Abstract
Numerical Weather Prediction (NWP) models are typically run with a horizontal resolution of 1-5 km. This means that they are unable to accurately capture small-scale effects of orography and surface roughness variations, and can result in large errors of wind climate estimations in undulating terrain, near coastlines and around forested sites. Stability effects can enhance these errors.
To achieve more accurate estimations of the local wind climate in these areas it is necessary to use downscaling techniques that are able to take into account the small scale effects as well. A common technique to achieve this is to couple a mesoscale model with a microscale model. In Badger et al. (2014) the output from a mesoscale model is used to generate wind speed frequency distributions for a number of wind direction sectors, and these are then used to create input for a linearized flow model (Troen et al. (1989)). While the approach in Badger et al. (2014) is based on a statistical-dynamical coupling technique, several attempts of dynamical coupling of meso- and microscale models have been made (e.g. Castro et al. 2014, Zajaczkowskiet al. 2011).
This study aims at using statistical-dynamical coupling of a meso- and microscale model to accurately predict the wind climate at complicated sites.
During the first part of this study to establish the performance of state-of-the-art mesoscale models in predicting local wind climates a benchmarking exercise is undertaken in collaboration with the European Wind Energy Association (EWEA). In this exercise more than 20 different mesoscale models are benchmarked in terms of their ability to accurately predict the local wind climate at six uncomplicated sites (see the green box to the right).
To achieve more accurate estimations of the local wind climate in these areas it is necessary to use downscaling techniques that are able to take into account the small scale effects as well. A common technique to achieve this is to couple a mesoscale model with a microscale model. In Badger et al. (2014) the output from a mesoscale model is used to generate wind speed frequency distributions for a number of wind direction sectors, and these are then used to create input for a linearized flow model (Troen et al. (1989)). While the approach in Badger et al. (2014) is based on a statistical-dynamical coupling technique, several attempts of dynamical coupling of meso- and microscale models have been made (e.g. Castro et al. 2014, Zajaczkowskiet al. 2011).
This study aims at using statistical-dynamical coupling of a meso- and microscale model to accurately predict the wind climate at complicated sites.
During the first part of this study to establish the performance of state-of-the-art mesoscale models in predicting local wind climates a benchmarking exercise is undertaken in collaboration with the European Wind Energy Association (EWEA). In this exercise more than 20 different mesoscale models are benchmarked in terms of their ability to accurately predict the local wind climate at six uncomplicated sites (see the green box to the right).
| Original language | English |
|---|---|
| Publication date | 2015 |
| Number of pages | 1 |
| Publication status | Published - 2015 |
| Event | 11th EAWE PhD seminar on Wind Energy in Europe - Stuttgart, Germany Duration: 23 Sep 2014 → 25 Sep 2015 Conference number: 11 |
Seminar
| Seminar | 11th EAWE PhD seminar on Wind Energy in Europe |
|---|---|
| Number | 11 |
| Country | Germany |
| City | Stuttgart |
| Period | 23/09/2014 → 25/09/2015 |