chirps

API Client for CHIRPS

Satellite-based monitoring of dry and wet condition using Standardized Precipitation Index (SPI)

This is a package to access the ClimateSERV API

ClimateSERV allows development practitioners, scientists/researchers, and government decision-makers to visualize and download historical rainfall data, vegetation condition data, and 180-day forecasts of rainfall and temperature to improve understanding of, and make improved decisions for, issues related to agriculture and water availability.

Satellite-based monitoring of dry and wet condition using Standardized Precipitation Index (SPI)

A python library designed for downloading and managing hydro-meteorological data sourced from satellites and global models.

Workflow to Downscale Climate Projections

Python Jupyter notebooks for exploratory reading and visualization (RV) of various geospatial data products and file formats

Landing académica sobre la tesis de Guillermo S. Fuentes-Jaque acerca de precipitación mensual en alta resolución en el desierto de Atacama a partir de productos CHIRPS.

Interactive Gu 2026 rainfall anomaly analysis for Somaliland using CHIRPS3, R, and GeoLibre, with 1991–2020 climatology and regional diagnostics.

Climate–Health Vulnerability Index (CHVI) for Nigeria: a reproducible GIS-based research framework for investigating climate-health vulnerability through the integration of climate hazards, population exposure, healthcare accessibility, transportation accessibility, settlement morphology, and adaptive capacity indicators.

climate-agent is a command-line Python tool that automates the full pipeline for acquiring, processing, and validating climate datasets — primarily for East Africa impact modelling at ILRI (the International Livestock Research Institute).

The distributed Agentic Post Office - .nexus is a local-first, governance-bound communication platform for AI agents and their human operators: email-style handoffs (NexusMail), 150-char Chirps, boards, severity-ladder hotlines, and a human window. MCP-native, harness-agnostic, append-only.

Time-series monitoring of drought and vegetation stress across Northern Nigeria using MODIS vegetation indices and CHIRPS rainfall data.

Python workflow for multi-criteria landslide susceptibility zonation: AHP weighting with consistency ratio, raster reclassification, weighted linear combination and pooled quantile classification. Multi-software pipeline with NumPy, rasterio, GRASS GIS, GDAL and GMT; fully scripted and reproducible from public geospatial data.

Reproducible analysis of socio-ecological drivers of forest change in Montes de María (Colombia). Integrates dasymetric population grids (2005-2018), CHIRPS precipitation anomalies (2003-2020), and cross-sensor-harmonized Landsat EVI composites (2008-2010, 2021-2022) in a GWR model with land-cover masking.

Sources and standardizes open data (CHIRPS NASA POWER GEE iSDA)

Herramienta de Hydro-Intelligence para balances hídricos globales. Usa Google Earth Engine (GEE) y Python para procesar series CHIRPS (0.05°) y ERA5-Land. Implementa un buffer de 5.5 km de diámetro para coincidir con la resolución nativa del píxel (Escala 1:1), garantizando precisión en el cálculo de P, ETP.

Cloud-assisted Quantile Delta Mapping of CMIP6 temperature and precipitation using Earth Engine, Colab, CHIRPS or local MSWEP.

Village-level drinking-water accessibility for 5,159 villages in Sindh, Pakistan, from open satellite data, and why the standard method ranks the driest district as best-served.