Systematic Study of Weather Variables for Rainfall Detection

TL;DR

This study systematically analyzes various weather and time variables using PCA to understand their interdependencies and effectiveness in rainfall detection, revealing that all variables contribute similarly and four principal components explain most variance.

Contribution

It introduces a PCA-based approach to analyze multiple weather variables for rainfall detection, highlighting their combined importance and interdependencies.

Findings

Four principal components explain 85% of variance.

All seven variables contribute similarly to rainfall detection.

PCA effectively distinguishes rain and no-rain scenarios.

Abstract

Numerous weather parameters affect the occurrence and amount of rainfall. Therefore, it is important to study these parameters and their interdependency. In this paper, different weather and time-related variables -- relative humidity, solar radiation, temperature, dew point, day-of-year, and time-of-day are analyzed systematically using Principal Component Analysis (PCA). We found that four principal components explain a cumulative variance of 85%. The first two principal components are applied to distinguish rain and no-rain scenarios as well. We conclude that all 7 variables have similar contribution towards rainfall detection.