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climate-data-analysis

Process NetCDF and GRIB climate datasets for climatology, spatial aggregation, and visualization.

2|Updated Jun 4, 2026
Search Tags:#climate#geospatial#xarray#netcdf#cartopy#era5#cmip6
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill climate-data-analysis-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: climate-data-analysis
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/climate-data-analysis
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill climate-data-analysis-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires xarray, netCDF4, cartopy, matplotlib, scipy, cdsapi, intake, intake-esm, xesmf, cfgrib, eccodes.

What problem does it solve?

This skill addresses the complexity of processing massive, multi-dimensional climate datasets, enabling researchers to perform sophisticated analysis without getting bogged down in low-level data manipulation.

Core Features & Use Cases

  • Data Processing: Efficiently load and manipulate NetCDF and GRIB files using xarray.
  • Climate Analytics: Compute climatologies, anomalies, trends, and climate indices like ENSO or drought metrics.
  • Visualization: Generate publication-quality maps with complex geographic projections using cartopy.
  • Use Case: A researcher can use this skill to calculate the 30-year temperature anomaly for a specific region using ERA5 reanalysis data and visualize the results on a Robinson projection map.

Quick Start

Use the climate-data-analysis skill to load the file era5_temperature_2020.nc and compute the annual mean temperature.

Frequently Asked Questions about climate-data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute climatology and spatial aggregation for NetCDF climate data?▼

Use xarray to load NetCDF climate data and compute climatologies by grouping data across time dimensions, enabling efficient spatial aggregation and regional averaging.

Can I use ERA5 reanalysis data to calculate temperature anomalies and visualize them on a Robinson projection?▼

Yes, you can process ERA5 reanalysis data retrieved via cdsapi to calculate temperature anomalies using xarray, and render publication-quality maps with a Robinson projection using cartopy.

Does this approach support processing CMIP6 model outputs and GRIB files?▼

Yes, processing CMIP6 model outputs and GRIB files is supported through intake-esm for collection cataloging and cfgrib with eccodes for loading GRIB data into xarray datasets.

What's the best way to perform bias correction on large-scale climate model outputs?▼

The best way to perform bias correction on large-scale climate model outputs is to load datasets with xarray, apply statistical adjustments using scipy, and regrid data to observational grids using xesmf.

How do I generate publication-quality cartographic visualizations for atmospheric research?▼

To generate publication-quality cartographic visualizations for atmospheric research, use cartopy to define complex geographic projections and matplotlib to render multi-dimensional climate data into high-resolution maps.

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