File size: 4,334 Bytes
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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.
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