References
- Bethere, L., Sennikovs, J. and Bethers, U. 2017. Climate indices for the Baltic states from principal component analysis. Earth Syst. Dynam. 8, 951–962. https://esd.copernicus.org/articles/8/951/2017/. doi:https://doi.org/10.5194/esd-8-951-2017
- Briede, A. 2016. Klimats un ilgtspejga attistiba. Latvijas Universitate, Chap. 4., 78–81 isbn 978-9934.18-136-8.
- ECMWF. 2019. UERRA regional reanalysis for Europe on single levels from 1961 to 2019. Online at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-single-levels?tab=overview.
- Gupta, A. and Dhir, A. 2013. Estimation of horizontal pollution potential by calculating impact area for Patiala, Punjab using wind data. Int. J. Innov. Res. Sci. Eng. Technol. 2, 2271–2279.
- Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M. and co-authors. 2020. The making of the New European Wind Atlas - Part 1: Model sensitivity. Geosci. Model Dev. 13, 5053–5078. In: url: https://gmd.copernicus.org/articles/13/5053/2020/. doi:https://doi.org/10.5194/gmd-13-5053-2020
- Jaagus, J. and Kull, A. 2011. Changes in surface wind directions in Estonia during 1966-2008 and their relationships with large-scale atmospheric circulation. Estonian J. Earth Sci. 60, 220. In: doi:https://doi.org/10.3176/earth.2011.4.03
- Jaagus, J., Briede, A., Rimkus, E. and Remm, K. 2009. Precipitation pattern in the Baltic countries under the influence of large-scale atmospheric circulation and local landscape factors. Int. J. Climatol. 30, n/a–720. In: doi:.
- Jolliffe, I. T. 2002. Principal Component Analysis. Springer Series in Statistics. New York: Springer-Verlag. ISBN: 0-387-95442-2. http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-95442-4.
- Jungo, P., Goyette, S. and Beniston, M. 2002. Daily wind gust speed probabilities over Switzerland according to three types of synoptic circulation. Int. J. Climatol. 22, 485–499. In: doi:. doi:https://doi.org/10.1002/joc.741
- Klink, K. and Willmott, C. 1989. Principal components of the surface wind field in the United States: A comparison of analyses based upon wind velocity, direction, and speed. Int. J. Climatol. 9, 293–308. doi:https://doi.org/10.1002/joc.3370090306
- Koch, W. 2004. Directional analysis of SAR images aiming at wind direction. IEEE Trans. Geosci. Remote Sens. 42, 702–710. doi:https://doi.org/10.1109/TGRS.2003.818811
- Kouts, T. 1998. Forcing Factors for Hydrography and Currents – Meteorological and Hydrological Variables. In: The Gulf of Riga Project (funded by The Nordic Council of Ministers). SMHI, Norrkoping, Sweden.
- Lea, D. A. and Helvey, R. A. 1971. A directional bias in wind roses due to mixed compass formats. J. Appl. Meteor. 10, 1037–1039. In: Oct. > 2.0.CO;2. url: https://journals.ametsoc.org/view/journals/apme/10/5/1520-0450_1971_010_1037_adbiwr_2_0_co_2.xml. doi:https://doi.org/10.1175/1520-0450(1971)010<1037:ADBIWR>2.0.CO;2
- Met Office. 2010. Cartopy: A Cartographic Python Library with a Matplotlib Interface. Exeter, Devon, 2010–2015. https://scitools.org.uk/cartopy.
- Mezaache, H., Bouzgou, H. and Raymond, C. 2016. Kernel principal components analysis with extreme learning machines for wind speed prediction. In Seventh International Renewable Energy Congress, IREC 2016. Hammamet, Tunisia, March. https://hal.inria.fr/hal-01394000.
- Pedro, A. J., González‐Rouco, J. F., Montávez, J. P., García‐Bustamante, E., & Navarro, J. 2009. Climatology of wind patterns in the northeast of the Iberian Peninsula. Int. J. Climatol. 29.4, 501–525. doi:.
- Pele, O. and Werman, M. 2008. A linear time histogram metric for improved sift matching. In: Computer Vision – ECCV. Springer, Berlin, Heidelberg, Oct., pp. 495–508.
- Pele, O. and Werman, M. 2009. Fast and robust earth mover's distances. In: 2009 IEEE 12th Inter-national Conference on Computer Vision. IEEE. Sept., pp. 460–467.
- Ratner, B. 1950. A method for eliminating directional bias in wind roses. Mon. Wea. Rev. 78, 185–188. 0493(1950)078 < 0185:AMFEDB > 2.0.CO;2. url: https://journals.ametsoc.org/view/journals/mwre/78/10/1520-0493_1950_078_0185_amfedb_2_0_co_2.xml. doi:https://doi.org/10.1175/1520-0493(1950)078<0185:AMFEDB>2.0.CO;2
- Rubner, Y., Tomasi, C. and Guibas, L. J. 2000. The earth mover's distance as a metric for image retrieval. Int. J. Computer Vision. 40, 99–121. url: https://doi.org/https://doi.org/10.1023/A:1026543900054. doi:https://doi.org/10.1023/A:1026543900054
- Rutgersson, A. 2015. Recent change | atmosphere. In: Second Assessment of Climate Change for the Baltic Sea Basin (ed. The BACC II Author Team). Springer International Publishing, Cham, pp. 69–97. ISBN: 978-3-319-16006-1. 1007/978 - 3 - 319 - 16006 - 1 _ 4. url: https://doi.org/https://doi.org/10.1007/978-3-319-16006-1_4.
- Sepp, M., Post, P., Mändla, K., & Aunap, R. et al. 2018. On cyclones entering the Baltic Sea region. Boreal Environ. Res. 23, 1–14.
- Sīle, T., Seņņikovs, J. and Bethers, U. June 2018. Evidence for low-level jets caused by coastal baroclinity at the Kurzeme shore of the Baltic Sea. Estonian J. Earth Sci. 67, 149. In: doi:https://doi.org/10.3176/earth.2018.11
- Soomere, T. and Keevallik, S. 2001. Anisotropy of moderate and strong winds in the Baltic Proper. Proc. Estonian Acad. Sci. Eng. 7, 35–49.
- Team, B. A. 2008. Assessment of Climate Change for the Baltic Sea Basin. Springer, Berlin Heidelberg. isbn: 978-3-540-72786-6. url: https://doi.org/https://doi.org/10.1007/978-3-540-72786-6.
- WMO. 2017. WMO Guidelines on the Calculation of Climate Normal. 2017th ed. World Meteorological Organization (WMO). isbn: 978-92-63-11203-3.