Omkar2118/datasets huggingface dataset:
WEA
Full name: Weather Forecasting on 5 Locations

**Description**:
The WEA dataset is derived from the WeatherBench repository and designed for medium-range weather forecasting at five geographically diverse cities: London (UK), New York (US), Hong Kong (China), Cape Town (South Africa), and Singapore. It spans the period from 1979 to 2018, with a temporal resolution of 6 hours and a spatial resolution of 5.625° in both latitude and longitude. Each city is matched to its nearest grid point on the WeatherBench grid using minimal absolute distance in both axes.

**Task**: Time series forecasting of the 850 hPa temperature (T850) — a widely used mid-tropospheric climate indicator — at each location.

**Features**:

- Target Variable: T850 (850 hPa temperature in Kelvin) at the central grid point.

- Exogenous Variables (Total: 44):

    - Local: Z500 (500 hPa geopotential), t2m (2m temperature), u10 (10m zonal wind), v10 (10m meridional wind).

    - Spatial context: The same 5 variables (T850 + 4 exogenous) from the surrounding 8 grid points (3×3 window).

**Temporal Coverage**:

- Training Set: From Jan 1, 1980 to the end of the year preceding validation (e.g., up to Dec 31, 2014 for the 2015 validation year).

- Validation Set: One year preceding each test year (2015, 2016, 2017).

- Test Set: Years 2016, 2017, 2018 — each containing ~1,460 time steps.

**Use Cases**: Benchmarking time series models for weather forecasting in diverse climatic conditions, studying the impact of spatial and exogenous inputs on model performance, and evaluating generalisation across different latitudes and climate zones.

**Source**: Based on climate variables from the WeatherBench repository.

Introducing paper: Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting