Mesoscale to microscale wind farm flow modeling and evaluation

Wiley Interdisciplinary Reviews: Energy and Environment

Volumn 6, Issue 2, 2017, Pages

  Sanz Rodrigo, Javier ^a   Chávez Arroyo, Roberto Aurelio ^a   Moriarty, Patrick ^b   Churchfield, Matthew ^b   Kosović, Branko ^c   Réthoré, Pierre Elouan ^d   Hansen, Kurt Schaldemose ^d   Hahmann, Andrea ^d   Mirocha, Jeffrey D ^e   Rife, Daran ^f  

^a No 7   (Spain)

^b NATIONAL RENEWABLE ENERGY LABORATORY   (United States)

^c NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^d TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^e LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^f DNV GL   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

ATMOSPHERIC BOUNDARY LAYER; ATMOSPHERIC MOVEMENTS; ATMOSPHERIC THERMODYNAMICS; ATMOSPHERIC TURBULENCE; BOUNDARY LAYER FLOW; BOUNDARY LAYERS; BRIDGES; ELECTRIC UTILITIES; GEOPHYSICS; UNCERTAINTY ANALYSIS; WIND POWER; WIND TURBINES;

ATMOSPHERIC RESEARCH; BOUNDARY LAYER STRUCTURE; COMPREHENSIVE EVALUATION; EXPERIMENTAL REQUIREMENTS; METEOROLOGICAL MODELS; SURFACE BOUNDARY-LAYER; SYSTEMATIC EVALUATION; UNCERTAINTY QUANTIFICATIONS;

STRUCTURAL DESIGN;

EID: 84984674363     PISSN: 20418396     EISSN: 2041840X     Source Type: Journal    
DOI: 10.1002/wene.214     Document Type: Article

References (198)

1. Landberg L. Meteorology for Wind Energy: An Introduction. West Sussex, United Kingdom: Wiley; 2015, 224. ISBN: 978-1-118-91344-4.
2. Emeris S. Wind Energy Meteorology: Atmospheric Physics for Wind Power Generation. Springer-Verlag Berlin Heidelberg; 2013, 212, ISBN-10: 3642305229.
3. IRENA. REmap 2030: a renewable energy roadmap. IRENA Report, Abu Dhabi, June 2014. Available at: http://www.irena.org/remap/REmap_Report_June_2014.pdf. (Accessed November, 2015).
4. Poore RZ, Ostridge C, Geer T, Jones SR. Wind power performance white paper: actual versus predicted: 2014 update. DNV GL Report RANA-WP-01, July 2014.
5. Shaw WJ, Lundquist JK, Schreck SJ. Workshop on research needs for wind resource characterization. Bull Am Meteorol Soc 2009, 90:535–538.
6. Sanz Rodrigo J, Moriarty P. WAKEBENCH model evaluation protocol for wind farm flow models. 1st ed. IEA Task 31 Report to the IEA-Wind Executive Committee, May 2015.
7. Britter R, Schatzmann M. Model evaluation guidance and protocol document. COST Action 732, © COST Office, distributed by University of Hamburg, 2007, 28.
8. AIAA. Guide for the verification and validation of computational fluid dynamics simulations. AIAA-G-077-1998, American Institute of Aeronautics and Astronautics, Reston, VA, 1998.
9. Oberkampf WL, Trucano TG. Verification and validation in computational fluid dynamics. Prog Aerosp Sci 2002, 38:209–272.
10. Oberkampf WL, Trucano TG, Hirsch C. Verification, validation, and predictive capability in computational engineering and physics. Appl Mech Rev 2004, 57:345–384.
11. Oberkampf WL, Barone MF. Measures of agreement between computation and experiment: validation metrics. J Comput Phys 2006, 217:5–36.
12. Pitch M, Trucano T, Moya J, Froehlich G, Hodges A, Peercy D. Guidelines for Sandia ASCI verification and validation plans—content and format: version 2.0. Sandia Report No. SAND2000-3101, January 2001.
13. Hills RG, Maniaci DC, Naughton JW. V&V framework. Sandia Report No. SAND2015-7455, September 2015.
14. IEC 61400–1. Wind turbines part 1: design requirments. 3rd ed. IEC 61400–1:2005(E), International Electrotechnical Commisssion, 2005.
15. Sanz Rodrigo J, Frias L, Stoffels N, von Bremen L. Wind power predictability assessment from large to local scale. In: European Wind Energy Conference (EWEA), Vienna, Austria, February 2013.
16. EWEA. The Economics of Wind Energy. Brussels: European Wind Energy Association; 2009, 155.
17. Hale E. The uncertainty of uncertainty. In: Wind Energy Systems Engineering Workshop 2015. Boulder, CO: University of Colorado Boulder, 2015.
18. Bailey BH, Kunkel J. The financial implications of resource assessment uncertainty. NAW1504, North American Wind Power, 2015 (published online).
19. Natarajan A. An overview of the state of the art technologies for multi-MW scale offshore wind turbines and beyond. Wiley Interdiscip Rev Energy Environ 2014, 3:111–121. doi:10.1002/wene.80.
20. Krogsæter O, Reuder J. Validation of boundary layer parameterization schemes in the Weather Research and Forecasting (WRF) model under the aspect of offshore wind energy applications—part II: boundary layer height and atmospheric stability. Wind Energy 2015, 18:1291–1302. doi:10.1002/we.1765.
21. Monin AS, Obukhov AM. Basic laws of turbulent mixing in the atmospheric surface layer. Trudy Geofiz Inst Acad Sci USSR 1954, 24:163–187.
22. Porté-Agel F, Lu H, Wu Y-T. Interaction between large wind farms and the atmospheric boundary layer. Proc IUTAM 2015, 10:307–318. doi:10.1016/j.piutam.2014.01.026.
