Random Forest climate downscaling

Local Knowledge Graph (36 entities)

Knowledge graph centered on Random Forest climate downscaling with 36 nodes and 108 connections. Top connected: snowpack persistence, air temperature, precipitation, snow water equivalent, East River.

Description

Machine learning approach using Random Forest algorithms to downscale coarse resolution climate data to higher spatial resolution using topographic predictors. Models are trained on relationships between climate variables and geographic features, then applied to generate fine-scale climate surfaces.

Typical Equipment

• PRISM climate dataset
• Daymet dataset
• Random Forest algorithm
• Digital elevation model

Output Measurements

• daily precipitation
• daily temperature
• 400 m resolution climate grids

Papers Using This Protocol (1)

Places Where Used