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Title: Weighting of NMME temperature and precipitation forecasts across Europe
Authors: Slater, Louise
Villarini, Gabriele
Bradley, Allen
Keywords: Temperature
Precipitation
NMME
Forecasts
Multi-models
Europe
Issue Date: 2017
Publisher: © Elsevier
Citation: SLATER, L., VILLARINI, G. and BRADLEY, A., 2017. Weighting of NMME temperature and precipitation forecasts across Europe. Journal of Hydrology, 552, pp. 646–659.
Abstract: Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCMs) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.
Description: This paper is in closed access until 18th July 2018.
Sponsor: This study was supported by NOAA's Climate Program Office's Modeling, Analysis, Predictions, and Projections Program, Grant #NA15OAR4310073. Gabriele Villarini and Louise Slater also acknowledge financial support from the Broad Agency Announcement (BAA) Program and the Engineer Research and Development Center (ERDC)–Cold Regions Research and Engineering Laboratory (CRREL) under Contract No. W913E5-16-C-0002.
Version: Accepted for publication
DOI: 10.1016/j.jhydrol.2017.07.029
URI: https://dspace.lboro.ac.uk/2134/25933
Publisher Link: https://doi.org/10.1016/j.jhydrol.2017.07.029
ISSN: 0022-1694
Appears in Collections:Closed Access (Geography)

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