Data Grouping Justification for Missing Value Imputation

Alot of values were found to be missing. I imputed missing values were by month so that values can reflect seasonal structure in the dataset. Research shows that environmental variables such as temperature, precipitation, humidity, wind speed, and solar irradiance often vary systematically across months, so month-based imputation preserves these seasonal patterns better than using one overall mean for the entire dataset. This same approach was applied consistently across the selected variables, including CO2 concentration, urbanization index, sea surface temperature, and particulate matter, so that imputed values remain contextually relevant to the time of year.

Summary

Row

Monthly Climate Summary

This dashboard summarizes the climate data from 2020 to 2024 using monthly averages across key environmental variables. It provides an overview of trends in temperature, precipitation, humidity, wind speed, solar irradiance, cloud cover, CO2 concentration, vegetation, particulate matter, and sea surface temperature.

Aggregating the data by month makes seasonal patterns easier to observe and compare across years. This helps reveal recurring climate behavior, shifts in environmental conditions, and broader patterns that support data-driven interpretation and environmental decision-making.

Row

Average Temperature

12.9 C

Total Precipitation

105625 mm

Average CO2 Concentration

422.7 ppm

Average Sea Surface Temperature

19.5 C

Mean Vegetation Index

0.45

Mean Urbanization Index

0.54

Temperature Trends

Row

Average Temperature with Time

This plot shows how average temperature changed from 2020 to 2024. It highlights fluctuations across the study period and provides a clear view of overall temperature patterns over time.

Row

Seasonal Variation in Average Temperature

This plot shows the cyclical pattern of temperature throughout the year. It reveals the trend of the recurring seasonal, with warmer and cooler periods showing natural climate variability across months.

Row

Correlation Analysis

Row

Correlation Heatmap of Climate Variables

This heatmap shows how the climate variables relate to one another. Stronger positive and negative correlations help reveal patterns and interactions across temperature, rainfall, atmospheric, and environmental indicators.

Row

Precipitation

Row

Precipitation Trends Over Time

This plot shows how rainfall varied from 2020 to 2024. After removing extreme outliers, the pattern becomes clearer and reveals seasonal fluctuations without a strong long-term increase or decrease.

Row

Atmospheric Indicators

Row

CO2 Concentration Over Time

This plot illustrates how atmospheric CO2 levels changed between 2020 and 2024. Despite short-term fluctuations, the overall trend shows a gradual increase, highlighting the persistent rise in greenhouse gas concentrations and their role in driving climate change.

Row

Ocean Indicators

Row

Sea Surface Temperature Over Time

This plot illustrates changes in sea surface temperature from 2020 to 2024. While temperatures fluctuate over time, the overall trend suggests gradual warming, highlighting natural ocean variability and its influence on broader climate systems.

Row

ENSO Index vs. Sea Surface Temperature

This plot illustrates the relationship between the ENSO Index and sea surface temperature. After filtering unrealistic ENSO values, the visualization reveals a weak negative correlation, showing a subtle influence of large-scale ocean-atmosphere interactions on marine temperatures.

Row

Land and Urbanization

Row

Vegetation Index vs. Urbanization Index

This plot illustrates how vegetation cover changes with increasing urbanization. After filtering extreme values, a slight negative trend emerges, suggesting that higher levels of urban development are associated with reduced vegetation. This highlights the environmental impact of urban expansion on green spaces.