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Perdigão Publications

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Publications

Barber, S. and coauthors, 2020: IEA Wind Task 31: Design of a new comparison metrics simulation challenge for wind resource assessment in complex terrain Stage 1. J. Phys.: Conf. Ser. 1618 062013
Publications
Barthelmie, R. J. and coauthors, 2018: Wind turbine wake characterization in complex terrain via integrated Doppler lidar data from the Perdigão experiment. J. Phys.: Conf. Ser. 1037 052022. doi :10.1088/1742-6596/1037/5/052022.
Publications
Barthelmie, R. J. and Pryor, S. C.: Automated Wind Turbine Wake Characterization in Complex Terrain, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-461, in review, 2019.
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Barthelmie, R. J. and S C Pryor, 2019: Impact of local meteorology on wake characteristics at Perdigão. J. Phys.: Conf. Ser. 1256 012007
Publications
Bell, T. M., Klein, P., Wildmann, N., and Menke, R.: Analysis of flow in complex terrain using multi-Doppler lidar retrievals, Atmos. Meas. Tech., 13, 1357–1371, https://doi.org/10.5194/amt-13-1357-2020, 2020.
Publications
Bodini, N., Lundquist, J. K., and Optis, M.: Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?, Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, 2020.
Publications
Connolly, A.; van Veen, L.; Neher, J.; Geurts, B.J.; Mirocha, J.; Chow, F.K. Efficacy of the Cell Perturbation Method in Large-Eddy Simulations of Boundary Layer Flow over Complex Terrain. Atmosphere 2021, 12, 55. https://doi.org/10.3390/atmos12010055
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Dar, A. S., Berg, J., Troldborg, N., and Patton, E. G.: On the self-similarity of wind turbine wakes in a complex terrain using large eddy simulation, Wind Energ. Sci., 4, 633–644, https://doi.org/10.5194/wes-4-633-2019, 2019.
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Fernando, H., J. Mann, J. Palma, J. Lundquist, R. Barthelmie, M. BeloPereira, W. Brown, F. Chow, T. Gerz, C. Hocut, P. Klein, L. Leo, J. Matos, S. Oncley, S. Pryor, L. Bariteau, T. Bell, N. Bodini, M. Carney, M. Courtney, E. Creegan, R. Dimitrova, S. Gomes, M. Hagen, J. Hyde, S. Kigle, R. Krishnamurthy, J. Lopes, L. Mazzaro, J. Neher, R. Menke, P. Murphy, L. Oswald, S. Otarola-Bustos, A. Pattantyus, C. Rodrigues, A. Schady, N. Sirin, S. Spuler, E. Svensson, J. Tomaszewski, D. Turner, L. van Veen, N. Vasiljevic, D. Vassallo, S. Voss, N. Wildmann, and Y. Wang, 2019: The Perdigão: Peering into Microscale Details of Mountain Winds. Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-1-0227.1.
Publications
Finnigan, J., Ayotte, K., Harman, I. et al., 2020: Boundary-Layer Flow Over Complex Topography. Boundary-Layer Meteorol 177, 247–313. https://doi.org/10.1007/s10546-020-00564-3
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Hannesdóttir, A. and coauthors, 2020: Can we predict short term extreme conditions from 10-minute data only? J. Phys.: Conf. Ser. 1452 012025
Publications
Letson, F., Barthelmie, R. J., Hu, W., and Pryor, S. C.: Characterizing wind gusts in complex terrain, Atmos. Chem. Phys., 19, 3797-3819, https://doi.org/10.5194/acp-19-3797-2019, 2019.
Publications
Mann, J. and coauthors, 2018: Challenges in using scanning lidars to estimate wind resources in complex terrain. J. Phys.: Conf. Ser. 1037 072017. doi :10.1088/1742-6596/1037/7/072017.
Publications
Menke, R., Vasiljević, N., Hansen, K. S., Hahmann, A. N., and Mann, J.: Does the wind turbine wake follow the topography? A multi-lidar study in complex terrain, Wind Energ. Sci., 3, 681-691, https://doi.org/10.5194/wes-3-681-2018, 2018.
Publications
Menke, R., Vasiljević, N., Mann, J., and Lundquist, J. K.: Characterization of flow recirculation zones at the Perdigão site using multi-lidar measurements, Atmos. Chem. Phys., 19, 2713-2723, https://doi.org/10.5194/acp-19-2713-2019, 2019.
Publications
Menke, R., Vasiljević, N., Wagner, J., Oncley, S. P., and Mann, J.: Multi-lidar wind resource mapping in complex terrain, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-85, in review, 2019.
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Palma, J. M. and coauthors, 2019: Unravelling the wind flow over highly complex regions through computational modeling and two-dimensional lidar scanning. J. Phys.: Conf. Ser. 1222 012006