23. Petersen EL, Troen I. Wind conditions and resource assessment. Wiley Interdiscip Rev Energy Environ 2012, 1:206–217. doi:10.1002/wene.4.
24. Watson S. Quantifying the variability of wind energy. Wiley Interdiscip Rev Energy Environ 2014, 3:330–342. doi:10.1002/wene.95.
25. Wyngaard JC. Toward numerical modeling in the “Terra Incognita”. J Atmos Sci 2004, 61:1816–1826. doi:10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2.
26. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J R Meteorol Soc 2011, 137:553–597. doi:10.1002/qj.828.
27. Hamill TM, Whitaker JS, Mullen SL. Reforecasts: an important dataset for improving weather predictions. Bull Am Meteorol Soc 2006, 87:33–46. doi:10.1175/2008bams2725.1.
28. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG. A description of the advanced research WRF version 3. Technical Note NCAR/TN-475+STR, NCAR, Boulder, CO, June 2008.
29. Xue M, Droegemeier KK, Wong V, Shapiro A, Brewster K, Carr F, Weber D, Liu Y, Wang D-H. The advanced regional prediction system (ARPS)—a multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: model physics and applications. Meteorol Atmos Phys 2001, 76:134–165. doi:10.1007/s007030170027.
30. Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA, Grasso LD, Nichols ME, Moran MD, Wesley DA, Lee TJ, et al. A comprehensive meteorological modeling system—RAMS. Meteorol Atmos Phys 1992, 49:69–91. doi:10.1007/BF01025401.
31. Chen S, Cummings J, Doyle J, Hodur R, Holt T, Liou C-S, Liu M, Ridout J, Schmidt J, Thompson W, et al. COAMPS Version 3 Model Description—General Theory and Equations. Monterey, CA: Naval Research Laboratory; 2003.
32. NOAA. The high-resolution rapid refresh (HRRR). Available at: http://rapidrefresh.noaa.gov/hrrr/. (Accessed October, 2015).
33. Park S-H, Klemp JB, Skamarock WC. A comparison of mesh refinement in the global MPAS-A and WRF models using an idealized normal-mode baroclinic wave simulation. Mon Weather Rev 2014, 142:3614–3634. doi:10.1175/MWR-D-14-00004.1.
34. Draxl C, Hahmann AN, Peña A, Giebel G. Evaluating winds and vertical wind shear from weather research and forecasting model forecasts using seven planetary boundary layer schemes. Wind Energy 2014, 17:39–55. doi:10.1002/we.1555.
35. Kleczek MA, Steeveneveld GL, Holtslag AAM. Evaluation of the weather research and forecasting mesoscale model for GABLS3: impact on boundary-layer schemes, boundary conditions and spin-up. Bound-Layer Meteorol 2014, 152:213–243. doi:10.1007/s10546-014-9925-3.
36. Hahmann AN, Lennard C, Badger J, Vincent CL, Kelly MC, Volker PJH, Brendan A, Nielsen JR. Mesoscale modeling for the Wind Atlas of South Africa (WASA) project. DTU Wind Energy No. 0050 (updated), February 2015.
37. Sanz Rodrigo J, Lozano GS, Fernandes Correia PM, Cantero Nouqueret E, García Hevia B, Stathopoulos C, Borbón F, Chávez Arroyo RA, Gancarski P, Koblitz T, Barranger N, Conan B. Benchmarking of wind resource assessment flow models: the Alaiz complex terrain test case. Deliverable D26 of the FP7-WAUDIT project, grant agreement number 238576, October 2013.
38. Giebel G. The state-of-the-art in short-term prediction of wind power: a literature overview. 2nd ed. Deliverable D-1.2 of the ANEMOS.plus project, January 2011.
39. Tammelin B, Vihma T, Atlaskin E, Badger J, Fortelius C, Gregow H, Horttanainen M, Hyvönen R, Kilpinen J, Latikka J, et al. Production of the Finnish Wind Atlas. Wind Energy 2013, 16:19–35. doi:10.1002/we.517.
40. Draxl C, Hodge B-M, Clifton A. Overview and meteorological validation of the wind integration national dataset toolkit. NREL Technical Report No. NREL/TP-5000-61740, April 2015.
41. Rife DL, Vanvyve E, Pinto JO, Monaghan AJ, Davis CA, Poulos GS. Selecting representative days for more efficient dynamical climate downscaling: application to wind energy. J Appl Meteorol Climatol 2013, 52:47–63. doi:10.1175/JAMC-D-12-016.1.
42. Badger J, Frank H, Hahmann AN, Giebel G. Wind-climate estimation based on mesoscale and microscale modeling: statistical–dynamical downscaling for wind energy applications. J Appl Meteorol Climatol 2014, 53:1901–1919. doi:10.1175/JAMC-D-13-0147.1.
43. Hahmann AN, Vincent CL, Peña A, Lange J, Hasager CB. Wind climate estimation using WRF model output: method and model sensitivities over the sea. Int J Climatol 2014, 35:3422–3439. doi:10.1002/joc.4217.
44. Jiménez PA, Dudhia J. Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J Appl Meteorol Climatol 2012, 51:300–316. doi:10.1175/JAMC-D-11-084.1.
45. Jiménez PA, Dudhia J. On the ability of the WRF model to reproduce the surface wind direction over complex terrain. J Appl Meteorol Climatol 2013, 52:1610–1617. doi:10.1175/JAMC-D-12-0266.1.
46. Fitch A, Olson J, Lundquist J. Parametrization of wind farms in climate models. J Clim 2013, 26:6439–6458. doi:10.1175/JCLI-D-12-00376.1.
47. Jiménez PA, Navarro J, Palomares AM, Dudhia J. Mesoscale modeling of offshore wind turbine wakes at the wind farm resolving scale: a composite-based analysis with the weather research and forecasting model over Horns Rev. Wind Energy 2014, 18:559–566. doi:10.1002/we.1708.
