How to create an operational multi-model of seasonal forecasts?
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How to create an operational multi‑model of seasonal forecasts? Stephan Hemri1 · Jonas Bhend1 · Mark A. Liniger1 · Rodrigo Manzanas2 · Stefan Siegert3 · David B. Stephenson3 · José M. Gutiérrez4 · Anca Brookshaw5 · Francisco J. Doblas‑Reyes6,7 Received: 22 November 2019 / Accepted: 26 May 2020 © The Author(s) 2020
Abstract Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multimodel seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas. Keywords Seasonal forecasts · Multi-model combination · Recalibration
1 Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00382-020-05314-2) contains supplementary material, which is available to authorized users. * Stephan Hemri [email protected] 1
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich‑Airport, Switzerland
2
Meteorology Group, Dpto. de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander, Spain
3
University of Exeter, Exeter, UK
4
Meteorology Group, Instituto de Física de Cantabria (CSIC-Universidad de Cantabria), Santander, Spain
5
European Centre for Medium-Range Weather Forecasts, Reading, UK
6
ICREA, Pg. Lluis Companys, Barcelona, Spain
7
Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
Seasonal forecasts of atmospheric variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders in fields like agriculture (Ouédraogo et al. 2015; Ramírez-Rodrigues et al. 2016; Roudier et al. 2016; Rodriguez et al.
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