Below are the datasets our lab uses as well as curated.  The paper citations for the datasets have been added as well.

TROPOMI-derived ground ozone in California

Using the TROPOMI satellite data and the HRRR meteorological feild, the daily-level ground ozone concentrations were estimated using random forest model. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

Spatial Resolution: 10 km

Temporal Resolution: daily

Temporal Coverage: May 2018 – April 2019

The estimated ozone season, non-ozone season average ozone concentrations and the model was publicly available at:  https://zenodo.org/records/14498553

Reference: Wang, W., Liu, X., Bi, J., & Liu, Y. (2022). A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology. Environment International, 158, 106917. [link]