48. Volker PJH, Badger J, Hahmann AN, Ott S. The Explicit Wake Parametrisation V1.0: A wind farm parametrization in the mesoscale model WRF. Geosci Model Dev Discuss 2015, 8:3715–3731. doi:10.5194/gmd-8-3715-2015.
49. Leutbecher M, Palmet TN. Ensemble forecasting. J Comput Phys 2008, 227:3515–3539. doi:10.1016/j.jcp.2007.02.014.
50. Buizza R, Houtekamer PL, Pellerin G, Toth Z, Zhu Y, Wei M. A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon Weather Rev 2005, 133:1076–1097. doi:10.1175/mwr2905.1.
51. Johnson C, Bowler N. On the reliability and calibration of ensemble forecasts. Mon Weather Rev 2009, 137:1717–1720. doi:10.1175/2009mwr2715.1.
52. Berner J, Ha S-Y, Hacker JP, Fournier A, Snyder C. Model uncertainty in a mesoscale ensemble prediction system: stochastic versus multiphysics representations. Mon Weather Rev 2011, 139:1972–1995. doi:10.1175/2010MWR3595.1.
53. Vanvyve E, Delle ML, Rife D, Monaghan A, Pinto J. Wind resource estimates with an analog ensemble approach. Renew Energy 2015, 74:761–773. doi:10.1016/j.renene.2014.08.060.
54. Landberg L, Riffe D. A science-based commerical look at meso-scale modelling. In: European Wind Energy Conference (EWEA), Barcelona, Spain, March 2014.
55. Poli P. ECMWF global reanalyses: resources for the wind energy community (and a few myth-busters). In: EWEA Wind Resource Assessment Technology Workshop 2013, Dublin, Ireland, June 2013.
56. Measnet. MEASNET Procedure: Evaluation of Site-Specific Wind Conditions, Version 1. MEASNET; 2009.
57. Clifton A, Courtney M. Ground-based vertically profiling remote sensing for wind resource assessment. 1st ed. Expert Group Study on Recommended Practices, 15, International Energy Agency, January 2013.
58. Sanz Rodrigo J, Cantero E, García B, Borbón F, Irigoyen U, Lozano S, Fernandes P-M, Chávez RA. Atmospheric stability assessment for the characterization of offshore wind conditions. J Phys Conf Ser 2015, 625:012044. doi:10.1088/1742-6596/625/1/012044.
59. Hansen K. Guideline for qualification of SCADA data for wake efficiency analysis. In: Sanz Rodrigo J, Moriarty P, eds. WAKEBENCH Best Practice Guidelines for Wind Farm Flow Models. IEA-Wind Task 31. 1 ed. 2015.
60. Coleman GN. Similarity statistics from a direct numerical simulation of the neutrally stratified boundary layer. J Atmos Sci 1999, 56:891–900. doi:10.1175/1520-0469(1999)056<0891:ssfadn>2.0.co;2.
61. Mellor GL, Yamada T. A hierarchy of turbulence closure models for planetary boundary layers. J Atmos Sci 1972, 31:1791–1806. doi:10.1175/1520-0469(1974)031<1791:ahotcm>2.0.co;2.
62. Holt T, Raman S. A review of comparative evaluation of multilevel boundary layer parameterizations for first-order and turbulent kinetic energy closure schemes. Rev Geophys 1988, 26:761–780. doi:10.1029/rg026i004p00761.
63. Weng W, Taylor PA. On modelling the one-dimensional atmospheric boundary layer. Bound-Layer Meteorol 2003, 107:371–400. doi:10.1016/0004-6981(83)90127-0.
64. Sogachev A, Kelly M, Leclerc MY. Consistent two-equation modelling for atmospheric research: buoyancy and vegetation implementations. Bound-Layer Meteorol 2012, 145:307–327. doi:10.1007/s10546-012-9726-5.
65. Gibson MM, Launder BE. Ground effects on pressure fluctuations in the atmospheric boundary layer. J Fluid Mech 1978, 86:491–511. doi:10.1017/s0022112078001251.
66. Lilly DK. The representation of small-scale turbulence in numerical simulation experiments. NCAR Manuscript No. 281, 1966.
67. Piomelli U, Balaras E. Wall-layer models for large-eddy simulations. Annu Rev Fluid Mech 2002, 34:349–374. doi:10.1016/j.paerosci.2008.06.001.
68. Spalart PR, Jou W-H, Strelets M, Allmaras SR. Comments on the feasibility of LES for wings, and on a hybrid RANS/LES approach. In: 1st AFOSR International Symposium on Engineering Turbulence Modelling and Measurements, Corsica, France, May 1997.
69. Haupt SE, Zajaczkowski FJ, Peltier LJ. Detached eddy simulation of atmospheric flow about a surface mounted cube at high reynolds number. J Fluids Eng 2011, 133:31002. doi:10.1115/1.4003649.
70. Deardorff JW. Preliminary results from numerical integration of the unstable planetary boundary layer. J Atmos Sci 1970, 27:1209–1211. doi:10.1175/1520-0469(1970)027<1209:prfnio>2.0.co;2.
71. Mason PJ, Derbyshire SH. Large-eddy simulation of the stably-stratified atmospheric boundary layer. Bound-Layer Meteorol 1990, 53:117–162. doi:10.1007/bf00122467.
72. Kosovic B, Curry JA. A large eddy simulation study of a quasi-steady, stably stratified atmospheric boundary layer. J Atmos Sci 2000, 57:1052–1068. doi:10.1175/1520-0469(2000)057<1052:ALESSO>2.0.CO;2.
73. Cuxart J, Holtslag AAM, Beare RJ, Bazile E, Beljaars A, Cheng A, Conangla L, Ek M, Freedman F, Hamdi R, et al. Single-column model intercomparison for a stably stratified atmospheric boundary layer. Bound-Layer Meteorol 2006, 118:273–303.
