Preprints
https://doi.org/10.5194/egusphere-2022-1401
https://doi.org/10.5194/egusphere-2022-1401
 
21 Dec 2022
21 Dec 2022
Status: this preprint is open for discussion.

Assessment of S2S ensemble extreme precipitation forecasts over Europe

Pauline Rivoire1,2, Olivia Martius1,3, Philippe Naveau4, and Alexandre Tuel1 Pauline Rivoire et al.
  • 1Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Switzerland
  • 2Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Switzerland
  • 3Mobiliar Lab for Natural Risks, University of Bern, Switzerland
  • 4Laboratoire des Sciences du Climat et de l’Environnement, ESTIMR, CNRS-CEA-UVSQ, Gif-sur-Yvette, France

Abstract. Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal to seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to introduce a new methodology to assess the skill of rare events, here extreme precipitation, in S2S forecasts. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyse daily events locally and spatially aggregated, as well as 7-day extreme event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g. temperature extremes or river discharge.

Pauline Rivoire et al.

Status: open (until 21 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1401', Anonymous Referee #1, 05 Jan 2023 reply
  • RC2: 'Comment on egusphere-2022-1401', Anonymous Referee #2, 23 Jan 2023 reply

Pauline Rivoire et al.

Pauline Rivoire et al.

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Short summary
Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. In this article, we assess the capacity of the precipitation forecast provided by ECMWF to predict heavy precipitation events on a subseasonal to seasonal (S2S) timescale over Europe. We find that the forecast skill of such events is generally higher in winter than in summer.