EGU23-6267, updated on 21 Feb 2024
https://doi.org/10.5194/egusphere-egu23-6267
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Daily extremes from the MSWEP global rainfall dataset compared to estimates from buoy networks through MEVD-based downscaling

Giorgio Dalmasso1,2, Emmanouil Anagnostou8, Luca Brocca4, Elsa Cattani5, Gaby Gruendemann3, Lanxin Hu8, Sante Laviola5, Vincenzo Levizzani5, Francesco Marra5,10, Christian Massari4, Efrat Morin6, Efthymios Nikolopoulos7, Ruud van Der Ent3, Enrico Zorzetto9, and Marco Marani1
Giorgio Dalmasso et al.
  • 1Department of Civil, Environmental, and Architectural Engineering, University of Padova, Padova, Italy
  • 2Scuola Universitaria Superiore IUSS, Pavia, Italy
  • 3Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands
  • 4Research Institute for Geo-Hydrological Protection, National Research Council (CNR), Perugia, Italy
  • 5National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, Italy
  • 6The Fredy and Nadine Hermann Institute of Earth Sciences, The Hebrew University of Jerusalem, Israel
  • 7Civil and Environmental Engineering Dept., Rutgers University, New Jersey, USA
  • 8Department of Civil & Environmental Engineering, University of Connecticut, Storrs, Connecticut
  • 9Program in atmospheric and oceanic sciences, Princeton University NJ
  • 10Department of Geosciences, University of Padova, Italy

Estimating the frequency of extreme precipitation events, both locally and over extended areas, is key for developing risk reduction measures in present and future climates. Large areas of the world are characterized by sparse or absent rain-gauge networks, which poses significant challenges to the estimation of extreme events in many applications. Remote sensing and reanalysis datasets may contribute to filling some of these gaps, but their use meets some important obstacles: 1) remote sensing/reanalysis rainfall estimates are defined at coarse resolutions, thereby preventing direct validations against ground observations; 2) they usually span a ~20-year observation period, making it difficult to estimate the frequency of large extremes; 3) they suffer from significant uncertainties. Using the novel Metastatistical Extreme Value Distribution (MEVD) and a recent statistical downscaling technique, we compare ground and satellite-based/model estimates of rainfall to quantify the improvement achieved through downscaling in high-quantile quantification. We focus on ocean rainfall observations, which are rarely considered in validating global databases, from the Tao-Triton, Pirata, and Rama buoy networks. We quantify the estimation uncertainty for point extremes associated with the MSWEP rainfall dataset. We find that the MEVD-based extreme value downscaling approach generally improves point extreme estimates. 

How to cite: Dalmasso, G., Anagnostou, E., Brocca, L., Cattani, E., Gruendemann, G., Hu, L., Laviola, S., Levizzani, V., Marra, F., Massari, C., Morin, E., Nikolopoulos, E., van Der Ent, R., Zorzetto, E., and Marani, M.: Daily extremes from the MSWEP global rainfall dataset compared to estimates from buoy networks through MEVD-based downscaling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6267, https://doi.org/10.5194/egusphere-egu23-6267, 2023.