| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - time-series-forecasting |
| - other |
| tags: |
| - climate |
| - weather |
| - open-meteo |
| - era5 |
| - duckdb |
| - pyspark |
| pretty_name: Weather & Climate Big Data Analytics — 100-City ERA5 Historical Dataset (2016–2025) |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # 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`). |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| ```text |
| 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/ |
| ``` |
|
|
| --- |
|
|
| ## 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` |
|
|
| --- |
|
|
| ## 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`) | |
|
|
| --- |
|
|
| ## Attributions & Licensing |
|
|
| - **Weather Data Source:** [Open-Meteo Historical Weather API](https://open-meteo.com/) (ERA5 Reanalysis by ECMWF). |
| - **Location Catalogue Source:** SimpleMaps Basic World Cities Database. Licensed under [Creative Commons Attribution 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). |
|
|
| --- |
|
|
| ## 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. |
|
|
