Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

Niko Wanders, Eric F. Wood

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a 'standard' equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

Original languageEnglish (US)
Article number094007
JournalEnvironmental Research Letters
Volume11
Issue number9
DOIs
StatePublished - Aug 31 2016

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Public Health, Environmental and Occupational Health

Keywords

  • NMMEphase 2
  • extreme events
  • global
  • sub-seasonal forecasting
  • weighted ensemble mean

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