{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:36:53Z","timestamp":1760240213893,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented.<\/jats:p>","DOI":"10.3390\/rs11070781","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T03:21:26Z","timestamp":1554175286000},"page":"781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Editorial for the Special Issue \u201cRemote Sensing of Atmospheric Conditions for Wind Energy Applications\u201d"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2124-5651","authenticated-orcid":false,"given":"Charlotte Bay","family":"Hasager","sequence":"first","affiliation":[{"name":"Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3094-2109","authenticated-orcid":false,"given":"Mikael","family":"Sj\u00f6holm","sequence":"additional","affiliation":[{"name":"Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,1]]},"reference":[{"key":"ref_1","unstructured":"(2019, March 22). World Wind Energy Association. Available online: https:\/\/wwindea.org\/information-2\/information\/."},{"key":"ref_2","unstructured":"(2019, March 22). WindScanner.eu. Available online: http:\/\/www.windscanner.eu\/."},{"key":"ref_3","unstructured":"Mikkelsen, T., Siggaard Knudsen, S., Sj\u00f6holm, M., Angelou, N., and Pedersen, A.T. (2012, January 22). WindScanner.eu\u2014A new Remote Sensing Research Infrastructure for On- and Offshore Wind Energy. Proceedings of the International Conference on Wind Energy: Materials, Engineering and Policies (WEMEP-2012), Hyderabad, India."},{"key":"ref_4","unstructured":"J\u00f8rgensen, H.E., Mikkelsen, T., Mann, J., Bryce, D., Coffey, A., Harris, M., and Smith, D. (2004, January 19). Site wind field determination using a CW Doppler lidar - comparison with cup anemometers at Ris\u00f8. Proceedings of the Special Topic Conference: The Science of Making Torque from Wind, Delft, The Netherlands."},{"key":"ref_5","unstructured":"Pe\u00f1a, A., Hasager, C.B., Badger, M., Barthelmie, R.J., Bing\u00f6l, F., Cariou, J.-P., Emeis, S., Frandsen, S.T., Harris, M., and Karagali, I. (2019, March 22). Remote Sensing for Wind Energy. DTU Wind Energy. Available online: http:\/\/orbit.dtu.dk\/files\/111814239\/DTU_Wind_Energy_Report_E_0084.pdf."},{"key":"ref_6","unstructured":"Kariniotakis, G. (2017). Measurement methodologies for wind energy based on ground-level remote sensing. Renewable Energy Forecasting: From Models to Applications, Woodhead Publishing."},{"key":"ref_7","unstructured":"(2019, March 22). Special Issue \u201cRemote Sensing for Wind Energy\u201d 2016 A special issue of Remote Sensing (ISSN 2072-4292). Available online: https:\/\/www.mdpi.com\/journal\/remotesensing\/special_issues\/wind_energy_sensing."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kumer, V.-M., Reuder, J., and Oftedal Eikill, R. (2017). Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements. Remote Sens., 9.","DOI":"10.3390\/rs9030242"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Doubrawa, P., Barthelmie, R.J., Wang, H., Pryor, S.C., and Churchfield, M.J. (2016). Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements. Remote Sens., 8.","DOI":"10.3390\/rs8110939"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Van Dooren, M.F., Trabucchi, D., and K\u00fchn, M. (2016). A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements. Remote Sens., 8.","DOI":"10.3390\/rs8100809"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Borraccino, A., Courtney, M., and Wagner, R. (2016). Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars. Remote Sens., 8.","DOI":"10.20944\/preprints201609.0100.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pauscher, L., Vasiljevi\u0107, N., Callies, D., Lea, G., Mann, J., Klaas, T., Hieronimus, J., Gottschall, J., Schwesig, A., and K\u00fchn, M. (2016). An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain. Remote Sens., 8.","DOI":"10.3390\/rs8090782"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pauscher, L., Vasiljevic, N., Callies, D., Lea, G., Mann, J., Klaas, T., Hieronimus, J., Gottschall, J., Schwesig, A., and K\u00fchn, M. (2017). Erratum: Pauscher, L., et al. An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain. Remote Sens. 2016, 8, 782 . Remote Sens., 9.","DOI":"10.3390\/rs8090782"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Floors, R., Pe\u00f1a, A., Lea, G., Vasiljevi\u0107, N., Simon, E., and Courtney, M. (2016). The RUNE Experiment\u2014A Database of Remote-Sensing Observations of Near-Shore Winds. Remote Sens., 8.","DOI":"10.20944\/preprints201610.0070.v1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, H.-G., Jeon, W.-H., and Kim, D.-H. (2016). Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD. Remote Sens., 8.","DOI":"10.3390\/rs8121019"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bos, R., Giyanani, A., and Bierbooms, W. (2016). Assessing the Severity of Wind Gusts with Lidar. Remote Sens., 8.","DOI":"10.3390\/rs8090758"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vasiljevi\u0107, N., Lea, G., Courtney, M., Cariou, J.