# Meteorology Driver Classification

> Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories. Use when this capability is needed.

- Skill: `tomevault-io/meteorology-driver-classification` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/meteorology-driver-classification`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/meteorology-driver-classification/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/meteorology-driver-classification

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# Driver Classification Guide

## Overview

When analyzing what drives changes in an environmental system, it is useful to group individual variables into broader categories based on their physical meaning.

## Common Driver Categories

### Heat
Variables related to thermal energy and radiation:
- Air temperature
- Shortwave radiation
- Longwave radiation
- Net radiation (shortwave + longwave)
- Surface temperature
- Humidity
- Cloud cover

### Flow
Variables related to water movement:
- Precipitation
- Inflow
- Outflow
- Streamflow
- Evaporation
- Runoff
- Groundwater flux

### Wind
Variables related to atmospheric circulation:
- Wind speed
- Wind direction
- Gust speed
- Atmospheric pressure

### Human
Variables related to anthropogenic activities:
- Developed area
- Agriculture area
- Impervious surface
- Population density
- Industrial output
- Land use change rate

## Derived Variables

Sometimes raw variables need to be combined before analysis:
```python
# Combine radiation components into net radiation
df['NetRadiation'] = df['Longwave'] + df['Shortwave']
```

## Grouping Strategy

1. Identify all available variables in your dataset
2. Assign each variable to a category based on physical meaning
3. Create derived variables if needed
4. Variables in the same category should be correlated

## Validation

After statistical grouping, verify that:
- Variables load on expected components
- Groupings make physical sense
- Categories are mutually exclusive

## Best Practices

- Use domain knowledge to define categories
- Combine related sub-variables before analysis
- Keep number of categories manageable (3-5 typically)
- Document your classification decisions

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<!-- tomevault:4.0:skill_md:2026-04-11 -->