Global Climate Data with Environmental Features, 2020-2024

Monthly global climate data from January 2020 to May 2024 includes average temperature, precipitation, humidity, and environmental factors like CO2 concentration and particulate matter. The dataset was created by authors Atif Masih and Faisal Hameed and sourced from Kaggle and OpenML. It contains features for location, weather, and environmental indices.

Use Cases

• Study climate trends and patterns based on monthly temperature and precipitation data.
• Build predictive models for weather conditions based on features like humidity, wind speed, and cloud cover.
• Analyze environmental impacts based on CO2 concentration, particulate matter, and sea surface temperature.
• Assess climate change effects on agriculture or public health based on temporal and spatial data features.
• Inform policy-making for climate resilience based on urbanization, vegetation, and ENSO indices.

Strengths

• Data spans over four years from 2020 to 2024, providing a multi-year temporal series.
• Includes 20 diverse features covering weather, environmental conditions, and geographical context.
• Preprocessing steps were applied, such as cleaning the 'Sea_Surface_Temp_degC' column by removing NaN and 'Unknown' values.

Limitations

• Row count, file formats, and sample data are unknown, limiting suitability assessment.
• Last update date and organization of origin are unknown; freshness is unverified.
• Column-level documentation is limited; field semantics for some indices may require inference after download.

Provenance

Source

Kaggle, OpenML

Collection Method

Collection of global climate data, method unspecified.

Time Range

January 2020 to May 2024

Freshness

Data covers up to May 2024, but the last update date for the dataset itself is unknown.

Geography

Global, as indicated by latitude and longitude columns.