Add dataset documentation README.md

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- tabular-clustering
+- time-series-forecasting
+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.