References
- Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M. and co-authors. 2011. Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Mon. Weather Rev. 139, 3887–3905. doi:https://doi.org/10.1175/MWR-D-10-05013.1
- Barthlott, C., Kalthoff, N. and Fiedler, F. 2003. Influence of high-frequency radiation on turbulence measurements on a 200 m tower. Meteorol. Z. 12, 67–71. doi:https://doi.org/10.1127/0941-2948/2003/0012-0067
- Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J. and co-authors. 2017. The HARMONIE–AROME model configuration in the ALADIN–HIRLAM NWP system. Mon. Weather Rev. 145, 1919–1935. doi:https://doi.org/10.1175/MWR-D-16-0417.1
- Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H. and co-authors. 2011. The joint UK land environment simulator (JULES), model description – Part 1: energy and water fluxes. Geosci. Model Dev. 4, 677–699. doi:https://doi.org/10.5194/gmd-4-677-2011
- Beyrich, F., Herzog, H.-J. and Neisser, J. 2002. The LITFASS project of DWD and the LITFASS-98 experiment: the project strategy and the experimental setup. Theor. Appl. Climatol. 73, 3–18. doi:https://doi.org/10.1007/s00704-002-0690-8
- Bianco, L., Djalalova, I. V., Wilczak, J. M., Cline, J., Calvert, S. and co-authors. 2016. A wind energy ramp tool and metric for measuring the skill of numerical weather prediction models. Weather Forecast. 31, 1137–1156. doi:https://doi.org/10.1175/WAF-D-15-0144.1
- Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S. and co-authors. 2015. Towards a high-resolution regional reanalysis for the European CORDEX domain. Q. J. R. Meteorol. Soc. 141, 1–15. 2486. doi:https://doi.org/10.1002/qj.2486
- Borsche, M., Kaiser-Weiss, A. K. and Kaspar, F. 2016. Wind speed variability between 10 and 116m height from the regional reanalysis COSMO-REA6 compared to wind mast measurements over Northern Germany and the Netherlands. Adv. Sci. Res. 13, 151–161. doi:https://doi.org/10.5194/asr-13-151-2016
- Brümmer, B., Lange, I. and Konow, H. 2012. Atmospheric boundary layer measurements at the 280 m high Hamburg weather mast 1995-2011: mean annual and diurnal cycles. Meteorol. Z. 21, 319–335. doi:https://doi.org/10.1127/0941-2948/2012/0338
- Brümmer, B. and Schultze, M. 2015. Analysis of a 7-year low-level temperature inversion data set measured at the 280 m high Hamburg weather mast. Meteorol. Z. 24, 481–494. doi:https://doi.org/10.1127/metz/2015/0669
- Bubnov, R., Hello, G., Bénard, P. and Geleyn, J.-F. 1995. Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/ALADIN NWP system. Mon. Weather Rev. 123, 515–535. doi:https://doi.org/10.1175/1520-0493(1995)123<0515:IOTFEE>2.0.CO;2
- Cannon, D., Brayshaw, D., Methven, J., Coker, P. and Lenaghan, D. 2015. Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renew. Energy 75, 767–778. doi:https://doi.org/10.1016/j.renene.2014.10.024
- Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N. and co-authors. 2011. The joint UK land environment simulator (JULES), model description – Part 2: carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722. doi:https://doi.org/10.5194/gmd-4-701-2011
- Cuxart, J., Bougeault, P. and Redelsperger, J.-L. 2000. A turbulence scheme allowing for mesoscale and large-eddy simulations. Q. J. R. Meteorol. Soc. 126, 1–30. doi:https://doi.org/10.1002/qj.49712656202
- Cuxart, J., Holtslag, A. A. M., Beare, R. J., Bazile, E., Beljaars, A. and co-authors. 2006. Single-column model intercomparison for a stably stratified atmospheric boundary layer. Boundary-Layer Meteorol. 118, 273–303. doi:https://doi.org/10.1007/s10546-005-3780-1
- Dahlgren, P., Landelius, T., Kållberg, P. and Gollvik, S. 2016. A high-resolution regional reanalysis for Europe. Part 1: three-dimensional reanalysis with the regional HIgh-Resolution Limited-Area Model (HIRLAM). Q. J. R. Meteorol. Soc. 142, 2119–2131. doi:https://doi.org/10.1002/qj.2807
- de Rooy, W. and de Vries, H. 2017. Harmonie Verification and Evaluation. Technical Report. Availabe from HIRLAM-A Programme c/o Onvlee, KNMI, P.P. Box. 201, 3730 AE. 79 pp.
- Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P. and co-authors. 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597. doi:https://doi.org/10.1002/qj.828
- Dickinson, R. E. 1984. Modeling evapotranspiration for three-dimensional global climate models. Climate Processes and Climate Sensitivity 58–72. doi:https://doi.org/10.1029/gm029p0058
- Drechsel, S., Mayr, G. J., Messner, J. W., Stauffer, R., Drechsel, S. and co-authors. 2012. Wind speeds at heights crucial for wind energy: measurements and verification of forecasts. J. Appl. Meteorol. Climatol. 51, 1602–1617. doi:https://doi.org/10.1175/JAMC-D-11-0247.1
- Dürr, B. 2004. The greenhouse effect in the Alps - by models and observations. Ph.D. thesis.
- Emeis, S., Münkel, C., Vogt, S., Müller, W. J. and Schäfer, K. 2004. Atmospheric boundary-layer structure from simultaneous SODAR, RASS, and ceilometer measurements. Atmos. Environ. 38, 273–286. 054. doi:https://doi.org/10.1016/j.atmosenv.2003.09.054
- Frank, C., Pospichal, B., Wahl, S., Keller, J. D., Hense, A. and co-authors. 2020. The added value of high resolution regional reanalyses for wind power applications. Renew. Energy 148, 1094–1109. doi:https://doi.org/10.1016/j.renene.2019.09.138
- Frank, C. W., Wahl, S., Keller, J. D., Pospichal, B., Hense, A. and co-authors. 2018. Bias correction of a novel European reanalysis data set for solar energy applications. Sol. Energy 164, 12–24. doi:https://doi.org/10.1016/j.solener.2018.02.012
- Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A. and co-authors. 2017. The modern-era retrospective analysis for research and applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454. doi:https://doi.org/10.1175/JCLI-D-16-0758.1
- Geyer, B. 2014. High-resolution atmospheric reconstruction for Europe 1948–2012: coastDat2. Earth Syst. Sci. Data 6, 147–164. doi:https://doi.org/10.5194/essd-6-147-2014
- Geyer, B. 2017. coastDat-3_COSMO-CLM_ERAi. World Data Center for Climate (WDCC) at DKRZ, last access at Aug 2018. Online at: http://cera-www.dkrz.de/WDCC/ui/Compact. jsp?acronym=coastDat-3{\_}COSMO-CLM{\_}ERAi.
- González-Aparicio, I., Monforti, F., Volker, P., Zucker, A., Careri, F. and co-authors. 2017. Simulating European wind power generation applying statistical downscaling to reanalysis data. Appl. Energy 199, 155–168. doi:https://doi.org/10.1016/j.apenergy.2017.04.066
- Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80–91. doi:https://doi.org/10.1016/j.jhydrol.2009.08.003
- Heide, D., Greiner, M., von Bremen, L. and Hoffmann, C. 2011. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renew. Energy 36, 2515–2523. doi:https://doi.org/10.1016/j.renene.2011.02.009
- Helbig, N., Löwe, H., Lehning, M., Helbig, N., Löwe, H. and co-authors. 2009. Radiosity approach for the shortwave surface radiation balance in complex terrain. J. Atmos. Sci. 66, 2900–2912. doi:https://doi.org/10.1175/2009JAS2940.1
- Hong, S.-Y. and Chang, E.-C. 2012. Spectral nudging sensitivity experiments in a regional climate model. Asia-Pacific J. Atmos. Sci. 48, 345–355. doi:https://doi.org/10.1007/s13143-012-0033-3
- Hoyer, S. and Hamman, J. J. 2017. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. 5. doi:https://doi.org/10.5334/jors.148
- Jacob, M. 2013. Beeinflussung Von Windmessungen an Einem Rohrmast Durch Die Maststruktur (Bachelor Thesis). Technical Report, Department MIN, University of Hamburg, 35 pp.
