Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration
| Autor: | Gabriele Casciaro, Francesco Ferrari, Daniele Lagomarsino-Oneto, Andrea Lira-Loarca, Andrea Mazzino |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Physics - Atmospheric and Oceanic Physics General Energy Statistics - Machine Learning Mechanical Engineering Atmospheric and Oceanic Physics (physics.ao-ph) FOS: Physical sciences Machine Learning (stat.ML) Building and Construction Electrical and Electronic Engineering Pollution Industrial and Manufacturing Engineering Civil and Structural Engineering |
| DOI: | 10.48550/arxiv.2201.12234 |
| Popis: | 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 six-hour 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. |
| Databáze: | OpenAIRE |
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