Melbourne Weather Data Analysis

Comprehensive analysis of Melbourne's 2024 weather patterns using data from multiple weather stations

365
Days Analyzed
2
Weather Stations
12
Variables Studied
Explore Analysis

Data Overview

Dataset Information

Analysis of Melbourne weather data covering the entire year 2024, including daily measurements of temperature, precipitation, wind, and atmospheric pressure from two distinct weather stations.

  • Daily weather observations
  • Temperature (min, max, average)
  • Precipitation and wind data
  • Atmospheric pressure readings

Data Limitations

Initial exploration of the Melbourne weather dataset revealed several limitations including significant missing values in 'snow', 'wpgt', and 'tsun' columns, limited temporal coverage from 2024 data only, and incomplete information for key variables in recent records.

  • Snow column 93.7% missing (expected for Melbourne)
  • Single year temporal coverage (2024 only)
  • Measurement uncertainties and outliers
  • Limited spatial representation (2 stations)

Analysis Methods

Applied comprehensive statistical analysis including descriptive statistics, correlation analysis, and hypothesis testing.

  • Descriptive statistics
  • Inferential statistics
  • Multi-station comparison
  • Extreme weather analysis

Data Analysis & Visualization

Monthly Temperature Trends

Monthly average temperatures showing Melbourne's distinct seasonal patterns with summer peaks in December-February and winter lows in June-August. Note the consistent 0.3°C temperature difference between stations.

Temperature Statistics

20.3°C
Average Summer Temperature
8.6°C
Average Winter Temperature
40.0°C
Highest Recorded Temperature
-1.2°C
Lowest Recorded Temperature

Key Insight

Melbourne experienced an 11.7°C difference between average summer and winter temperatures, demonstrating the city's temperate oceanic climate with distinct seasonal variation. The Viewbank station recorded slightly higher temperatures (0.3°C higher on average) than the main Melbourne station.

Precipitation Distribution Analysis

Distribution of rainfall events by intensity. The analysis shows that 65% of rainfall events were light (0-5mm), 25% moderate (5-15mm), with heavy rainfall (>15mm) being relatively rare at only 10% of rain days, following a typical right-skewed distribution for precipitation data.

Multi-Station Analysis

Comparison between Melbourne Main station and Viewbank station reveals important microclimatic variations across the metropolitan area.

Temperature Differences

0.3°C Mean Temperature Difference

Viewbank station consistently recorded slightly different temperatures than the main Melbourne station, with statistical significance (p < 0.001) indicating genuine microclimatic variations.

Precipitation Variability

4 Heavy Rain Events (Both Stations)

Of 11 heavy rainfall events (≥25mm) recorded across both stations, only 4 were captured by both stations simultaneously, highlighting the highly localized nature of intense precipitation in Melbourne.

Wind Correlation

0.78 Wind Speed Correlation

Strong correlation between wind speeds at both stations, but differences in wind direction patterns suggest local topographic influences.

Extreme Weather

18 vs 15 Hot Days (≥35°C)

Viewbank recorded more extreme temperature days than the main station, likely due to its more inland location away from the moderating effect of Port Phillip Bay.

Station Temperature Comparison

Bar chart comparing extreme weather events and mean temperatures between Melbourne Main and Viewbank stations, showing consistent patterns with systematic differences in extreme event frequency.

Statistical Insights

Correlation Analysis

Temperature Variables r > 0.8
Pressure vs Precipitation r = -0.27
Wind Speed vs Temperature r = -0.24

Strong positive correlations between temperature variables confirmed expected relationships, while pressure showed weak negative correlation with precipitation.

Hypothesis Testing

Summer vs Winter Temperatures
p < 0.0001
Highly Significant
Pressure on Rainy vs Dry Days
p < 0.05
Significant
Station Temperature Differences
p < 0.001
Highly Significant

All hypothesis tests confirmed statistically significant differences, validating the observed weather patterns and station variations.

Extreme Weather Events

Heat Waves
15-18 days ≥35°C
Cold Snaps
17-22 days ≤0°C
Heavy Rain
11 days ≥25mm

Extreme weather events showed spatial variability between stations, with Viewbank experiencing more temperature extremes due to its inland location.

Variable Relationships

Visual representation of key correlations between weather variables, confirming expected relationships between temperature variables (r > 0.8) and identifying the negative correlation between pressure and precipitation (r = -0.27).

Seasonal Weather Characteristics

Radar chart showing the distinct characteristics of each season in Melbourne's climate. Winter shows lower temperatures but higher pressure (1017.2 hPa), while summer displays higher temperatures with lower pressure (1011.4 hPa). Wind speeds are generally higher in winter and spring.

Key Conclusions

Confirmed Findings

  • Distinct seasonal temperature patterns with 11.7°C difference between summer (20.3°C) and winter (8.6°C) averages
  • Consistent precipitation distribution throughout the year with right-skewed distribution typical for rainfall data
  • Strong negative correlation between atmospheric pressure and precipitation probability (4.1 hPa difference)
  • Prevailing northerly and southwesterly wind patterns with stronger winds associated with northerly flows

New Insights

  • Significant microclimatic variations exist within the Melbourne metropolitan area
  • Only 4 of 11 heavy rainfall events were recorded at both stations, highlighting localized precipitation
  • Extreme temperature events vary by location, with inland areas experiencing more extremes
  • Local topography and urban structure significantly influence wind patterns

Recommendations

  • Implement multi-station weather monitoring networks for comprehensive urban climate analysis
  • Extend temporal coverage to identify long-term climate trends and anomalies
  • Develop urban climate models incorporating local geographic and structural factors
  • Use network approaches for accurate extreme weather event characterization

Limitations

  • Single year analysis limits long-term trend identification
  • Missing data in some variables affects seasonal comparison accuracy
  • Two-station network provides limited spatial coverage
  • Extreme value validation requires additional historical data verification