74. Beare RJ, Macvean MK, Holtslag AAM, Cuxart J, Esau I, Golaz J-C, Jimenez MA, Khairoutdinov M, Kosovic B, Lewellen D, et al. An intercomparison of large-eddy simulations of the stable boundary layer. Bound-Layer Meteorol 2006, 118:247–272. doi:10.1007/s10546-004-2820-6.
75. Sanz Rodrigo J, Anderson P. Investigation of the stable atmospheric boundary layer at Halley Antarctica. Bound-Layer Meteorol 2013, 148:517–539. doi:10.1007/s10546-013-9831-0.
76. Svensson G, Holtslag AAM, Kumar V, Mauritsen T, Steeneveld GJ, Angevine WM, Bazile E, Beljaars A, de Bruijn EIF, et al. Evaluation of the diurnal cycle in the atmospheric boundary layer over land as represented by a variety of single column models—the second GABLS experiment. Bound-Layer Meteorol 2011, 140:177–206. doi:10.1007/s10546-011-9611-7.
77. Bosveld FC, Baas P, vanMeijgaard E, de Bruijn EIF, Steeneveld GJ, Holtslag AAM. The third GABLS intercomparison case for evaluation studies of boundary-layer models, part A: case selection and set-up. Bound-Layer Meteorol 2014, 152:133–156. doi:10.1007/s10546-014-9917-3.
78. Bosveld FC, Baas P, Steeneveld GJ, Holtslag AAM, Angevine WM, Bazile E, de Bruin EIF, Deacu D, Edwards JM, Michael EK, et al. The third GABLS intercomparison case for evaluation studies of boundary-layer models, part B: results and process understanding. Bound-Layer Meteorol 2014, 152:157–187. doi:10.1007/s10546-014-9919-1.
79. Holtslag AAM. Introduction to the third GEWEX atmospheric boundary layer study (GABLS3). Bound-Layer Meteorol 2014, 152:127–132. doi:10.1007/s10546-014-9931-5.
80. Holtslag AAM, Svensson G, Baas P, Basu S, Beare B, Beljaars ACM, Bosveld FC, Cuxart J, Lindvall J, Steeneveld GJ, et al. Stable atmospheric boundary layers and diurnal cycles—challenges for weather and climate models. Bull Am Meteorol Soc 2013, 94:1691–1706. doi:10.1175/bams-d-11-00187.1.
81. Sherman CA. A mass-consistent model for wind fields over complex terrain. J Clim Appl Meteorol 1978, 17:312–319. doi:10.1175/1520-0450(1978)017<0312:AMCMFW>2.0.CO;2.
82. Guo X, Palutikof JP. A study of two mass-consistent models: problems and possible solutions. Bound-Layer Meteorol 1990, 53:303–332. doi:10.1007/BF02186092.
83. Jackson PS, Hunt JCR. Turbulent wind flow over a low hill. Q J R Meteorol Soc 1975, 101:929–955. doi:10.1002/qj.49710143015.
84. Troen I, Petersen EL. European Wind Atlas. Roskilde: Risø National Laboratory; 1989, 656. ISBN: 87-550-1482-8.
85. Palma JMLM, Castro FA, Ribeiro LF, Rodrigues AH, Pinto AP. Linear and nonlinear models in wind resource assessment and wind turbine micro-siting in complex terrain. J Wind Eng Ind Aerodyn 2008, 96:2308–2326. doi:10.1016/j.jweia.2008.03.012.
86. Koblitz T, Bechmann A, Berg J, Sogachev A, Sørensen N, Réthoré P-E. Atmospheric stability and complex terrain: comparing measurements and CFD. J Phys Conf Ser 2014, 555:012060. doi:10.1088/1742-6596/555/1/012060.
87. Silva LA, Palma JMLM, Castro FA. Simulation of the Askervein flow, part 2: large-eddy simulations. Bound-Layer Meteorol 2007, 125:85–108. doi:10.1007/s10546-007-9195-4.
88. Bechmann A, Sørensen NN. Hybrid RANS/LES applied to complex terrain. Wind Energy 2010, 13:36–50. doi:10.1002/we.414.
89. Diebold M, Higgins C, Fang J, Bechmann A, Parlange MB. Flow over hills: a large-eddy simulation of the Bolund case. Bound-Layer Meteorol 2013, 148:177–194. doi:10.1007/s10546-013-9807-0.
90. Wood N. Wind flow over complex terrain: a historical perspective and the prospect for large-eddy modelling. Bound-Layer Meteorol 2000, 96:11–32. doi:10.1023/A:1002017732694.
91. Moeng C-H, Dudhia J, Klemp J, Sullivan P. Examining two-way grid nesting for large eddy simulation of the PBL using the WRF model. Mon Weather Rev 2007, 135:2295–2311. doi:10.1175/mwr3406.1.
92. Bechmann A, Sørensen NN, Berg J, Mann J, Réthoré P-E. The Bolund experiment, part II: blind comparison of microscale flow models. Bound-Layer Meteorol 2011, 141:245–271. doi:10.1007/s10546-011-9637-x.
93. Richards PJ, Hoxey R. Appropriate boundary conditions for computational wind engineering models using the k–ε turbulence model. J Wind Eng Ind Aerodyn 1993, 46–47:145–153. doi:10.1016/b978-0-444-81688-7.50018-8.
94. Zhang J, Yang Q, Li QS. Developments and applications of a modified wall function for boundary layer flow simulations. Wind Struct 2013, 17:361–377. doi:10.12989/was.2013.17.4.361.
95. Koblitz T. CFD modelling of non-neutral atmospheric boundary layer conditions. PhD Thesis, Technical University of Denmark, DTU Wind Energy PhD-0019 (EN), July 2013, 97.
96. Taylor P, Teunissen H. The Askervein hill project: overview and background data. Bound-Layer Meteorol 1987, 39:15–39. doi:10.1007/bf00121863.
