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Assessing the Ability of Gridded Datasets to Identify Local Extreme Weather Events
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  • Nina Grant,
  • Xinyue Luo,
  • Delavane Diaz,
  • Erik T Smith
Nina Grant
Rutgers University
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Xinyue Luo
Rice University
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Delavane Diaz
Electric Power Research Institute
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Erik T Smith
Kent State University

Corresponding Author:[email protected]

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Reanalysis products, or gridded datasets more broadly, are often used in place of surface observations. While they have been shown to capture long-term statistics on global or regional levels, it is still unclear how well they perform at the tails of the distribution, especially on daily timescales. Four widely used datasets, ERA5, ERA5-Land, MERRA-2, and PRISM, were assessed for their ability to capture extreme heat, extreme cold, and heavy precipitation events over the contiguous US (CONUS). While biases are evident in each dataset, particularly across the western US for temperature and along the Gulf Coast for heavy precipitation, all datasets do reasonably well in capturing extreme events and trends. Extreme heat is better represented than extreme cold or heavy precipitation. While no dataset emerges as a clear best for extreme heat, PRISM generally performs best for extreme cold and the bias-adjusted MERRA-2 dataset generally performs best for heavy precipitation days.