Fig. 1 – AQ System AQ510 Wind Finder Sodar (Sonic Detection And Ranging) together with mast. Sodar measures wind speed and direction up to 200 m height. Sodar was place there for calibration / accuracy assessment in forest conditions.

(see more images at the end of the document)

Forest effects on the wind resource, turbulence and loads are studied within this project. Over forest, there exists a larger vertical wind change as well as higher turbulence compared to places without forest. This can lead to reduced electricity production but the uncertainty is large. Measurements at each individual wind turbine location are too expensive. Therefore, one must use models to estimate the energy production a priori. The models’ uncertainty over forest, however, is much greater than over places without forest.

The central questions of the project are:

  • Are wind turbines in forest generally exposed to larger loads than standards from the International Electrotechnical Commission (IEC) prescribe?
  • Is energy production affected by increased turbulence and vertical wind change and can we estimate these effects?
  • Can we improve the description of the turbulence for wind turbine load calculations?
  • How does inhomogeneous forest affect key parameters needed in wind simulation models?
  • Can one use advanced Large Eddy Simulation (LES) models to investigate these issues?

The project aims at a better assessment of how a wind farm in forest produces electricity and what loads the wind turbines experience. Another aim is to reduce wind simulation model uncertainty over forest, which is much greater than over places without forest.

Specific aims are:

  1. Increased knowledge of wind and turbulence conditions up to about 200 m above typical Swedish forests through measurements and model simulations
  2. Implement Large-Eddy simulations (LES) to increase understanding of turbulence over forests
  3. Increase understanding of turbulence and wind conditions across homogeneous and non-homogeneous forests through model simulations
  4. Quantify the effect of forest wind and turbulence on wind-turbine energy production and loads.


Existing models for wind resource and load calculation are further developed. An LES-model is coupled to a mesoscale model (= high resolution weather prediction model) for studying turbulence over forest. Production data from existing wind farms in forests is analyzed along with meteorological data. Additional wind and turbulence measurements are made with a 180 m high mast to gain further knowledge on the effect of forest on the wind and turbulence field.

This includes:

  • Using data from airborne laser scans to produce maps of roughness, zero plane offset and leaf area density with very high resolution
  • Developing methodology for linking a LES model to a mesoscale model
  • Developing better description of turbulence for load calculations (through better models of synthetic turbulence)

Wind Power, Forest, Turbulence, Wind Turbine Loads, Mesoscale Modeling, LES Modeling, Airborne Laser Scanning of Forest

Research Group

Project leader
Dr. Matthias Mohr

Dept. Earth Sciences, Uppsala University

Other project members

Dr. Johan Arnqvist, postdoc

Dept. Earth Sciences, Uppsala University

Dr. Hans Bergström

Dept. Earth Sciences, Uppsala University

Dr. Antonio Segalini

Dept. Mechanics, KTH Royal Institute of Technology

Prof. Henrik Alfredsson

Dept. Mechanics, KTH Royal Institute of Technology

Dr. Stefan Söderberg

WeatherTech Scandinavia AB

Mr. Magnus Baltscheffsky

WeatherTech Scandinavia AB

Dr. Hamidreza Abedi

Dept. Applied Mechanics, Chalmers University of Technology

Prof. Lars Davidson

Dept. Applied Mechanics, Chalmers University of Technology

Mr. Ingemar Carlén

TG Teknikgruppen AB

Dr. Ebba Dellvik

Dept. Wind Energy, Technical University of Denmark

Links and references

Project homepage:

Installation of mast video:

Homepage of planned wind farm:

Previous project – final report:

[1] Arnqvist, J. (2015): Mean Wind and Turbulence Conditions in the Boundary Layer above Forests. Digital Comprehensive Summaries of Uppsala Dissertations from the Facilty of Science and Technology 1212. Se

[2] Nebenführ, B. (2015): Turbulence-resolving simulations for engineering applications. Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie nr. 3935. Se

[3] A. Segalini, J. Arnqvist, I. Carlén, H. Bergström & P. H. Alfredsson (2015): A spectral model of stably stratified surface-layer turbulence. Submitted to Wake Conference 2015 in Visby, Sweden.