97. Xu D, Taylor PA. A non-linear extension of the mixed spectral finite difference model for neutrally stratified boundary-layer flow over topography. Bound-Layer Meteorol 1992, 59:177–186. doi:10.1007/bf00120693.
98. Castro FA, Palma JMLM, Silva LA. Simulation of the Askervein hill flow, part I: Reynolds averaged Navier-Stoker equations (k-ε turbulence model). Bound-Layer Meteorol 2003, 107:501–530. doi:10.1023/A:1022818327584.
99. Undheim O, Anderson HI, Berge E. Non-linear, microscale modelling of the flow over Askervein hill. Bound-Layer Meteorol 2006, 120:477–495. doi:10.1007/s10546-006-9065-5.
100. Chow FK, Street RL. Evaluation of turbulence closure models for large-eddy simulation over complex terrain: flow over Askervein hill. J Appl Meteorol Climatol 2009, 48:1050–1065. doi:10.1175/2008jamc1862.1.
101. Berg J, Mann J, Bechmann A, Courtney MS, Jørgensen HE. The Bolund experiment, part I: flow over a steep, three-dimensional hill. Bound-Layer Meteorol 2011, 140:1–25.
102. Finnigan JJ, Shaw RH, Patton EG. Turbulence structure above a vegetation canopy. J Fluid Mech 2009, 637:387–424. doi:10.1017/s0022112009990589.
103. Finnigan JJ. Turbulence in plant canopies. Annu Rev Fluid Mech 2000, 32:519–571. doi:10.1146/annurev.fluid.32.1.519.
104. Sanz Rodrigo J, van Beeck J, Dezsö-Weidinger G. Wind tunnel simulation of the wind conditions inside bidimensional forest clear-cuts: application to wind turbine siting. J Wind Eng Ind Aerodyn 2007, 95:609–634. doi:10.1016/j.jweia.2007.01.001.
105. Bergström H, Alfredsson H, Arnqvist J, Carlén I, Dellwik E, Fransson J, Ganander H, Mohr M, Segalini A, Söderberg S. Wind power in forests: wind and effects on loads. Vindforsk Rapport, Elforsk AB, 2013, 167.
106. Belcher SE, Harman IN, Finnigan JJ. The wind in the willows: flows in forest canopies in complex terrain. Annu Rev Fluid Mech 2012, 44:479–504. doi:10.1146/annurev-fluid-120710-101036.
107. Sogachev A, Panferof O. Modification of two-equation models to account for plant drag. Bound-Layer Meteorol 2006, 121:229–266. doi:10.1007/s10546-006-9073-5.
108. Boudreault L-E, Bechmann A, Tarvainen L, Klemedtsson L, Shendryk I, Dellwik E. A LiDAR method of canopy structure for wind modeling of heterogeneous forests. Agric For Meteorol 2015, 201:86–97. doi:10.1016/j.agrformet.2014.10.014.
109. Nebenführ B, Davidson L. Large-eddy simulation study of thermally stratified canopy flow. Bound-Layer Meteorol 2015, 156:253–276. doi:10.1007/s10546-015-0025-9.
110. Ross AN. Large-eddy simulations of flow over forested ridges. Bound-Layer Meteorol 2008, 128:59–76. doi:10.1007/s10546-008-9278-x.
111. Patton EG, Horst TW, Sullivan PP, Lenschow DH, Oncley SP, Brown WOJ, Burns SP, Guenther AB, Held A, Karl T, et al. The canopy horizontal array turbulence study. Bull Am Meteorol Soc 2011, 92:593–611. doi:10.1175/2010BAMS2614.1.
112. Barthelmie RJ, Hansen KS, Pryor SC. Meteorological controls on wind turbine wakes. Proc IEEE 2012, 101:1010–1019. doi:10.1109/JPROC.
113. Crespo A, Hernandez J, Frandsen S. Survey of modeling methods for wind turbine wakes and wind farms. Wind Energy 1999, 2:1–24. doi:10.1002/(sici)1099-1824(199901/03)2:1<1::aid-we16>3.3.co;2-z.
114. España G, Aubrun S, Loyer S, Devinant P. Wind tunnel study of the wake meandering downstream of a modelled wind turbine as an effect of large scale turbulent eddies. J Wind Eng Ind Aerodyn 2012, 101:24–33. doi:10.1016/j.jweia.2011.10.011.
115. Hansen KS, Réthoré P-E, Sørensen JN, Palma J, Hevia BG, Prospathopoulos J, Peña A, Ott S, Schepers G, Palomares A, et al. Simulation of wake effects between two wind farms. J Phys Conf Ser 2015, 625:012008. doi:10.1088/1742-6596/625/1/012008.
116. Christiansen MB, Hasager CB. Wake effects of large offshore wind farms identified from satellite SAR. Remote Sens Environ 2015, 98:251–268. doi:10.1016/j.rse.2005.07.009.
117. Schlez W, Neubert A. New developments in large wind farm modeling. In: European Wind Energy Conference, Marseille, France, March 2009.
118. Barthelmie R, Jensen LE. Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm. Wind Energy 2010, 13:573–586. doi:10.1002/we.408.
119. Nygaard NG. Wakes in very large wind farms and the effect of neighbouring wind farms. J Phys Conf Ser 2014, 524:012162. doi:10.1088/1742-6596/524/1/012162.
120. Frandsen S. On the wind speed reduction in the center of large clusters of wind turbines. J Wind Eng Ind Aerodyn 1992, 39:251–265. doi:10.1016/0167-6105(92)90551-k.
121. Peña A, Rathmann O. Atmospheric stability-dependent infinite wind-farm models and the wake-decay coefficient. Wind Energy 2013, 17:1269–1285. doi:10.1002/we.1632.
