Wind Speed Forecasting for a Large-Scale Measurement Network and Numerical Weather Modeling

Authors: Marek Brabec, Pavel Krč, Kryštof Eben, Emil Pelikán
Publication year: 2017
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Advances in Time Series Analysis and Forecasting
Projects: Advanced random field methods in data assimilation for short-term weather prediction
Bibliographic citation: BRABEC, Marek, KRČ, Pavel, EBEN, Kryštof, PELIKÁN, Emil. Wind Speed Forecasting for a Large-Scale Measurement Network and Numerical Weather Modeling. In: ROJAS, I., POMARES, H., VALENZUELA, O., eds. Advances in Time Series Analysis and Forecasting. Cham: Springer, 2017, S. 361-373. ISBN 978-3-319-55788-5. ISSN 1431-1968
Dostupné z: doi:10.1007/978-3-319-55789-2_25
DOI: 10.1007/978-3-319-55789-2_25
ID: 477859

Abstract

We investigate various problems encountered when forecasting wind speeds for a network of measurements stations using outputs of numerical weather prediction (NWP) model as one of the predictors in a statistical forecasting model. First, it is interesting to analyze prediction error properties for different station types (professional and amateur). Secondly, the statistical model can be viewed as a calibration of the original NWP model. Hence, careful semi-parametric smoothing of NWP input can discover various weak points of the NWP, and at the same time, it improves forecasting performance. It turns out that useful information is contained not only in the latest prediction available. It is beneficial to combine different horizon NWP predictions to one target time. GARCH sub-model for the residuals then shows complicated structure usable for short-term forecasts.