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Palma, J., C. A. M. Silva, V. C. Gomes, A. S. Lopes, T. Simoes, P. Costa, V. T. P. Batista (2020): The digital terrain model in the computational modelling of the flow over the Perdigão site: the appropriate grid size, Wind Eng. Sci., 5, 1469–1485,
https://doi.org/10.5194/wes-5-1469-2020
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Quimbayo-Duarte, J., J. Wagner, N. Wildmann, T. Gerz, and J. Schmidli, 2022: Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain. A case study using WRF-LES V4.0.1. Geoscientific Model Development, 15(13), 5195-5209, doi: 10.5194/gmd-15-5195-2022.
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Ribeiro, C., Rocha da Silva, J., Aguiar Castro, J., Carvalho Amorim, R., Correia Lopes, J., & David, G. (2018). Research Data Management Tools and Workflows: Experimental Work at the University of Porto. IASSIST Quarterly, 42(2), 1–16. https://doi.org/10.29173/iq925
Publications
Shaw, W. J., Berg, L. K., Cline, J., Draxl, C., Djalalova, I., Grimit, E. P., Lundquist, J. K., Marquis, M., McCaa, J., Olson, J. B., Sivaraman, C., Sharp, J., & Wilczak, J. M. (2019). The Second Wind Forecast Improvement Project (WFIP2): General Overview, Bulletin of the American Meteorological Society, 100(9), 1687-1699.
Publications
Sun, H., X. Gao, and H. Yang, 2020: A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect. Renewable and Sustainable Energy Reviews, 132, doi: 10.1016/j.rser.2020.110042.
Publications
Turner, D. D. and Löhnert, U.: Ground-based Temperature and Humidity Profiling: Combining Active and Passive Remote Sensors, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-352, in review, 2020.
Publications
Vasiljević, N., L. M. Palma, J. M., Angelou, N., Carlos Matos, J., Menke, R., Lea, G., Mann, J., Courtney, M., Frölen Ribeiro, L., and M. G. C. Gomes, V. M., 2017: Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments. Atmos. Meas. Tech., 10, 3463-3483, doi: 10.5194/amt-10-3463-2017.
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Vassallo, D., Krishnamurthy, R., and Fernando, H. J. S.: Decreasing wind speed extrapolation error via domain-specific feature extraction and selection, Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, 2020.
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Vassallo, D., Krishnamurthy, R., and Fernando, H. J. S.: Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error, Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, 2021.
Publications
Vassallo, D., Krishnamurthy, R., Menke, R., & Fernando, H. J. S. (2021). Observations of Stably Stratified Flow through a Microscale Gap, Journal of the Atmospheric Sciences, 78(1), 189-208.
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Wagner, J., Gerz, T., Wildmann, N., and Gramitzky, K.: Long-term simulation of the boundary layer flow over the double-ridge site during the Perdigão 2017 field campaign, Atmos. Chem. Phys., 19, 1129–1146, https://doi.org/10.5194/acp-19-1129-2019, 2019.
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Wildmann N. and coauthors, 2018: Coplanar lidar measurement of a single wind energy converter wake in distinct atmospheric stability regimes at the Perdigão 2017 experiment. 018 J. Phys.: Conf. Ser. 1037 052006
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Wildmann, N. and coauthors, 2020: Long-range Doppler lidar measurements of wind turbine wakes and their interaction with turbulent atmospheric boundary-layer flow at Perdigao 2017. J. Phys.: Conf. Ser. 1618 032034
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Wildmann, N., Bodini, N., Lundquist, J. K., Bariteau, L., and Wagner, J.: Estimation of turbulence dissipation rate from Doppler wind lidars and in situ instrumentation for the Perdigão 2017 campaign, Atmos. Meas. Tech., 12, 6401–6423, https://doi.org/10.5194/amt-12-6401-2019, 2019.
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Wildmann, N., Vasiljevic, N., and Gerz, T.: Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup, Atmos. Meas. Tech., 11, 3801–3814, https://doi.org/10.5194/amt-11-3801-2018, 2018.
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Wise, A. S., J. M. T. Neher, R. S. Arthur, J. D. MIrocha, J. K. Lundquist, and F. K. Chow, 2022: Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain. Wind Energy Science, 7(1), 367-386, doi: 10.5194/wes-7-367-2022.
Publications

