Probing NWP model deficiencies by statistical postprocessing
Autor: | Torben Skov Nielsen, Henrik Aalborg Nielsen, Andrea N. Hahmann, Martin Haubjerg Rosgaard |
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Rok vydání: | 2016 |
Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Meteorology 020209 energy Terrain 02 engineering and technology General linear modelling Wind energy scheduling Statistical forecasting 01 natural sciences Backward elimination Wind speed Model Output Statistics Bayesian information criterion 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences Wind direction Numerical weather prediction Backward stepwise selection Model output statistics Variable (computer science) Climatology Environmental science Bayesian Information Criterion NWP model development Lifted index |
Zdroj: | Rosgaard, M H, Nielsen, H A, Nielsen, T S & Hahmann, A N 2016, ' Probing NWP model deficiencies by statistical postprocessing ', Quarterly Journal of the Royal Meteorological Society, vol. 142, no. 695 Part B, pp. 1017–1028 . https://doi.org/10.1002/qj.2705 |
ISSN: | 1477-870X 0035-9009 |
DOI: | 10.1002/qj.2705 |
Popis: | The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational numerical weather prediction (NWP) model generating global weather forecasts four times daily, with numerous users worldwide. The analysis is based on two years of hourly wind speed time series measured at three locations; offshore, in coastal and flat terrain, and inland in complex topography, respectively. Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites. |
Databáze: | OpenAIRE |
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