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NEWA Meso-Micro Challenge: Phase 1

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The first results of this benchmark for the Cabauw site have been published in the Torque 2018 conference, 20-22 Jun 2018:


This first phase will be the basis to establish an assessment process for meso-micro wind resource assessment methodologies. To this end, initial datasets from two sites in horizontally homogeneous  conditions are proposed:

  • Cabauw, onshore, and
  • Fino1 offshore

such that single-column models can be used cost-effectively as proxy for 3D RANS models. This will provide a more efficient approach to test statistical methodologies that can be later applied to heterogeneous sites in 3D.

Data Accessibility

Data from Cabauw and Fino1 are provided here reformatted to a common standard based on the original data from, respectively, KNMI's CESAR and BSH'x FINO official web repositories. 


The objectives of the challenge apply in this first phase as follows:

  • Test meso-micro methodologies consistently for two sites and map accuracy vs cost for relevant quantities of interest, notably for annual energy production (AEP) and site suitability parameters.
  • Establish open-access practices for the assessment of these methodologies that can determine the added value of meso-micro with respect to conventional methods based on microscale modeling only.
  • Compare methodologies for uncertainty estimation of gross AEP and discuss their adequacy to the wind resource assessment process used by industry.

Input data

Input data is stored in the following shared folder:


One year of observations from two tall masts are provided in NetCDF format:

  • Cabauw: 200-m mast, year 2006 (51.971ºN, 4.927ºE)
  • Fino-1: 100-m mast, year 2006 (54.0143ºN, 6.5933ºE) 
Elevation and Land Cover

Microscale models shall consider a uniform roughness length for Cabauw of 0.15 m, as in the GABLS3 benchmark. For Fino-1, surface roughness shall depend on wind conditions through the Charnock relation, z0 = Cchu*2/g, with Cch = 0.0062, calibrated in neutral conditions for year 2006 in [1].

Mesoscale Forcing

You are welcome to use your own mesoscale simulations to feed the meso-micro methodology. However, if you only plan to run microscale simulations, for consistency you should use the mesoscale input forcing provided herein.  

Mesoscale input forcing in terms of mesoscale tendencies is provided for the sites following the same methodology of the GABSL3 benchmark case and described in [2]. For each site, a reference WRF configuration based on one-way telescoping nests at 27, 9 and 3 km horizontal resolution, all of them with grid dimensions of 61x61 and 61 vertical levels up to 5000 Pa. The yearly period is integrated based on two-day runs with an additional day for spin-up. Simulations are initialized at 12UTC using ERA-Interim reanalysis data. The U.S. Geological Survey (USGS) land-use surface data, that comes by default with the WRF model, is used together with the unified Noah land-surface model to define the boundary conditions at the surface. Other physical parameterizations used are: the rapid radiative transfer model (RRTM), the Dudhia radiation scheme and the Yonsei University (YSU) first-order PBL scheme.

A NetCDF file for mesoscale data is provided with the following information:

  • Site coordinates and Coriolis parameter
  • Time-height 2D arrays of velocity components (UVW) and potential temperature (Th)
  • Time-height 2D arrays of mesoscale forcings (tendencies): geostrophic wind (Ug, Vg), advective wind (Uadv, Vadv) and advective potential temperature (Thadv)
  • Time array of surface-layer quantities: friction velocity (ust), kinematic heat flux (wt), 2-m temperature (T2), skin temperature (TSK), surface pressure (Psfc)

Units, dimensions and variables description are all provided in the NetCDF file. Momentum tendencies are provided in [m s-1] and should be multiplied by the Coriolis parameter (fc = 0.00115 s-1) to obtain appropriate forces in [m s-2]. For convenience, we have omitted information about humidity since the assumption of dry-atmosphere is typically adopted by wind energy flow models.

Reference Power Curve

The NREL 5 MW reference power curve will be used to evaluate AEP [3]. A text file is provided with other additional wind speed relationships computed by NREL. 

