Effective environmental planning and management to address climate change could be achieved through extensive environmental modeling with machine learning and conventional physical models. In order to develop and improve these models, practitioners and researchers need comprehensive benchmark datasets that are prepared and processed with environmental expertise that they can rely on. This study presents an extensive dataset of rainfall events for the state of Iowa (2016-2019) acquired from the National Weather Service Next Generation Weather Radar (NEXRAD) system and processed by a quantitative precipitation estimation system. The dataset presented in this study could be used for better disaster monitoring, response and recovery by paving the way for both predictive and prescriptive modeling.
@article{arxiv.2107.03432,
title = {IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation},
author = {Muhammed Sit and Bong-Chul Seo and Ibrahim Demir},
journal= {arXiv preprint arXiv:2107.03432},
year = {2021}
}
Comments
4 pages, Accepted to Tackling Climate Change with Machine Learning workshop at ICML 2021