All numerical weather prediction models used for the wind industry need to produce their forecasts starting from the main synoptic hours 00, 06,12, and 18 UTC, once the analysis becomes available. The 6h latency time between two consecutive model runs calls for strategies to fill the gap by providing new accurate predictions having, at least, hourly frequency. This is done to accommodate the request of frequent, accurate and fresh information from traders and system regulators to continuously adapt their work strategies. Here, we propose a strategy where quasi-real time observed wind speed and weather model predictions are combined by means of a novel Ensemble Model Output Statistics (EMOS) strategy. The success of our strategy is measured by comparisons against observed wind speed from SYNOP stations over Italy in the years 2018 and 2019.(c) 2022 Published by Elsevier Ltd.

Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration

Casciaro G.;Ferrari F.;Lagomarsino Oneto D.;Lira Loarca A.;Mazzino A.
2022-01-01

Abstract

All numerical weather prediction models used for the wind industry need to produce their forecasts starting from the main synoptic hours 00, 06,12, and 18 UTC, once the analysis becomes available. The 6h latency time between two consecutive model runs calls for strategies to fill the gap by providing new accurate predictions having, at least, hourly frequency. This is done to accommodate the request of frequent, accurate and fresh information from traders and system regulators to continuously adapt their work strategies. Here, we propose a strategy where quasi-real time observed wind speed and weather model predictions are combined by means of a novel Ensemble Model Output Statistics (EMOS) strategy. The success of our strategy is measured by comparisons against observed wind speed from SYNOP stations over Italy in the years 2018 and 2019.(c) 2022 Published by Elsevier Ltd.
File in questo prodotto:
File Dimensione Formato  
Increasing_the_skill_2022.pdf

accesso chiuso

Tipologia: Documento in Post-print
Dimensione 2.05 MB
Formato Adobe PDF
2.05 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1102757
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact