Picture: Rainfall on a tree. Credit: Shah-shah (Unsplash).

Observational evidence of precipitation extremes is vital to better understand how these events might change in a future warmer climate. However, its measurement is complicated by the wide variability of the groundbased measurement network. Observational datasets created from satellite retrievals generally provide better spatial coverage compared to station-based gridded products, but there is little guidance on the reliability of these observations.

Focusing on the land regions around the world, the researchers assessed the representation of annual maximum of daily precipitation (Rx1day) across 22 observational products gridded at 1°x1° resolution. These were clustered into four categories:

  • Station-based in situ,
  • Satellite observations with a correction to rain gauges,
  • Satellite observations without a correction to rain gauges
  • Reanalyses.

The researchers also evaluated the spread of measurements for different products within each category and then across all four categories. This was used to measure observational uncertainty.

The result showed that given the level of uncertainties associated with the estimation of Rx1day in the observations, none of the datasets could be regarded as the best estimate.

The researchers recommended to avoid using reanalyses as observational evidence and to consider in situ and satellite data (the corrected version preferably) in an ensemble of products to gain a better estimation of precipitation extremes and their observational uncertainties.

  • Paper: Alexander, Lisa V., Margot Bador, Rémy Roca, Steefan Contractor, Markus Donat, and Phuong Loan Nguyen. “Intercomparison of Annual Precipitation Indices and Extremes over Global Land Areas from in Situ, Space-Based and Reanalysis Products.” Environmental Research Letters, 2020. https://doi.org/10.1088/1748-9326/ab79e2.