122. Calaf M, Meneveau C, Meyers J. Large eddy simulation study of fully developed wind-turbine array boundary layers. Phys Fluids 2010, 22:1–16. doi:10.1063/1.3291077.
123. Andersen SJ, Witha B, Breton S-P, Sørensen JN, Mikkelsen RF, Ivanell S. Quantifying variability of large eddy simulations of very large wind farms. J Phys Conf Ser 2015, 625:012027. doi:10.1088/1742-6596/625/1/012027.
124. Sørensen JN, Mikkelsen RF, Henningson DS, Ivanell S, Sarmast S, Andersen SJ. Simulation of wind turbine wakes using the actuator line technique. Philos Trans A Math Phys Eng Sci 2015, 373:20140071. doi:10.1098/rsta.2014.0071.
125. Réthoré P-E, Fuglsang P, Larsen GC, Buhl T, Larsen TJ, Madsen HA. TOPFARM: multi-fidelity optimization of wind farms. Wind Energy 2014, 17:1797–1816. doi:10.1002/we.1667.
126. Vermeer LJ, Sørensen JN, Crespo A. Wind turbine wake aerodynamics. Prog Aerosp Sci 2003, 39:467–510.
127. Sanderse B, van der Pijl SP, Koren B. Review of CFD for wind-turbine wake aerodynamics. Wind Energy 2010, 14:799–819. doi:10.1002/we.458.
128. Ainslie JF. Calculating the flowfield in the wake of wind turbines. J Wind Eng Ind Aerodyn 1988, 27:213–224. doi:10.1016/0167-6105(88)90037-2.
129. Katic I, Højstrup J, Jensen NO. A simple model for cluster efficiency. In: European Wind Energy Association Conference, Rome, Italy, October 1986
130. Larsen GC, Madsen HA, Bingol F, Mann J, Ott S, Sørensen JN, Okulov V, Troldborg N, Nielsen M, Thomsen K, Larsen TJ, Mikkelsen R. Dynamic wake meandering modelling. Risø-R-1607(EN), Technical University of Denmark, June 2007, 83.
131. Gupta S, Leishman JG. Comparison of momentum and vortex methods for the aerodynamic analysis of wind turbines. In: 24th ASME Wind Energy Symposium 2005, Reno, NV, January 2005.
132. Ott S, Berg J, Nielsen M Linearised CFD Models for wakes. Risø Report Risø-R-1772(EN), Risø National Laboratory, Røskilde, Denmark, December 2011.
133. Crespo A, Hernandez J. A numerical model of wind turbine wake and wind farms. In: European Wind Energy Conference, Rome, Italy, October 1986.
134. Schepers JG, van der Pijl SP. Improved modelling of wake aerodynamics and assessment of new farm control strategies. J Phys Conf Ser 2007, 75:12–39. doi:10.1088/1742-6596/75/1/012039.
135. Sørensen JN, Kock CW. A model for unsteady rotor aerodynamics. J Wind Eng Ind Aerodyn 1995, 58:259–275. doi:10.1016/0167-6105(95)00027-5.
136. Mikkelsen R. Actuator disc methods applied to wind turbines. PhD Thesis, Technical University of Denmark, June 2003, 110.
137. Kasmi AE, Masson C. An extended k-ε model for turbulent flow through horizontal-axis wind turbines. J Wind Eng Ind Aerodyn 2008, 96:103–122. doi:10.1016/j.jweia.2007.03.007.
138. Cabezón D, Migoya E, Crespo A. Comparison of turbulence models for the computational fluid dynamics simulation of wind turbine wakes in the atmospheric boundary layer. Wind Energy 2011, 14:909–921. doi:10.1002/we.516.
139. van der Laan PM, Sørensen NN, Réthoré P-E, Mann J, Kelly MC, Troldborg N, Schepers JG, Machefaux E. An improved k-ε model applied to a wind turbine wake in atmospheric turbulence. Wind Energy 2014, 18:889–907. doi:10.1002/we.1736.
140. Jimenez A, Crespo A, Migoya E, Garcia J. Advances in large-eddy simulation of a wind turbine wake. J Phys Conf Ser 2007, 75:12–41. doi:10.1088/1742-6596/75/1/012041.
141. Troldborg N, Sørensen JN, Mikkelsen R. Actuator line simulation of wake of wind turbine operating in turbulent inflow. J Phys Conf Ser 2007, 75:12–63. doi:10.1088/1742-6596/75/1/012063.
142. Ivanell S, Sørensen JN, Mikkelsen R, Henningson D. Analysis of numerically generated wake structures. Wind Energy 2009, 12:63–80. doi:10.1002/we.285.
143. Wu Y-T, Porté-Agel F. Large-eddy simulation of wind-turbine wakes: evaluation of turbine parameterisations. Bound-Layer Meteorol 2011, 138:345–366. doi:10.1007/s10546-010-9569-x.
144. Curchfield MJ, Lee S, Moriarty PJ, Martínez LA, Leonardi S, Vijayakumar G, Brasseur JG. A large-eddy simulation of wind-plant aerodynamics. In: 50th AIAA Aerospace Sciences Meeting 2012, New York, January 2012.
145. Nilsson K, Ivanell S, Hansen KS, Mikkelsen R, Sørensen JN, Breton S-P, Henningson D. Large-eddy simulations of the Lillgrund wind farm. Wind Energy 2014, 18:449–467. doi:10.1002/we.1707.
146. Larsen GC, Madsen HA, Thomsen K, Larsen TJ. Wake meandering: a pragmatic approach. Wind Energy 2008, 11: 377–395, doi: 10.1002/we.267
147. Bingol F, Mann J, Larsen GC. Light detection and ranging measurements of wake dynamics, part 1: one-dimensional scanning. Wind Energy 2010, 13:51–61. doi:10.1002/we.352.
