Improving wind climate estimation using one-way coupled meso- to microscale models

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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).
Original languageEnglish
Publication date2015
Number of pages1
Publication statusPublished - 2015
Event11th EAWE PhD seminar on Wind Energy in Europe - Stuttgart, Germany
Duration: 23 Sep 201425 Sep 2015
Conference number: 11

Seminar

Seminar11th EAWE PhD seminar on Wind Energy in Europe
Number11
CountryGermany
CityStuttgart
Period23/09/201425/09/2015

Cite this

Olsen, B. T., Badger, J., Hahmann, A. N., Cavar, D., & Mann, J. (2015). Improving wind climate estimation using one-way coupled meso- to microscale models. Poster session presented at 11th EAWE PhD seminar on Wind Energy in Europe, Stuttgart, Germany.
@conference{aa0f2603f42141e58639ad9a208550c4,
title = "Improving wind climate estimation using one-way coupled meso- to microscale models",
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).",
author = "Olsen, {Bjarke Tobias} and Jake Badger and Hahmann, {Andrea N.} and Dalibor Cavar and Jakob Mann",
year = "2015",
language = "English",
note = "11th EAWE PhD seminar on Wind Energy in Europe, EAWE ; Conference date: 23-09-2014 Through 25-09-2015",

}

Olsen, BT, Badger, J, Hahmann, AN, Cavar, D & Mann, J 2015, 'Improving wind climate estimation using one-way coupled meso- to microscale models' 11th EAWE PhD seminar on Wind Energy in Europe, Stuttgart, Germany, 23/09/2014 - 25/09/2015, .

Improving wind climate estimation using one-way coupled meso- to microscale models. / Olsen, Bjarke Tobias; Badger, Jake; Hahmann, Andrea N.; Cavar, Dalibor; Mann, Jakob.

2015. Poster session presented at 11th EAWE PhD seminar on Wind Energy in Europe, Stuttgart, Germany.

Research output: Contribution to conferencePosterResearchpeer-review

TY - CONF

T1 - Improving wind climate estimation using one-way coupled meso- to microscale models

AU - Olsen, Bjarke Tobias

AU - Badger, Jake

AU - Hahmann, Andrea N.

AU - Cavar, Dalibor

AU - Mann, Jakob

PY - 2015

Y1 - 2015

N2 - 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).

AB - 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).

M3 - Poster

ER -

Olsen BT, Badger J, Hahmann AN, Cavar D, Mann J. Improving wind climate estimation using one-way coupled meso- to microscale models. 2015. Poster session presented at 11th EAWE PhD seminar on Wind Energy in Europe, Stuttgart, Germany.