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Publications

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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

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.

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.

Barthelmie, R. J. and S C Pryor, 2019: Impact of local meteorology on wake characteristics at Perdigão. J. Phys.: Conf. Ser. 1256 012007

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.

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.

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

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.

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.

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

Hannesdóttir, A. and coauthors, 2020: Can we predict short term extreme conditions from 10-minute data only? J. Phys.: Conf. Ser. 1452 012025

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.

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.

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.

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.

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.

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

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

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

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

Conference Proceedings

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Bell, T. and coauthors, 2017: Nocturnal Boundary-Layer Phenomena Observed at a Complex Site During the Perdigão Experiment. AGU 2017 Fall Meeting, A23J-08.

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.

Bodini, N., 2018: Variability of Turbulence Dissipation Rate in Complex Terrain: the Perdigão 2017 Field Campaign. 18th Conference on Mountain Meteorology, 9.3A.

Chow, F. K., 2018: Generation of Turbulent Structures in Nested Simulations for the Perdigao Campaign. 18th Conference on Mountain Meteorology, 9.4A.

Connolly, A. D., 2020: Development of Fine-Scale Structures in Large-Eddy Simulations over Complex Terrain. 19th Conference on Mountain Meteorology, 8.1.

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.

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.

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

Gerz, T. and coauthors, 2017: Numerical Simulation of Low-Level Jets during the Perdigão Field Campaign. AGU 2017 Fall Meeting, A21E-0062.

Gerz, T. and coauthors, 2017: Numerical Simulation of Low-Level Jets during the Perdigão Field Campaign. AGU 2017 Fall Meeting, A21E-0062.

Hocut, C. M. and coauthors, 2018: Mesoscale Flows and Turbulence Measurements on Forested Slope during Peridgão 2017. 18th Conference on Mountain Meteorology, 14.6A.

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.

Krishnamurthy, R., 2017: Nocturnal Reversed Flows Above Parallel Ridges in Perdigão, Portugal. AGU 2017 Fall Meeting, A21E-0060.

Lundquist, J. K., 2017: Measurements of Turbulent Kinetic Energy Dissipation Rate in Complex Terrain from the Perdigão Experiment. AGU 2017 Fall Meeting, A23J-06.

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.

Menke, R. and coauthors, 2018: Characterisation of Recirculation Zones in Complex Terrain Using Multi-Lidar Measurements. 18th Conference on Mountain Meteorology, 9.2A.

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.

Salvadore, J. J. and coauthors, 2017: Boundary Layer Characterization during Perdigão Field Campaign 2017. AGU 2017 Fall Meeting, A21E-0069.

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.

Reports

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

Theses

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Cardoso, N. V., 2020: Data analysis of the flow over Serra do Perdigão based on the sonic flow anemometers. MS Thesis.

Carvalho, J., 2019: Analysis of stationary periods during the Perdigao 2017 campaign. MS Thesis.

Kigle, Stephan (2017) Wake Identification and Characterization of a Full Scale Wind Energy Converter in Complex Terrain with Scanning Doppler Wind Lidar Systems. Master's, Ludwig-Maximilians-Universität München.

Mendes, F. M. M., 2020: Análise das medições de campo na Serra do Perdigão. MS Thesis.

Morgado, P., 2020: Análise de Dados de Medição de Campo na Serra do Perdigão. MS Thesis.

van Veen, L., 2018: The Perdigão field campaign : evaluation of the Cell Perturbation Method in atmospheric simulations. MS Thesis.

Vilaca, J., 2018: Analysis of atmospheric conditions: data quality and stationarity in Perdigao. MS Thesis.

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