Astro-Weather Forecasting vs Meteorological Forecasts #

Research Question #

Is there any correlation between planetary configurations (Planets, Nodes, Zodiac Signs, Elements) and terrestrial weather patterns (Precipitation and Temperature)? Specifically, does the "Water Sign" hypothesis hold?

Hypothesis #

Planetary positions have no statistically significant impact on daily weather patterns when analyzed over large datasets. However, we test if Tropical Water signs correlate with increased precipitation.

Background #

Astrological traditions often link Moon phases and signs to weather (e.g., "Water signs bring rain"). This project specifically analyzes the "Tithi" (Lunar Day), Moon Sign, and Full Planetary Positions (Sun through Pluto + Nodes) in both Tropical and Vedic Zodiacs.

Data Sources #

• NOAA GHCN-Daily: Daily summaries of precipitation and temperature from global weather stations.
• Swiss Ephemeris: High-precision planetary position calculations (Tropical & Vedic/Lahiri).

Scripts #

• astro_data_generator.py: Generates a comprehensive lookup table of planetary positions (Sign, Longitude, Element) for 1900-2024.
• analysis.py: Aggregates NOAA weather data against all astrological features.
• precipitation_analysis.py: (Legacy) Aggregates NOAA PRCP data by Lunar Day.
• temperature_analysis.py: (Legacy) Aggregates NOAA TMAX data by Lunar Day.

Results #

See RESULTS.md for detailed findings.

• Confirmed: Sun in Tropical Water signs corresponds to notably higher daily precipitation (+14% vs avg).
• Element Analysis: Earth and Water signs generally showed different precipitation profiles depending on the planet (Slow planets act as climate proxies; Fast planets act as seasonal proxies).

1. Multivariate regression: Control for known factors
2. Spectral analysis: Identify common frequencies

Implementation Plan #

Step 1: Data Collection #

• Download historical weather data (temperature, precipitation, pressure)
• Generate planetary position and aspect data
• Obtain solar and lunar data for controls

Step 2: Data Alignment #

• Align time series to common frequency
• Create lagged versions of planetary variables
• Calculate aspect strength indicators
• Handle missing data appropriately

Step 3: Correlation Analysis #

• Calculate cross-correlations at multiple lags
• Test for Granger causality
• Control for sun, moon, and seasonal effects
• Test different weather variables

Step 4: Regression Modeling #

• Build multivariate regression models
• Include planetary variables as predictors
• Compare to baseline (sun/moon only)
• Assess out-of-sample prediction

Step 5: Spectral Analysis #

• Apply Fourier analysis to weather data
• Compare frequencies to planetary cycles
• Test for common periodicities

Expected Outputs #

• Correlation analysis results
• Granger causality test results
• Spectral analysis comparisons
• Assessment of astro-weather claims

Required Python Libraries #

pyswisseph
pandas
numpy
statsmodels
scipy
xarray
matplotlib
cartopy

Ethical Considerations #

• Clear distinction from weather forecasting
• Acknowledge climate science consensus
• No contribution to climate misinformation

Data Provenance #

Meteorological Data #

• Source: NOAA National Centers for Environmental Information (NCEI).
• Dataset: Global Historical Climatology Network (GHCN).
• Link: https://www.ncdc.noaa.gov/cdo-web/