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name geo-infer-climate
description 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.
prerequisites
required recommended
geo-infer-space
geo-infer-time
geo-infer-bayes
geo-infer-data
difficulty intermediate
estimated_time 45min
examples_dir ../GEO-INFER-EXAMPLES/examples/

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 ImpactAssessment

Examples

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