PhD Summer School Boulder Colorado

PhD Summer School 2024 - Boulder

We can now confirm that the summer school will proceed as scheduled.

The DTU Wind and Energy Systems PhD Summer School in Remote Sensing at University of Colorado, Boulder, USA is going to take place  24 – 28 June 2024.

This 5-day summer school will focus on advances in remote sensing techniques useful in wind energy. The themes to be covered are development, instrument configuration, signal processing, data analysis and applications of various remote sensing instruments including lidar and radar. Applied use includes wind resource mapping, wind profiling, inflow characterization, wind loads, turbulence, and wind turbine control. Theoretical aspects of scattering and atmospheric boundary-layer characteristics relevant in remote sensing for wind energy will also be covered. Visits will demonstrate remote sensing methodologies, and advantages and limitations will be discussed.

Cost for participants:
250 euros per PhD students
2500 euros per non-PhD students

Deadline for registration: 1 June 2024

Please register for the course here

The registration fee covers participation in the summer school, course material and listing of recommended reading, lunches and coffee breaks from Monday to Friday.

Registration DOES NOT include the hotel booking.

We have agreements with hotels in Boulder for special rates.

You can get a group rate under the Renewable and Sustainable Energy Institute (RASEI) umbrella at: Book your group rate for RASEI

And the following link is set up with University discount already:
Book Your Group/Corporate Rate | Marriott International

Please note that in case the participant wants to cancel registration, we CAN NOT provide refund of the registration fee.

For further details, please e-mail Alfredo Pena at aldi@dtu.dk.

Organizers:
Alfredo Peña, Julie Lundquist and Jakob Mann.

Lecturers:
To be confirmed but a preliminary schedule with possible lecturers is shown below.

We plan hands-on exercises. Please bring your laptop.

Credits:
Credits for the course are 2.5 ECTS. This includes 34 hours of preparation time studying the recommended reading:

Chapter 3 Climatological and meteorological aspects of predicting offshore wind energy
Chapter 4: Atmospheric turbulence
Chapter 5 Introduction to continuous-wave
Chapter 6 Pulsed lidars
Chapter 9 Lidars and wind turbine control
Chapter 10 Lidars and wind profiles

The 'Compendium of the PhD Summer School: Remote Sensing for Wind Energy' is available at http://orbit.dtu.dk/files/111814239/DTU_Wind_Energy_Report_E_0084.pdf 

Chapter 2 Measurement methodologies for wind energy based on ground-level remote sensing in Sven-Erik Gryning, Torben Mikkelsen, Christophe Baehr, Alain Dabas, Paula Gomez, Ewan O’Connor, Lucie Rottner, Mikael Sjöholm, Irene Suomi, Nikola Vasiljevic: Renewable Energy Forecasting 1st Edition Elsevier. https://www.elsevier.com/books/renewable-energy-forecasting/kariniotakis/978-0-08-100504-0 

Chapter 4 A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields in Vasiljevic, N 2014, A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields. Ph.D. thesis, DTU Wind Energy. DTU Wind Energy PhD, no. 0027(EN) http://orbit.dtu.dk/en/publications/a-timespace-synchronization-of-coherent-doppler-scanning-lidars-for-3d-measurements-of-wind-fields(e2519d99-5846-4651-947d-38c287452366).html
Airborne lidar at https://en.wikipedia.org/wiki/Lidar#Airborne_lidar

Boudreault, L-E., Bechmann, A., Taryainen, L., Klemedtsson, L., Shendryk, I., & Dellwik, E. (2015). A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests. Agricultural and Forest Meteorology, 201, 86-97. DOI: 10.1016/j.agrformet.2014.10.014

Dagestad, K. F., Horstmann, J., Mouche, A., Perrie, W., Shen, H., Zhang, B., ... & Badger, M. (2012). Wind retrieval from synthetic aperture radar - an overview. In 4th SAR Oceanography Workshop (SEASAR 2012).
http://orbit.dtu.dk/fedora/objects/orbit:124632/datastreams/file_8597009d-84ec-485e-8bfc-6802a8606721/content

GUM: Guide to the Expression of Uncertainty in Measurement
http://www.bipm.org/en/publications/guides/gum.html