148. Barlas E, Buckingham S, van Beeck J. Roughness effects on wind-turbine wake dynamics in a boundary-layer wind tunnel. Bound-Layer Meteorol 2016, 158:27–42. doi:10.1007/s10546-015-0083-z.
149. Jafari S, Chokani N, Abhari RS. Simulation of wake interactions in wind farms using an immersed wind turbine model. J Turbomach 2013, 136:061018. doi:10.1115/1.4025762.
150. Hansen K, Larsen GC, Ott S. Dependence of offshore wind turbine fatigue loads on atmospheric stratification. J Phys Conf Ser 2014, 524:012165. doi:10.1088/1742-6596/524/1/012165.
151. Moriarty P, Sanz Rodrigo J, Gankarski P, Chávez Arroyo R, Churchfield M, Naughton JW, Hansen KS, Machefaux E, Maguire E, Castellani F, et al. IEA-Task 31 WAKEBENCH: towards a protocol for wind farm flow model evaluation—part 2: wind farm wake models. J Phys Conf Ser 2014, 524:012185. doi:10.1088/1742-6596/524/1/012185.
152. Gaumond M, Réthoré P-E, Ott S, Peña A, Bechmann A, Hansen KS. Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm. Wind Energy 2014, 17:1169–1178. doi:10.1002/we.1625.
153. Politis E, Prospathopoulos J, Cabezón D, Hansen K, Chaviaropoulos P, Barthelmie R. Modelling wake effects in large wind farms in complex terrain: the problem, the methods and the issues. Wind Energy 2010, 15:161–182. doi:10.1002/we.481.
154. Krogstad P-A, Sætran L. Wind turbine wake interactions: results from blind tests. J Phys Conf Ser 2015, 625:012043. doi:10.1088/1742-6596/625/1/012043.
155. van der Laan MP, Hansen KS, Sørensen NN, Réthoré P-E. Predicting wind farm wake interaction with RANS: an investigation of the Coriolis force. J Phys Conf Ser 2015, 625:012026. doi:10.1088/1742-6596/625/1/012026.
156. Gebraad PMO, Teeuwisse FW, van Wingerden J-W, Fleming PA, Ruben SD, Marden JR, Pao L. Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study. Wind Energy 2014, 19:95–114. doi:10.1002/we.1822.
157. Wilby RL, Wigley TML. Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 1997, 21:530–548. doi:10.1177/030913339702100403.
158. Devis A, van Lipzig NPM, Demuzere M. A new statistical approach to downscale wind speed distributions at a site in northern Europe. J Geophys Res 2013, 118:2272–2283. doi:10.1002/jgrd.50245.
159. Pryor SC, Barthelmie RJ. Empirical downscaling of wind speed probability distributions. J Geophys Res 2005, 110:D19109. doi:10.1029/2005JD005899.
160. García-Bustamante E, Gonález-Rouco JF, Navarro J, Xoplaki E, Luterbacher J, Jiménez PA, Montávez JP, Hidalgo A, Lucio-Eceiza EE. Relationship between wind power production and North Atlantic atmospheric circulation over the northeastern Iberian Peninsula. Clim Dyn 2013, 40:935–949. doi:10.1007/s00382-012-1451-8.
161. Sehnke F, Strunk S, Felder M, Brombach J, Kaifel A, Meis J. Wind power resource estimation with deep neural networks. In: Artificial Neural Networks and Machine Learning—ICANN 2013, 2013, 563–570. Berlin/Heidelberg: Springer.
162. Rogers AL, Rogers JW, Manwell JF. Comparison of four measure-correlate-predict algorithms. J Wind Eng Ind Aerodyn 2005, 93:243–264. doi:10.1016/j.jweia.2004.12.002.
163. Woods JC, Watson SJ. A new matrix method of predicting long-term wind roses with MCP. J Wind Eng Ind Aerodyn 1997, 66:85–94. doi:10.1016/s0167-6105(97)00009-3.
164. Romo A, Amezcua J, Probst O. Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource. J Renew Sustain Energy 2011, 3:023105. doi:10.1063/1.3574447.
165. Wilks DS, Wilby RL. The weather generation game: a review of stochastic weather models. Prog Phys Geogr 1999, 23:329–357. doi:10.1177/030913339902300302.
166. Yan Z, Bate S, Chandler RE, Isham V, Wheater H. Changes in extreme wind speeds in NW Europe simulated by generalized linear models. Theor Appl Climatol 2006, 83:121–137. doi:10.1007/s00704-005-0156-x.
167. Gutiérrez JM, Cofiño AS, Cano R, Rodríguez MA. Clustering methods for statistical downscaling in short-range weather forecasts. Mon Weather Rev 2004, 132:2169–2183. doi:10.1175/1520-0493(2004)132<2169:cmfsdi>2.0.co;2.
168. Chávez-Arroyo R, Lozano GS, Sanz Rodrigo J, Probst O. On the application of principal component analysis for accurate statistical-dynamical downscaling of wind fields. Energy Procedia 2013, 40:67–76. doi:10.1016/j.egypro.2013.08.009.
169. Hewitson BC, Crane RG. Self-organizing maps: applications to synoptic climatology. Clim Res 2002, 22:13–26. doi:10.3354/cr022013.
170. Chávez-Arroyo R, Lozano-Galiana S, Sanz-Rodrigo J, Probst O. Statistical–dynamical downscaling of wind fields using self-organizing maps. Appl Thermal Eng 2014, 75:1201–1209. doi:10.1016/j.applthermaleng.2014.03.002.
171. Delle ML, Eckel FA, Rife DL, Nagarajan B, Searight K. Probabilistic weather prediction with an analog ensemble. Mon Weather Rev 2013, 141:3498–3516. doi:10.1175/mwr-d-12-00281.1.
172. Rife D, Delle ML, Ma J, Whiting R. Computationally efficient dynamical downscaling with an analog ensemble: application to wind resource assessment. In: EWEA Wind Resource Assessment Workshop 2015, Helsinki, Finland, June 2015.
