Climate & Environmental Science

CLIMATE & ENVIRONMENT

Big Data. Bigger Questions.

Climate models generate petabytes. Sensor networks stream continuously. Satellite imagery arrives daily. You need AI-powered analysis that scales — with governance that enables collaboration without compromising data integrity.

THE CHALLENGE

Climate science is a data problem. And a collaboration problem.

Multi-institution collaborations. Terabytes of model output. Decades of observational data. Multiple funding agencies with different requirements.

The chaos: Every researcher with their own environment. Dependencies that don’t match. Results that can’t be reproduced. Data scattered across institutions.

The solution: Governed, reproducible analysis environments that scale from laptop to cluster — with AI assistance that makes every researcher more productive.

Built for climate science realities:

Massive data. Complex collaboration. Reproducible results.

Talk to Us

Scale to your data

From gigabytes to petabytes. Same environment, same tools, more compute.

Multi-institution collaboration

Share analyses without sharing credentials. Governed access across organizations.

Reproducibility built-in

Containerized environments. Version-controlled notebooks. No more ‘worked on my cluster.’

AI-powered analysis

Turn your climate scientists into 10x data analysts. Natural language queries on model output.

Any data source

NetCDF, GRIB, satellite imagery, station data, reanalysis. Query anything, build any visualization.

Grant compliance

Demonstrate data governance for NSF, DOE, NOAA, and international funding agencies.

EARTH SCIENCE DATA

The question-answering machine for your climate data.

Connect your data sources:

Climate model output (CMIP, CESM, WRF)
Satellite and remote sensing data
Observational networks and station data
Reanalysis products (ERA5, MERRA-2)
Your institution’s data archives

Ask questions in plain English:

“Show me temperature anomalies for 2024 vs 1990-2020 baseline”
“Which CMIP6 models best match observed precipitation in the Amazon?”
“Generate a time series of Arctic sea ice extent by month”

Get publication-ready results: AI-assisted analysis that generates reproducible, documented outputs.

Climate & Environmental Capabilities

Climate Modeling

Model output analysis. Inter-model comparison. Bias correction.

JupyterLab with xarray, dask, and AI assistance for analyzing massive model ensembles.

Remote Sensing

Satellite imagery processing. Land use change detection. Vegetation indices.

Process Landsat, Sentinel, and MODIS data with AI-powered pattern recognition.

Observational Networks

Station data QC. Network analysis. Gap filling.

Connect to NOAA, ECMWF, and institutional data archives with governed access.

Ecosystem Modeling

Carbon cycle analysis. Ecosystem services. Biodiversity metrics.

Integrate ecological data with climate projections for impact assessment.

Air & Water Quality

Pollution monitoring. Source attribution. Trend analysis.

Real-time environmental monitoring with AI-assisted anomaly detection.

Climate Risk

Hazard assessment. Vulnerability mapping. Adaptation planning.

Connect physical climate data with socioeconomic datasets for risk quantification.

Deploy where your data lives.

Cloud-native — Scale to thousands of cores for massive model runs.

HPC integration — Connect to your institution’s supercomputing resources.

Multi-cloud — Data on AWS, compute on GCP? No problem.

Desktop — Local analysis with Ollama for offline fieldwork.

See Deployment Options

Climate science at the speed of AI. Reproducibility at the speed of collaboration.

Calliope gives climate and environmental researchers the AI-powered analysis they need to tackle planetary-scale questions — with the governance that enables trusted collaboration.