Methodology

Data Collection

We collect climate data from publicly available, authoritative sources:

• NASA POWER: Daily temperature, precipitation, and humidity (2001–2020)
• GeoNames: Geographic coordinates and city metadata

All data is fetched respectfully with appropriate rate limiting and caching to minimize load on source servers.

Climate Normals Computation

We compute monthly climate normals by processing the raw data from NASA POWER. For each city and month, we calculate:

• Temperature: Average, maximum, and minimum from daily measurements (both Celsius and Fahrenheit)
• Precipitation: Total monthly rainfall in mm and inches
• Rainy Days: Count of days with precipitation ≥ 1mm
• Humidity: Relative humidity percentage

Best Time to Visit Algorithm

We calculate a "best months" score for each month based on three factors:

1. Temperature Comfort (Weight: ~56%)

Ideal temperature is considered to be around 22°C (72°F). Scores decrease as temperatures deviate from this ideal, with the formula:

score = max(0, 100 - |temperature - 22| × 5)
      

2. Precipitation (Weight: ~28%)

Lower precipitation is generally preferred for travel. Score calculation:

score = max(0, 50 - precipitation_mm)
      

3. Humidity (Weight: ~16%)

Ideal humidity is around 60%. Score decreases with deviation:

score = max(0, 30 - |humidity - 60| × 0.5)
      

The total score is the sum of these three components. The top 3 months with the highest scores are designated as the "best months to visit."

Limitations and Considerations

• Our algorithm uses general comfort preferences that may not match every traveler's needs
• Some travelers prefer cooler or warmer weather than our "ideal" temperature
• Special events, festivals, and holidays are not factored into recommendations
• Temperature values are satellite/reanalysis grid averages and may differ from airport station normals by a few degrees
• Microclimates and weather variability within cities are not captured

Transparency

All our scoring algorithms are deterministic and open. We do not use machine learning or opaque methods. The complete data pipeline is automated and runs monthly to ensure up-to-date information.