climate-zarr-slr
Climate data pipeline for sea level rise and coastal vulnerability research
What It Does
climate-zarr-slr is a specialized processing pipeline that converts NetCDF climate model outputs into county-level statistics for sea level rise research. It enables researchers to analyze how precipitation and temperature projections interact with coastal population and income data across multiple climate scenarios.
The pipeline supports multi-region processing (CONUS, Alaska, Hawaii, Puerto Rico, Guam) with an interactive wizard interface and scales to large datasets using Dask distributed computing.
Key Features
NetCDF to Zarr
Converts climate model NetCDF files into cloud-optimized Zarr format with regional clipping for efficient access.
County-Level Statistics
Calculates precipitation and temperature statistics aggregated to US county boundaries using TIGER/Line shapefiles.
Interactive Wizard
Guided experience for configuring regions, variables, and scenarios with Rich terminal output and interactive prompts.
Cloud-Native Stack
Uses Dask distributed computing, VirtualiZarr, Kerchunk, and fsspec for scalable processing of large climate datasets.
Quick Start
git clone https://github.com/mihiarc/climate-zarr-slr.git
cd climate-zarr-slr
uv install# Guided wizard mode
python climate_cli.py wizard
# Create Zarr files from NetCDF
python climate_cli.py create-zarr
# Generate county-level statistics
python climate_cli.py county-stats
# List available regions
python climate_cli.py list-regionsClimate variables supported: precipitation (mm/day), air temperature, daily max/min temperature. Regions: CONUS, Alaska, Hawaii, Guam/Mariana Islands, Puerto Rico/USVI, Global.
Use Cases
- Analyzing climate change impacts on coastal populations vulnerable to sea level rise
- Generating county-level climate statistics for economic vulnerability modeling
- Converting large NetCDF climate datasets into analysis-ready Zarr format
- Multi-scenario climate analysis across all US territories
Get Started
Clone the repository and follow the setup guide to start processing climate data.
View on GitHub