of command line and Python tools for the analysis and reporting of meteorological data.

520 6 1
jleinonen
pytmatrix

Python code for T-matrix scattering calculations

408 125 50
adamzhen
climateservaccess

Custom library to access data through ClimateSERV API

361 2 0
mshumko
sampex

Programs to load and plot the SAMPEX satellite data.

348 1 0
SINApSE-INPE
ainpp-pb-latam

AINPP Precipitation Benchmark for Latin America.

338 1 0
montimaj
pycropwat

A Python Package for Computing Effective Precipitation Using Google Earth Engine Climate Data.

240 6 1
jkreklow
radproc

Radproc - RADOLAN composite processing, analysis and data exchange with ArcGIS

187 11 7
Dan-Boat
pyesd

Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.

120 60 11
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