A new ensemble to investigate spatiotemporal patterns of surface ozone from space

Our new paper in Scientific Data presents intercontinental hourly surface ozone datasets covering nearly 30 countries.
Published in Research Data
A new ensemble to investigate spatiotemporal patterns of surface ozone from space
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Surface ozone pollution is a global concern due to its detrimental effects on public health and food security. Yet it is not directly measurable from space due to the higher abundance of ozone in the stratosphere, which obscures measurements of surface ozone. Thanks to new-generation hyperspectral instruments and deep learning techniques, we have produced surface ozone datasets for a period of 10 years (2012-2021) in three regions: the Chinese mainland, Europe, and the continental United States. The surface ozone datasets were generated at a spatial resolution of 0.1◦×0.1◦ and across four timescales using the LEarning Surface Ozone (LESO) framework. The LESO framework proposed in our previous papers is independent of chemical transportation models and derives ozone in the lower atmosphere from satellite signals. All of the LESO datasets are available in Zenodo under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. 

This new ensemble of surface ozone concentrations possesses the capability to investigate the long-term spatiotemporal characteristics of ozone across a wider geographical range than any other currently available datasets. These datasets will not only contribute to an enhanced understanding of ecosystem resilience to climate change but also provide recommendations for globally coordinated ozone regulation.

References:

  1. Zhu, S., Xu, J., Zeng, J., Yu, C., Wang, Y., Wang, H., Shi, J., 2023. LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations. Sci Data 10, 741. https://doi.org/10.1038/s41597-023-02656-4
  2. Zhu, S., Xu, J., Yu, C., Wang, Y., Zeng, Q., Wang, H., Shi, J., 2022. Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China. IEEE Trans. Geosci. Remote Sensing 60, 1–11. https://doi.org/10.1109/TGRS.2022.3184629
  3. Zhu, S., Xu, J., Zeng, J., Yu, C., Wang, Y., Yan, H., 2022. Satellite-derived estimates of surface ozone by LESO: Extended application and performance evaluation. International Journal of Applied Earth Observation and Geoinformation 113, 103008. https://doi.org/10.1016/j.jag.2022.103008

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Research Data
Research Communities > Community > Research Data

Related Collections

With collections, you can get published faster and increase your visibility.

Epidemiological data

This Collection presents a series of articles describing epidemiological datasets spanning diverse populations, ecosystems, and disease contexts. Data are presented without hypotheses or significant analyses, and can be derived from population surveys, health registries, electronic health records, field sampling, or other sources.

Publishing Model: Open Access

Deadline: Dec 22, 2024

Metabolomics

This collection presents a series of articles describing metabolomics datasets, covering data from any organism type, collected via any valid metabolomic technique, and for any application.

Publishing Model: Open Access

Deadline: Nov 28, 2024