Temperature and rainfall datasets for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting"
<p>This page includes spatiotemporal datasets used in the paper <a href="https://doi.org/10.1016/j.neucom.2020.09.060">STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting.</a></p> <p>ARIMA and deep learning models use datasets, as follow:</p> <ul> <li>ARIMA models</li> </ul> <p>baseline-chirps-1981-2019.nc (rainfall data)<br> baseline-ucar-1979-2015.nc (temperature data)</p> <ul> <li>Deep learning models:</li> </ul> <p><em>5-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep5.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep5.nc (temperature data)</p> <p><em>15-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep15.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep15.nc (temperature data)</p>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 4