A case study: The Australian Weather Dataset

Have you ever wondered WHY WEATHER DATA ANALYSIS IS IMPORTANT? For agricultural, disaster preparedness, tourism, or climate policy.

Weather data analysis is important because it helps people, businesses, tourists, farmers, and governments make informed decisions by turning raw meteorological readings into actionable insights.

Below are the key reasons why it is important to study and analyse weather data:

1. For Agricultural and food security, Weather analysis is important to farmers because they rely heavily on rainfall, and with this, they decide when to plant and harvest. It is also important to them because it helps in predicting days of Natural disasters, and they avoid planting during these days.

2. It helps in disaster preparedness and ensures public safety: In the detection of extreme events like heatwaves, bushfires, cyclones, just to mention a few, it helps to save lives. This can also help authorities to issue timely warnings, which will bring about early evacuation orders.

3. We also have Climate Change Monitoring, which helps to track warming trends and shifting rainfall patterns. This also helps in supporting environmental policies and also in sustainable development.

4. Infrastructure and Urban planning: Through the study and analysis of weather, engineers use results obtained from weather data to design roads, buildings, and drainage systems that can withstand local climate conditions.

5. In Energy Management, Energy companies plan demand and supply based on seasonal temperatures. They also make use of weather patterns to forecast production. for example (solar and energy)

6. Tourism and Management Planning: Tourists can plan peak seasons based on average weather conditions.

Why Central Tendency:

Central tendency in simple terms

Central tendency is a statistical way of finding the centre or typical value present in the dataset. In other words, we say instead of looking at thousands of daily readings from our data from the year 2008 to 2025, central tendency tells us, on an average day, what the temperature or rainfall is like.

In our Australian dataset, central tendency summarises 17 years of our daily weather data into a few numbers which describe the usual conditions the citizens of Australia can expect on certain days.

In the context of our Australian weather dataset, the central tendency refers to finding the "typical"(on average, the weather conditions to expect) weather conditions from years of daily records. Instead of examining the thousands of individual weather conditions recorded, measures like the mean, median, and mode help summarise the data into simple and meaningful numbers.

Measures like the following:

The Mean: The mean helps to show the overall average temperature or rainfall.

The Median: The median reveals the middle value that represents a typical day without being skewed by extreme values.

The Mode: The mode represents the most common weather condition recorded.

Trimmed Mean: It calculates the mean after removing extremely high and low values(outliers). it matters because it reduces the effect of rare, extreme weather events on the average.

Median Absolute Deviation (MAD): The MAD measures how much daily values differ from the median, using absolute differences. It matters because it shows variability in a way that is affected by extreme values than standard deviation.

Standard Deviation: It measures how spread out the daily values are from the mean. It matters because it shows how predictable or unpredictable the weather is.

Standard Error: It estimates how far the sample mean is likely to be from the true population mean. It matters because it is important for judging the reliability of averages in research.

Skewness: It measures how asymmetrical the data distribution is. It also matters because it tells us whether the average has been pulled in one direction by extreme days.

Kurtosis: It measures how "peaked" or "flat" the distribution is, compared to normal. Kurtosis matters because it helps to identify the frequency of extreme conditions.

Together, these measures provide a clear picture of the usual climate patterns that can be expected across Australia. Knowing how these things work helps the government agencies, farmers, tourists, citizens, and businesses make better decisions.

Although advanced statistical and computational models are commonly utilised to analyse large datasets, the value of basic descriptive statistics is often underestimated. This study, therefore, emphasises the application of fundamental statistical measures to systematically uncover and interpret underlying patterns within the Australian weather dataset.

Aim and Objectives

Aim

The primary aim of this study is to investigate how measures of central tendency, specifically the mean, the median, and the mode, can be applied to the Australian Meteorological data to identify key climate patterns, and demonstrate their relevance in supporting data-driven informed decision-making that are data-driven.

Objectives

1. To compute and analyse the mean, the median, and the mode of key weather variables (like the temperature and rainfall) across different periods within the dataset.

2. To examine how these measures of central tendency reflect typical weather conditions while minimising the influence of extreme values present in the dataset.

3. To compare variations in central tendency across the partitioned data.

4. To demonstrate how basic descriptive statistics can provide actionable insights for decision-making in sectors like agriculture, climate monitoring, and disaster preparedness.

5. To highlight the importance of central tendency as a foundation for more complex statistical and predictive models.

In this study, the Australian Weather dataset, which spans from 2008 to 2025, was partitioned into 17parts. Each partition represents a complete weather cycle, which ensures that seasonal cycles are kept intact and also allows us to calculate and compare central tendency measures consistently across time.

Why is partitioning important in this dataset?

I partitioned this dataset for the following reasons:

1. Seasonality Alignment: In Australia, summer starts in December, so using Dec-Nov makes more sense than Jan-Dec for capturing full seasonal patterns.

2. Comparability Across Years: Partitioning into 17 parts allows us to compare averages, medians, and modes year by year, without seasonal overlap issues.

3. Clarity in Trend Analysis: Splitting the data prevents data overload, and this makes it easier to spot trends and anomalies across different years.

4. Consistency in Statistical Summaries: Each Partition provides a complete weather cycle, making descriptive statistics more meaningful.

Next, we go to the analysis of the dataset.

Data cleaning has been properly done on the dataset before partitioning into yearly blocks. The programming language used here is the R programming language. You can learn more about the programming language here. R 4 datascience

Here, we check the roles of central tendency in our partitioned dataset. Using the tidyverse and psych packages in R, we have visualised our partitioned data and obtained descriptive statistics for our dataset.

The table below represents the first 30 observations present in our dataset

The table below shows the last observations in the dataset

Examining the structure of our dataset, we can assess the quality of the data organisation.

From the above data structure, our dataset is a tibble with 8,171 observations (rows) and 27 variables (columns).

Using the describe() function from the psych package in R, we are going to conduct our basic statistics. i.e(descriptive statistics).

Insights generated from the variable by Variable analysis

• Date: Date is an identifier in the dataset. Statistical measures are irrelevant due to coding. It is useful for time-series tracking.

• Location: A mean of (25.64) suggests a location code, and a standard deviation of (14.07) indicates a diverse site

• Min_temp: Minimum temperature average is 15.42°C, with a wide range (35.3°C) and slight left skew (-0.29).

Insight: This indicates that preparation for occasional cold snaps must be done

• Max_temp: Maximum temperatures average 27.24°C, with a broad range (45.5°C). Insight: Expect heatwaves to tell us that we must plan cooling measures.

• Rainfall: Low mean (2.91 mm) but high max (301.4 mm) and skew (10.75).

Insight: This indicates rare heavy rains. Enhance flood preparedness.

• Evaporation: Evaporation averages 6.86 mm, with a high range (63.2 mm) and skew (2.48).

Insight: Adjust irrigation for dry periods.

• Sunshine: Sunshine averages 8.19 hours, with a moderate range (13.6).

Insight: Expect some cloudy days, plan solar energy use.

• wind_gust_dir*: This is a directional code with (mean 1776), variability (SD 3.99).

Insight: suggests shifting winds, monitor for safety.

• Wind_gust_speed: Gusts average 40.01 m/s, with a wide range (113.0) and right skew (0.87).

Insight: Prepare for strong winds.

• wind_dir_9am*: Directional code with (mean 7.72); stable with SD 4.21. Used for morning wind planning.

• wind_dir_3pm*: Afternoon winds (mean 8.35) show slight shift; plan afternoon activities accordingly.

• Wind_speed_9am: Morning speed averages 14.13 m/s, with a high range (69.0) and skew (0.78). Watch for gusts.

• Wind_speed_3pm: Afternoon speed rises to 18.21 m/s, with a wide range (72.0).

Insight: Strengthen afternoon safety measures.

• Humidity_9am: Morning humidity averages 67.5%, with a range of 93%.

Insight: Expect variable comfort levels.

• Humidity_3pm: Afternoon humidity drops to 48.86%, with wide variability.

Insight: Plan outdoor activities accordingly.

• Pressure_9am: Morning pressure stable at 1016.2 hPa, with a moderate range (46.4). Monitor for weather shifts.

• Pressure_3pm: Afternoon pressure at 1014.4 hPa, similar range (53.6). Consistent weather pattern.

• Cloud_9am: Morning cloud cover averages 5.32 oktas, with a full range (8.0).

Insight: Expect variable sunshine.

• Cloud_3pm: Afternoon cloud cover at 4.94 oktas, stable range (8.0).

Insight: Plan outdoor events with caution

• Temp_9am: Morning temp averages 20.25°C, with a wide range (39.3).

Insights: Prepare for cool mornings

• Temp_3pm: Afternoon temp at 25.68°C, with a broad range (45.8).

Insight: Plan for temperature swings.

• Rain_today: Rain today is rare (mean 1.20 mm), but max 2.0 mm with skewness (1.50). Prepare for sudden showers.

Conclusion:

Australian Weather Data Analysis is crucial because it offers a dependable basis for decision-making across all sectors. With central tendencies like mean temperature and median rainfall, farmers, fishers, tourists, and the government can plan effectively. However, the wide ranges in rainfall and wind gusts highlight the importance of preparing for extreme weather conditions. This insight ensures safety, resource optimisation, and resilience, making it a critical tool for surviving in a dynamic climate.