Conference Proceedings

Bell, T. and coauthors, 2017: Nocturnal Boundary-Layer Phenomena Observed at a Complex Site During the Perdigão Experiment. AGU 2017 Fall Meeting, A23J-08.
Conference Proceedings
Belo-Pereira, M., 2017: Design of a Data Catalogue for Perdigão-2017 Field Experiment: Establishing the Relevant Parameters, Post-Processing Techniques and Users Access. AGU 2017 Fall Meeting, A21E-0068.
Conference Proceedings
Bodini, N., 2018: Variability of Turbulence Dissipation Rate in Complex Terrain: the Perdigão 2017 Field Campaign. 18th Conference on Mountain Meteorology, 9.3A.
Conference Proceedings
Chow, F. K., 2018: Generation of Turbulent Structures in Nested Simulations for the Perdigao Campaign. 18th Conference on Mountain Meteorology, 9.4A.
Conference Proceedings
Connolly, A. D., 2020: Development of Fine-Scale Structures in Large-Eddy Simulations over Complex Terrain. 19th Conference on Mountain Meteorology, 8.1.
Conference Proceedings
Creegan, E. D. and coauthors, 2017: Interrelationship of Cn2 & Eddy Dissipation rate based on Scintillometer and Doppler Lidar observations in complex terrain during the Perdigao Campaign 2017. AGU 2017 Fall Meeting, A21E-0070.
Conference Proceedings
Dumais, R. E. and coauthors, 2018: High Resolution Simulation Using the VLES Configuration of WRF-ARW of a Low Level Jet Observed over the Vale Do Cobrão during the Perdigao Microscale Field Campaign of 2017. 18th Conference on Mountain Meteorology, 25.
Conference Proceedings
Elsen, K. M. and A. Schady, 2020: Influence of meteorological conditions on sound propagation of a wind turbine in complex terrain. Proc. Mtgs. Acoust. 41, 032001
Conference Proceedings
Gerz, T. and coauthors, 2017: Numerical Simulation of Low-Level Jets during the Perdigão Field Campaign. AGU 2017 Fall Meeting, A21E-0062.
Conference Proceedings
Hocut, C. M. and coauthors, 2018: Mesoscale Flows and Turbulence Measurements on Forested Slope during Peridgão 2017. 18th Conference on Mountain Meteorology, 14.6A.
Conference Proceedings
Kigle, S. and coauthors, 2017: Characterizing a Wind Energy Converter’s Wake in distinct ABL Conditions by means of Long-Range Lidar Measurements in the Context of the Perdigão 2017 Experiment. AGU 2017 Fall Meeting, A21E-0061.
Conference Proceedings
Krishnamurthy, R., 2017: Nocturnal Reversed Flows Above Parallel Ridges in Perdigão, Portugal. AGU 2017 Fall Meeting, A21E-0060.
Conference Proceedings
Lundquist, J. K., 2017: Measurements of Turbulent Kinetic Energy Dissipation Rate in Complex Terrain from the Perdigão Experiment. AGU 2017 Fall Meeting, A23J-06.
Conference Proceedings
Lundquist, J. K., 2018: Challenges in Simulating Boundary Layers and Turbulence in Complex Terrain: Insights from Recent Field Experiments. 18th Conference on Mountain Meteorology, 9.1.
Conference Proceedings
Menke, R. and coauthors, 2018: Characterisation of Recirculation Zones in Complex Terrain Using Multi-Lidar Measurements. 18th Conference on Mountain Meteorology, 9.2A.
Conference Proceedings
Neher, J. and coauthors, 2017: Meso- to micro-scale coupled simulations of flow over complex terrain at the Perdigao site. AGU 2017 Fall Meeting, A23J-05.
Conference Proceedings
Salvadore, J. J. and coauthors, 2017: Boundary Layer Characterization during Perdigão Field Campaign 2017. AGU 2017 Fall Meeting, A21E-0069.
Conference Proceedings
Wise, A. S. and coauthors, 2020: Multi-Scale Modeling of a Wind Turbine Wake Over Complex Terrain in Different Atmospheric Stability Regimes. 19th Conference on Mountain Meteorology, 8.2.
Conference Proceedings

Theses

Reports

Vasiljevic, N. and coauthors, 2018: e-WindLidar: making wind lidar data FAIR. http://ewindlidar.
windenergy.dtu.dk/document/report.pdf
Reports