Validation data

The following quantities of interest will be evaluated at the three sites:

  • Horizontal wind speed (S) and direction (WD) distributions at a reference hub-height of 80 m
  • AEPgross (p50, p90) at 80 m using the NREL 5MW reference power curve
  • Velocity and turbulence intensity profiles for 16 wind direction sectors and three stability classes

Stability will be characterized based on the local Obukhov length L using the stability parameter z/L , where L is obtained from sonic anemometer measurements at 3 m in Cabauw and at 40 m in Fino-1. Stability classes are defined as follows:

  • Unstable (u):                   -20 < z/L < -0.2
  • Weakly unstable (wu):   -0.2 < z/L < -0.02
  • Neutral (n):                  -0.02 < z/L < 0.02
  • Weakly stable (ws):      0.02 < z/L < 0.2
  • Stable (s):                      0.2 < z/L < 20

Model runs

Consistent with the philosophy of the challenge, each participant should develop a plan to span the accuracy vs cost figure. For instance:

  • A WRF modeler could run yearly simulations starting from the 3-nest configuration of the reference set-up (or a different one) and add other 3 nests switching to LES down to resolutions of the order of 100 m and provide 6 results, one from each nest, for the 3 sites.
  • A CFD modeler may vary the number of simulations included in the assessment and/or decide to increase resolution or switch to a higher-fidelity turbulence model when switching from planning to design phase.   

For each AEP assessment you should provide the cost in cpu-hr. 

Also adopting the end-user perspective, the simulations may consider how to best use the onsite measurements to calibrate their model-chain to the reference mast. This is equivalent to a conventional micrositing process in the design phase of ensuring that self-prediction at the reference site is free of bias before extrapolating horizontally or vertically to other target prediction sites. Introducing calibration is clear way of distinguishing between planning and design phase in the accuracy vs cost figure although the cpu-time may be roughly the same.

For consistency with the GABLS3 benchmark, microscale models using Sogachev et al. (2012) k-ε turbulence model shall use this set of constants: κ = 0.4, Cε1 = 1.52, Cε2 = 1.833, σk = 2.95, σε = 2.95 and Cμ = 0.03 [4]. 

Output data

Data should be provided in a single NetCDF file per site, as described in the python template based on the reference WRF simulation. Following the example above, the WRF-LES approach should provide 6x2 = 12 files.

Output quantities and dimmensions: 

  • dimmensions: time (t), height above ground (z), zflux height of surface-layer quantities
  • time-height: U, V velocity components, potential temperature (Th), turbulent kinetic energy (TKE) 
  • time: friction velocity (us), kinematic heat flux (wt) and/or Obukhov length (L) at zflux height and 2-m temperature (T2)

To homogenize the output data please consider these indications:

  • time series should be provided based on 10-min or 1-hr averages
  • Vertical profiles should be provided at the simulation levels.
  • A python script is provided to help you figure out the output format. Please respect the naming convention for variables to allow automatic post-processing. 
  • Time series: please follow the same format as the input .nc file, mean profiles will be generated in the post-processing.
  • Mean profiles: format to be defined soon, will be added to the python script 

Please upload your data compressed in the following shared folder: (only upload is alowed, your data is only shared with the benchmark manager)


[1] Sanz Rodrigo J (2011) Flux-profile characterization of the offshore ABL for the parameterization of CFD models. EWEA Offshore 2012 proceedings, Amsterdam, The Netherlands, November 2011

[2] Sanz Rodrigo J, Churchfield M, Kosović B (2017) A methodology for the design and testing of atmospheric boundary layer models for wind energy applications. Wind Energ. Sci. 2: 1-20, doi:10.5194/wes-2-1-2017

[3] Jonkman J, Butterfield S, Musial W and Scott G 2009 Definition of a 5-MW Reference Wind Turbine for Offshore System Development. Technical Report NREL/TP-500-38060, February 2009, available online

[4] Sogachev A., Kelly M., Leclerc M. Y. (2012) Consistent Two-Equation Closure Modelling for Atmospheric Research: Buoyancy and Vegetation Implementations, Bound.-Lay. Meteorol. 145, 307–327, doi:10.1007/s10546-012-9726-5


This challenge was launched with the support from NEWA (FP7-ENERGY.2013.10.1.2, European Commission's grant agreement number 618122) and MesoWake (FP7-PEOPLE-2013-IOF, European Commission’s grant agreement number 624562) EU projects. The benchmark are coordinated within the International Energy Agency IEA-Wind Task 31 "Wakebench". We would like to acknowledge the Royal Netherlands Meteorological Institute (KNMI) and the Federal Maritime and Hydrographic Agency (BSH) for maintaining the CESAR and Fino1 databases.