Lange, J, Mann, J, Angelou, N, Berg, J, Sjöholm, M& Mikkelsen, TK2016, 'Variations of the Wake Height over the Bolund Escarpment Measured by a Scanning Lidar'Boundary-Layer Meteorology, vol 159, pp. 147–159. DOI:10.1007/s10546-015-0107-8

Larsen, SE 1993, Observing and modelling the planetary boundary layer. in E Raschke & D Jacob (eds), Energy and water cycles in the climate system. Springer-Verlag, Berlin, pp. 365-418. NATO Advanced Study Institute Series I: Global environmental change, 5

Mann, J., et al: Complex terrain experiments in the New European Wind Atlas. Phil. Trans. R. Soc. A, 375, no 2091, 20160101 (2017) 10.1098/rsta.2016.0101

Peña A. (2009) Sensing the wind profile. Risø-PhD-45(EN), Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Roskilde. http://orbit.dtu.dk/fedora/objects/orbit:81302/datastreams/file_3737370/content

Sathe, A, Mann, J, Gottschall, J& Courtney, M2011, 'Can Wind Lidars Measure Turbulence?'Journal of Atmospheric and Oceanic Technology, vol 28, no. 7, pp. 853-868. DOI:10.1175/JTECH-D-10-05004.1

Sathe, A& Mann, J2013, 'A review of turbulence measurements using ground-based wind lidars'Atmospheric Measurement Techniques, vol 6, pp. 3147–3167. DOI:10.5194/amt-6-3147-2013

Sjöholm, M., Angelou, N., Hansen, P., Hansen, K. H., Mikkelsen, T., Haga, S., ... Starsmore, N. (2014). Two Dimensional Rotorcraft Downwash Flow Field Measurements by Lidar-Based Wind Scanners with Agile Beam Steering. Journal of Atmospheric and Oceanic Technology, 31(4), 930-937. DOI: 10.1175/JTECH-D-13-00010.1

Vasiljevic, N., & Courtney, M. (2017). Accuracy of dual-Doppler lidar retrievals of near-shore winds Kgs. Lyngby: Danmarks Tekniske Universitet (DTU). WindEurope Resource Assessment Workshop 2017, Edinburgh, United Kingdom, 16/03/2017

Vasiljević, N.; Lea, G.; Courtney, M.; Cariou, J.-P.; Mann, J.; Mikkelsen, T. Long-Range WindScanner System. Remote Sens. 2016, 8, 896.

Vasiljević, N., Palma, J. M. L. M., Angelou, N., Matos, J.C., Menke, R., Lea, G., Mann, J., Courtney, M., Ribeiro, L.F.,and Gomes, V. M. M. G. C. Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments. Atmos. Meas. Tech., 10, 3463-3483, 2017

Wagner et al., Accounting for the wind speed shear in wind turbine power performance measurement, Wind Energy. 2011; 14:993–1004. doi: 10.1002/we.509

Wagner et al., Uncertainty of power curve measurement with a two-beam nacelle-mounted lidar. Wind Energy. 2015; 19:1269–1287. doi: 10.1002/we.1897


Learning objectives:

A student who has met the objectives of the course will be able to:
• To explain basic principles of continuous-wave and pulsed Doppler lidar for wind energy
• To be able to interpret and analyse wind lidar data
• To describe ground-based and nacelle lidar
• To explain the basic principles of lidars for wind farm control
• To explain remote sensing techniques for observing turbulence and understand why lidars are not measuring the same turbulence as in-situ sensors
• To describe the capabilities and limitations of continuous-wave and pulsed Doppler lidar for measuring the wind flow over complex terrain
• To list the sensors needed to measure physical parameters related to the wind profile
• To be able to reconstruct orthogonal wind components from line-of-sight speeds
• To understand the main sources of uncertainty that impact lidar accuracy
• To develop a typical measurement plan using remote sensing devices for wind data
• To explain the basic principle of radar for wind
• To gain an overview of meteorological parameters related to the use of wind lidars and radars
• To understand temporal scales of flow characterization, main methods for wind resource assessment and major differences between on-shore and offshore flow related to wind energy

Preliminary Schedule for the course: