There are 4 repositories under precipitation-data topic.
Notebooks Python to download and view ERA5 climatologic data, as well as to extract time series (hourly to monthly data on many atmospheric and land-surface parameters)
a snow data network (SNOTEL) R package
NASAaccess is R package that can generate gridded ascii tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys, ..etc). The package assumes that users have already set up a registration account(s) with Earthdata login as well as authorizing NASA GESDISC data access. Please refer to https://disc.gsfc.nasa.gov/data-access for further details. The package relies on 'curl' (https://curl.haxx.se/) commands and library to access and download data from NASA remote sensing servers. Since Mac users have curl as part of macOS, Windows users should make sure that their local machines have 'curl' installed properly. Creating the ".netrc" file at the user machine 'Home' directory and storing the user NASA GESDISC logging information in it is needed to execute the package commands. Instructions on creating the ".netrc" and ".urs_cookies" files can be accessed at https://wiki.earthdata.nasa.gov/display/EL/How+To+Access+Data+With+cURL+And+Wget.
Historical & future climatologies (1970-2100) - Time series extraction and maps plotting
A utility to search, download global level PERSIANN precipitation data from CHRS Data Portal
Notebooks python pour l'exploitation des données Météo-France en ligne sur meteo.data.gouv.fr
Python code for reading SM2RAIN dataset (global daily precipitations between 2007 and 2019) . Extraction of time serie for N points
This repo illustrates how public users can use cdsapi Python API to download climate model data and do some first analysis
Buishand and Double Mass tests for hydrologic Time serie homogeneity
This repository contains Matlab codes I made to process different satellite precipitation products.
気象庁が公開する降水量データをDashで可視化するアプリケーション
Exploration and extraction of daily GHCN climatological data (Global Historical Climatologic Network)
Spatial Interpolation and Precipitation Data Analysis of Sydney, Australia using R, (period examined 1990-2010).
Space-Time Statistical Quality Control of Extreme Precipitation Observation
R code for generating the projected 15-min precipitation.
The R package SnowSeasonAnalysis contains some tools for the quality check of precipitation data and for snow analysis.
Notebook to create a isohyet map (isovalues) from precipitation data at several raingauges
Tools for processing radar hourly rainfall datasets
Ingest code to convert nClimGrid ASCII datsets into NetCDF
This is a description to convert precipitation data compressed binary files using CDO and then jupyter notebook to Convert it to CSV
This R project conducts statistical analysis & produces scatterplots / visuals based on monthly + annual Central Park precipitation data from 1860 to 2022.
Code dealing with NOAA's precipitation DataSet for climatological ends in a specific point
Application of the ARIMA model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.
Visualizing and analyzing the precipitation data downloaded from NASA GPM data files.
Validation of satellite-based precipitation and temperature variables and long-term trends in West Africa.
Python implementation for calculating the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI)
Precipitation dataset comparison in GEE. Supplementary material (code and data) for the article submitted for IGARSS 2024, entitled Evaluation of precipitation datasets available in Google Earth Engine on a daily basis for Czechia.
R tools to fetch and visualize NOAA's Monthly U.S. Climate Gridded Dataset (NClimGrid)
Getting Data NASA POWER, such as Precipitation in Regional by Single Point approach. Example Precipitation / Rainfall Data in East Java 2018-2022
Assessed the viability of opening up a joint, ice cream and surf equipment shop in Hawaii based on Hawaii’s aggregate temperature and precipitation data using Python and SQLite.