[4] Chougule, A., Mann J., Segalini A., Dellwik E , (2014):Spectral tensor parameters for wind turbine load modeling from forested and agricultural landscapes , Wind Energy, Wiley Online Library DOI: 10.1002/we.1709.

[5] Belcher, S.E., Jerram, N. and Hunt, J. C. R. (2003): Adjustment of a turbulent boundary layer to a canopy of roughness elements, J. Fluid Mech. 488, 369-398

[6] Nakamura, T. (2014): Effects of a Forest Clearing: Experimental and Numerical Assessment. Master Thesis. School of Science for Open and Environmental Systems, Graduate School of Science and Tecnology, Keio University, Japan.

[7] Nebenführ, B. and L. Davidsson (2016): Prediction of wind-turbine fatigue loads in forest regions based on turbulent LES inflow fields. Wind Energy (DOI: 10.1002/we.2076).

[8] Ebenhoch, R., Mura, B., Dahlberg, J.-Å., Berkesten Hägglund, P. and Segalini, A. (2016): A linearised numerical model of wind-farm flows, Wind Energy (DOI: 10.1002/we.2067).

[9] Segalini, A., Nakamura, T. and Fukagata, K. (2016): A linearised k − ϵ model of forest canopies and clearings, Bound.-Lay. Meteor. (DOI 10.1007/s10546-016-0190-5).

[10] Nebenführ, B. and L. Davidson (2015): Large-Eddy Simulation Study of Thermally Stratified Canopy Flow. Boundary-Layer Meteorology, Vol. 156, no. 2, 253-276.

[11] J. Mann, N. Angelou, J. Arnqvist, D. Callies, E. Cantero, R. Chávez Arroyo, M. Courtney, J. Cuxart, E. Dellwik, J. Gottschall, S. Ivanell, P. Kühn, G. Lea, J. C. Matos, J. M. L. M. Palma, L. Pauscher, A. Peña, J. Sanz Rodrigo, S. Söderberg, N. Vasiljevic and C. Veiga Rodrigues: Complex terrain experiments in the New European Wind Atlas, Phil. Trans. R. Soc. A 375, 20160101.

[12] A. Segalini (2017), Linearised simulation of the flow over wind farms and complex terrains, Phil. Trans. R. Soc. A 375, 20160099.

[13] P. H. Alfredsson & A. Segalini (2017), Wind farms in complex terrains: an introduction, Phil. Trans. R. Soc. A 375, 20160096.

[14] A. Hyvärinen & A. Segalini (2017), Effects from complex terrain on wind-turbine performance, J. Energy Resour. Technol. 139, September 2017.

[15] E. Dahl, AQ510 Wind Finder full classification according to IEC 61400-12-1: 2017, AQ System Internal Report No. AQS 510-007-008-04, Issue B, Status Release 2, Issue Date 2017-04-12. [Order via]

[16] Mohr, M., W. Jayawardena, J. Arnqvist and H. Bergström (2014): Wind energy estimation over forest canopies using WRF mesoscale model, Proceedings of EWEA Annual Event, Barcelona, Spanien, 10-13 March 2014.

Additional funding (apart from StandUp for Energy)

Vindforsk (now part of Energiforsk AB) (18% funding)

Swedish Energy Authority (42% funding)

In-kind contributions (industry and universities) (40% funding)

Fig 2 – 180 m high mast at Hornamossen (Mullsjö municipality) erected in collaboration with wind power developer OX2. Measures wind and turbulence at several heights.

Fig 3 – Gunnar Bergström and Johan Arnqvist at 40 m height in the mast for installing a webcam.

Fig 4 – LES (Large Eddy Simulation) model result over forest showing snapshot of west-east wind velocity at the fraction of a second. Turbulence can clearly be seen causing wind speeds to vary a lot.