Climate modeling and environmental analysis. Use when analyzing climate data, projecting future climate scenarios (RCP/SSP), computing climate indices (SPI, PDSI), performing statistical downscaling, or assessing climate change impacts on geographic regions.
Resources
9Install
npx skillscat add activeinferenceinstitute/geo-infer/geo-infer-climate Install via the SkillsCat registry.
SKILL.md
GEO-INFER-CLIMATE
Instructions
Core Capabilities
- Climate projections: RCP/SSP scenario modeling with ensemble methods
- Climate indices: SPI, PDSI, heat wave indices, frost days, growing degree days
- Downscaling: Statistical (bias correction, delta method) and dynamical downscaling
- Impact assessment: Sector-specific climate vulnerability and adaptation planning
- Data integration: CMIP6, ERA5, observational station networks, gridded datasets
Key Imports
from geo_infer_climate.core.projections import ClimateProjection
from geo_infer_climate.core.indices import ClimateIndexCalculator
from geo_infer_climate.core.downscaling import StatisticalDownscaler
from geo_infer_climate.core.impact import ImpactAssessmentExamples
from geo_infer_climate.core.indices import ClimateIndexCalculator
calculator = ClimateIndexCalculator()
spi = calculator.compute_spi(precipitation_series, scale=3)
pdsi = calculator.compute_pdsi(precip, temp, latitude)Guidelines
Integrations
- Integrates with WATER for hydrological climate impacts
- Integrates with AG for agricultural climate adaptation
- Integrates with ENERGY for renewable resource projections
- Test:
uv run python -m pytest GEO-INFER-CLIMATE/tests/ -v