-P., Mann, J., and Mikkelsen, T. (2016). Long-Range WindScanner System. Remote Sens., 8.","DOI":"10.20944\/preprints201610.0017.v1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chang, R., Zhu, R., and Guo, P. (2016). A Case Study of Land-Surface-Temperature Impact from Large-Scale Deployment of Wind Farms in China from Guazhou. Remote Sens., 8.","DOI":"10.3390\/rs8100790"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hasager, C.B., Astrup, P., Zhu, R., Chang, R., Badger, M., and Hahmann, A.N. (2016). Quarter-Century Offshore Winds from SSM\/I and WRF in the North Sea and South China Sea. Remote Sens., 8.","DOI":"10.3390\/rs8090769"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Clifton, A., Clive, P., Gottschall, J., Schlipf, D., Simley, E., Simmons, L., Stein, D., Trabucchi, D., Vasiljevi\u0107, N., and W\u00fcrth, I. (2018). IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar. Remote Sens., 10.","DOI":"10.3390\/rs10030406"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hill, C. (2018). Coherent Focused Lidars for Doppler Sensing of Aerosols and Wind. Remote Sens., 10.","DOI":"10.3390\/rs10030466"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Simley, E., F\u00fcrst, H., Haizmann, F., and Schlipf, D. (2018). Optimizing Lidars for Wind Turbine Control Applications\u2014Results from the IEA Wind Task 32 Workshop. Remote Sens., 10.","DOI":"10.3390\/rs10060863"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Carbajo Fuertes, F., Markfort, C.D., and Port\u00e9-Agel, F. (2018). Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation. Remote Sens., 10.","DOI":"10.3390\/rs10050668"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Carbajo Fuertes, F., and Port\u00e9-Agel, F. (2018). Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake. Remote Sens., 10.","DOI":"10.3390\/rs10050721"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, K., Cherukuru, N., Sun, X., and Calhoun, R. (2018). Wind Gust Detection and Impact Prediction for Wind Turbines. Remote Sens., 10.","DOI":"10.3390\/rs10040514"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mayor, S.D., and D\u00e9rian, P. (2018). Comments on \u201cWind Gust Detection and Impact Prediction for Wind Turbines\u201d. Remote Sens., 10.","DOI":"10.3390\/rs10101625"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hofs\u00e4\u00df, M., Clifton, A., and Cheng, P.W. (2018). Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach. Remote Sens., 10.","DOI":"10.3390\/rs10091465"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Risan, A., Lund, J.A., Chang, C.-Y., and S\u00e6tran, L. (2018). Wind in Complex Terrain\u2014Lidar Measurements for Evaluation of CFD Simulations. Remote Sens., 10.","DOI":"10.3390\/rs10010059"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J.A., Atlas, R., Emmitt, G.D., Bucci, L., and Ryan, K. (2018). Airborne Doppler Wind Lidar Observations of the Tropical Cyclone Boundary Layer. Remote Sens., 10.","DOI":"10.3390\/rs10060825"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Valldecabres, L., Nygaard, N.G., Vera-Tudela, L., Von Bremen, L., and K\u00fchn, M. (2018). On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts. Remote Sens., 10.","DOI":"10.3390\/rs10111701"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shimada, S., Takeyama, Y., Kogaki, T., Ohsawa, T., and Nakamura, S. (2018). Investigation of the Fetch Effect Using Onshore and Offshore Vertical LiDAR Devices. Remote Sens., 10.","DOI":"10.3390\/rs10091408"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gottschall, J., Catalano, E., D\u00f6renk\u00e4mper, M., and Witha, B. (2018). The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea. Remote Sens., 10.","DOI":"10.3390\/rs10101620"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guti\u00e9rrez-Antu\u00f1ano, M.A., Tiana-Alsina, J., Salcedo, A., and Rocadenbosch, F. (2018). Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar. Remote Sens., 10.","DOI":"10.3390\/rs10122037"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, Q., Xu, X., Zhang, K., Li, Z., Huang, W., Mansaray, L.R., Liu, W., Wang, X., Gao, J., and Huang, J. (2018). Assessing Global Ocean Wind Energy Resources Using Multiple Satellite Data. Remote Sens., 10.","DOI":"10.3390\/rs10010100"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/781\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:42:06Z","timestamp":1760186526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,1]]},"references-count":34,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070781"],"URL":"https:\/\/doi.org\/10.3390\/rs11070781","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,4,1]]}}}