Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe

Rivoire, Pauline; Martius, Olivia; Naveau, Philippe; Tuel, Alexandre (2023). Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe. Natural hazard and earth system science, 23(8), pp. 2857-2871. Copernicus Publications 10.5194/nhess-23-2857-2023

Preview	Text nhess-23-2857-2023.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (8MB) | Preview
	Text 2023_CLIMACT_Atmospheric_Science_Day_Certificate_Feldmann.pdf - Supplemental Material Restricted to registered users only Available under License Publisher holds Copyright. Download (81kB)

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 assess the forecast skill of rare events, here extreme precipitation, in S2S forecasts, using a metric specifically designed for extremes. 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 analyze daily events that are locally and spatially aggregated, as well as
7 d 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.

Interest & Impact

Downloads

15 since deposited on 11 Jan 2024
15 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item