173. Mengelkamp H-T, Kapitza H, Pfluger U. Statistical–dynamical downscaling of wind climatologies. J Wind Eng Ind Aerodyn 1997, 67–68:449–457. doi:10.1016/S0167-6105(97)00093-7.
174. Pinto JG, Neuhaus CP, Leckebusch GC, Reyers M, Kerschgens M. Estimation of wind storm impacts over western Germany under future climate conditions using a statistical–dynamical downscaling approach. Tellus 2010, 62A:188–201. doi:10.1111/j.1600-0870.2009.00424.x.
175. Frank HP, Landberg L. Modelling the wind climate of Ireland. Bound-Layer Meteorol 1997, 85:359–377. doi:10.1023/A:1000552601288.
176. Frey-Buness F, Heimann D, Sausen R. A statistical dynamical downscaling procedure for global climate simulations. Theor Appl Climatol 1995, 50:117–131. doi:10.1007/BF00866111.
177. Avila M, Folch A, Houzeaux G, Eguzkitza B, Prieto L, Cabezón D. A parallel CFD model for wind farms. Procedia Comput Sci 2013, 18:2157–2166. doi:10.1016/j.procs.2013.05.386.
178. Fuentes U, Heimann D. An improved statistical–dynamical downscaling scheme and its application to the Alpine precipitation climatology. Theor Appl Clim 2000, 65:119–135. doi:10.1007/s007040070038.
179. Cutler NJ, Jorgensen BH, Ersboll BK, Badger J. Class generation for numerical wind atlases. Wind Eng 2006, 30:401–415. doi:10.1260/030952406779502704.
180. Martinez YH, Yu W, Lin H. A new statistical–dynamical downscaling procedure based on EOF analysis for regional time series generation. J Appl Meteorol Climatol 2013, 52:935–952. doi:10.1175/JAMC-D-11-065.1.
181. Hahmann AN, Rostkier-Edelstein D, Warner TT, Vandenberghe F, Liu Y, Babarsky R, Swerdlin SP. A reanalysis system for the generation of mesoscale climatographies. J Appl Meteorol Climatol 2010, 49:954–972. doi:10.1175/2009JAMC2351.1.
182. Rife DL, Vanvyve E, Pinto JO, Monaghan AJ, Davis CA, Poulos GS. Selecting representative days for more efficient dynamical climate downscaling: application to wind energy. J Appl Meteorol Climatol 2013, 52:47–63. doi:10.1175/JAMC-D-12-016.1.
183. Zorita E, von Storch H. The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 1999, 12:2474–2489. doi:10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2.
184. Reusch DB, Alley RB, Hewitson BC. Relative performance of self-organizing maps and principal component analysis in pattern extraction from synthetic climatological data. Polar Geogr 2005, 29:188–212. doi:10.1080/789610199.
185. Lundquist KA, Chow FK, Lundquist JK. An immersed boundary method for the weather research and forecasting model. Mon Weather Rev 2010, 138:796–817. doi:10.1175/2009MWR2990.1.
186. Howard T, Clark P. Correction and downscaling of NWP wind speed forecasts. Meteorol Appl 2007, 14:105–116. doi:10.1002/met.12.
187. Gasset N, Landry M, Gagnon Y. A comparison of wind flow models for wind resource assessment in wind energy applications. Energies 2012, 5:4288–4322. doi:10.3390/en5114288.
188. Castro FA, Silva SC, Lopes da Costa JC. One-way mesoscale–microscale coupling for the simulation of atmospheric flows over complex terrain. Wind Energy 2015, 18:1251–1272. doi:10.1002/we.1758.
189. Mirocha JD, Kosović B, Kirkil G. Resolved turbulence characteristics in large-eddy simulations nested within mesoscale simulations using the weather research and forecasting model. Mon Weather Rev 2014, 142:806–831. doi:10.1175/MWR-D-13-00064.1.
190. Muñoz-Esparza D, Kosović B, van Beeck J, Mirocha JD. A stochastic perturbation method to generate inflow turbulence in large-eddy simulation models: application to neutrally stratified atmospheric boundary layers. Phys-Fluids 2015, 27:035102. doi:10.1063/1.4913572.
191. Muñoz-Esparza D, Kosović B, Mirocha JD, van Beeck J. Bridging the transition from mesoscales to microscale turbulence in atmospheric models. Bound-Layer Meteorol 2014, 153:409–440. doi:10.1007/s10546-014-9956-9.
192. Patton EG, Sullivan PP, Davis KJ. The influence of a forest canopy on top-down and bottom-up diffusion in the planetary boundary layer. Q J R Meteorol Soc 2003, 129:1415–1434. doi:10.1256/qj.01.175.
193. Linn R, Winterkamp J, Colman JJ, Edminster C, Balley JD. Modeling interactions between fire and atmosphere in discrete element fuel beds. Int J Wildland Fire 2005, 14:37–48. doi:10.1071/WF04043.
194. Arriola L, Hyman JM. Sensitivity analysis for uncertainty quantification in mathematical models. In: Mathematical and Statistical Estimation Approaches in Epidemiology. Dordrecht Heidelberg, London, & New York, Springer; 2009, 195–247.
195. Aspinall WP, Cooke RM. Risk and uncertainty assessment for natural hazards. In: Quantifying Scientific Uncertainty from Expert Judgement Elicitation. Cambridge: Cambridge University Press; 2013, 64–99.
196. Le Maitre O, Knio OM. Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics. Dordrecht Heidelberg, London, & New York, Springer; 2010, 156.
197. Rasmussen CE, Williams CKI. Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press; 2005.
198. Roy CJ, Oberkampf WL. A comprehensive framework for verification, validation, and uncertainty quantification. Comput Methods Appl Mech Eng 2011, 200:2131–2144. doi:10.1016/j.cma.2011.03.016.