tharinduperera/weather-clustering-data

Weather & Climate Big Data Analytics — 100-City ERA5 Historical Dataset (2016–2025)

Dataset Summary

This dataset contains 365,300 daily weather observations across 100 geographically diverse global cities spanning 80 countries over a 10-year continuous timeframe (January 1, 2016 – December 31, 2025).

The raw data was ingested from the Open-Meteo Historical Weather API (ERA5 Reanalysis Model) across 1,305 validated work units without missing values, then processed into Hive-partitioned Parquet format (year=YYYY).

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Dataset Structure

weather-clustering-data/
├── .gitattributes
├── README.md
├── locations/
│   └── locations.csv                 # 100-city global catalogue (80 countries)
├── metadata/
│   └── dataset_summary.json          # Dataset specifications & ingestion metadata
└── processed/
    └── parquet/
        └── weather/                  # 10 year partitions (2016 - 2025)
            ├── year=2016/
            ├── year=2017/
            ├── ...
            └── year=2025/

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Key Characteristics

• Total Rows: 365,300 daily records
• Locations: 100 global cities across 80 countries
• Timeframe: 2016-01-01 to 2025-12-31 (10 full calendar years, including leap years 2016, 2020, 2024 with 366 days each)
• Primary Data Format: Apache Parquet (Hive partitioned by year)
• Location Catalogue SHA-256: 5CBA155270694BB743E8ED4E95D3F6A95F135D4AA61CAF5B705CF13566F8BD19

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Data Schema & Variables

Column	Data Type	Description
location_id	BIGINT	Stable location identifier
city	VARCHAR	Primary city name
city_ascii	VARCHAR	ASCII-normalized city name
country	VARCHAR	Country name
iso2	VARCHAR	ISO 2-letter country code
iso3	VARCHAR	ISO 3-letter country code
admin_name	VARCHAR	State / Province / Administrative region
capital	VARCHAR	Capital classification
source_latitude	DOUBLE	Input city latitude
source_longitude	DOUBLE	Input city longitude
model_latitude	DOUBLE	ERA5 grid model latitude
model_longitude	DOUBLE	ERA5 grid model longitude
timezone	VARCHAR	Local IANA timezone
elevation	DOUBLE	Ground elevation (meters)
date	TIMESTAMP	Daily observation date (YYYY-MM-DD)
temperature_mean	DOUBLE	Mean daily 2m temperature (°C)
temperature_max	DOUBLE	Maximum daily 2m temperature (°C)
temperature_min	DOUBLE	Minimum daily 2m temperature (°C)
precipitation_sum	DOUBLE	Total daily precipitation (mm)
relative_humidity_mean	BIGINT	Mean daily relative humidity (%)
wind_speed_mean	DOUBLE	Mean daily 10m wind speed (km/h)
surface_pressure_mean	DOUBLE	Mean daily surface pressure (hPa)
year	BIGINT	Partition key (2016–2025)

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Attributions & Licensing

• Weather Data Source: Open-Meteo Historical Weather API (ERA5 Reanalysis by ECMWF).
• Location Catalogue Source: SimpleMaps Basic World Cities Database. Licensed under Creative Commons Attribution 4.0 (CC BY 4.0).

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Downstream Project Roadmap

1. Ingestion & Processing: Complete (1,305/1,305 work units, Parquet storage validated with DuckDB).
2. Phase 3 — DuckDB Exploratory Data Analysis (EDA): Statistical summary, seasonality, anomaly identification.
3. Phase 4 — Climate Feature Engineering: Annual climate metrics (Köppen-Geiger indicators, seasonality indices, temperature range, precipitation distribution).
4. Phase 5 — PySpark MLlib K-Means: Scalable climate regime clustering & silhouette evaluation.
5. Phase 6 — Cluster Interpretation: Climate zone profiles & geospatial taxonomy.
6. Phase 7 — Interactive Streamlit Dashboard: Web dashboard for climate exploration & visualization.