- James, P. M. 2007. An objective classification method for Hess and Brezowsky Grosswetterlagen over. Theor. Appl. Climatol. 88, 17–42. doi:https://doi.org/10.1007/s00704-006-0239-3
- Kaiser-Weiss, A. K., Kaspar, F., Heene, V., Borsche, M., Tan, D. G. H. and co-authors. 2015. Comparison of regional and global reanalysis near-surface winds with station observations over Germany. Adv. Sci. Res. 12, 187–198. doi:https://doi.org/10.5194/asr-12-187-2015
- Kaldemeyer, C., Boysen, C. and Tuschy, I. 2016. Compressed air energy storage in the German energy system – status quo & perspectives. Energy Procedia 99, 298–313. doi:https://doi.org/10.1016/j.egypro.2016.10.120
- Kalthoff, N. and Vogel, B. 1992. Counter-current and channelling effect under stable stratification in the area of Karlsruhe. Theor. Appl. Climatol. 45, 113–126. doi:https://doi.org/10.1007/BF00866400
- Konow, H. M. 2015. Tall wind profiles in heterogeneous terrain. Ph.D. thesis.
- Landelius, T., Dahlgren, P., Gollvik, S., Jansson, A. and Olsson, E. 2016. A high-resolution regional reanalysis for Europe. Part 2: 2D analysis of surface temperature, precipitation and wind. Q. J. R. Meteorol. Soc. 142, 2132–2142. doi:https://doi.org/10.1002/qj.2813
- Li, D., von Storch, H. and Geyer, B. 2016. Testing reanalyses in constraining dynamical downscaling. J. Meteorol. Soc. Jpn. 94A, 47–68. doi:https://doi.org/10.2151/jmsj.2015-044
- Lorenc, A. C. and Rawlins, F. 2005. Why does 4D-Var beat 3D-Var? Q. J. R. Meteorol. Soc. 131, 3247–3257. doi:https://doi.org/10.1256/qj.05.85
- Mohan, M. and Siddiqui, T. 1998. Analysis of various schemes for the estimation of atmospheric stability classification. Atmos. Environ. 32, 3775–3781. doi:https://doi.org/10.1016/S1352-2310(98)00109-5
- Monin, A. and Obukhov, 1954. Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR 151, 163–187.
- Neisser, J., Adam, W., Beyrich, F., Leiterer, U. and Steinhagen, H. 2002. Atmospheric boundary layer monitoring at the Meteorological Observatory Lindenberg as a part of the “Lindenberg Column”: facilities and selected results. Meteorol. Z. 11, 241–253. doi:https://doi.org/10.1127/0941-2948/2002/0011-0241
- Niermann, D., Borsche, M., Kaiser-Weiss, A. and Kaspar, F. 2019. Evaluating renewable energy relevant parameters of COSMO-REA6 by comparing against station observations, satellites and other reanalyses. Meteorol. Z. 28, 347–360. doi:https://doi.org/10.1127/metz/2019/0945
- Noilhan, J., Planton, S., Noilhan, J. and Planton, S. 1989. A simple parameterization of land surface processes for meteorological models. Mon. Weather Rev. 117, 536–549. doi:https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2
- Pfenninger, S. and Staffell, I. 2016. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy 114, 1251–1265. 2016.08.060. doi:https://doi.org/10.1016/j.energy.2016.08.060
- Rabin, J., Delon, J. and Gousseau, Y. 2008. Circular earth mover’s distance for the comparison of local features, In: 2008 19th International Conference on Pattern Recognition, IEEE, Tampa, Florida, 1–4,
- Randles, C. A., Da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A. and co-authors. 2017. The MERRA-2 aerosol reanalysis, 1980 - onward, Part I: system description and data assimilation evaluation. J. Clim. 30, 6823–6850. 1175/JCLI-D-16-0609.1. doi:https://doi.org/10.1175/JCLI-D-16-0609.1
- Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D. and co-authors. 2007. The Met Office global four-dimensional variational data assimilation scheme. Q. J. R. Meteorol. Soc. 133, 347–362. doi:https://doi.org/10.1002/qj.32
- Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J. and co-authors. 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648. doi:https://doi.org/10.1175/JCLI-D-11-00015.1
- Schraff, C. and Hess, R. 2003. A Description of the Non-Hydrostatic Regional Model LM - Part III: Data Assimilation. Technical Report, Deutscher Wetterdienst, Offenbach, Germany.
- Schubert-Frisius, M., Feser, F., von Storch, H. and Rast, S. 2017. Optimal spectral nudging for global dynamic downscaling. Mon. Weather Rev. 145, 909–927. doi:https://doi.org/10.1175/MWR-D-16-0036.1
- Sevlian, R. and Rajagopal, R. 2013. Detection and statistics of wind power ramps. IEEE Trans. Power Syst. 28, 3610–3620. doi:https://doi.org/10.1109/TPWRS.2013.2266378
- Sharp, E., Dodds, P., Barrett, M. and Spataru, C. 2015. Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information. Renew. Energy 77, 527–538. doi:https://doi.org/10.1016/j.renene.2014.12.025
- Staffell, I. and Pfenninger, S. 2016. Using bias-corrected reanalysis to simulate current and future wind power output. Energy 114, 1224–1239. doi:https://doi.org/10.1016/j.energy.2016.08.068
- Stull, R. B. 1988. An Introduction to Boundary Layer Meteorology. Springer, Dordrecht, 670 pp.
- Tammelin, B., Vihma, T., Atlaskin, E., Badger, J., Fortelius, C. and co-authors. 2011. Production of the Finnish Wind Atlas. Wind Energ. 16, 19–35. doi:https://doi.org/10.1002/we.517
- Unden, P. 2018. Uncertainties in Ensembles of Regional Reanalyses (UERRA) - Final Report. Technical Report, SVERIGES METEOROLOGISKA OCH HYDROLOGISKA INSTITUT.
- Urraca, R., Huld, T., Gracia-Amillo, A., Martinez-de Pison, F. J., Kaspar, F. and co-authors. 2018. Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data. Sol. Energy 164, 339–354. doi:https://doi.org/10.1016/j.solener.2018.02.059
- Van de Wiel, B. J. H., Moene, A. F., Steeneveld, G. J., Baas, P., Bosveld, F. C. and co-authors. 2010. A conceptual view on inertial oscillations and nocturnal low-level jets. J. Atmos. Sci. 67, 2679–2689. doi:https://doi.org/10.1175/2010JAS3289.1
- Van Ulden, A. P. and Wieringa, J. 1996. Atmospheric boundary layer research at Cabauw. Boundary-Layer Meteorol. 78, 39–69. doi:https://doi.org/10.1007/BF00122486
- Verkaik, J. W. and Holtslag, A. A. M. 2007. Wind profiles, momentum fluxes and roughness lengths at Cabauw revisited. Boundary-Layer Meteorol. 122, 701–719. doi:https://doi.org/10.1007/s10546-006-9121-1
- von Storch, H., Langenberg, H., Feser, F., von Storch, H., Langenberg, H. and co-authors. 2000. A spectral nudging technique for dynamical downscaling purposes. Mon. Weather Rev. 128, 3664–3673. doi:https://doi.org/10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2
- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R. and co-authors. 2017. The met office unified model global atmosphere 6.0/6.1 and jules global land 6.0/6.1 configurations. Geosci. Model Dev. 10, 1487–1520. doi:https://doi.org/10.5194/gmd-10-1487-2017
- Wenzel, A., Kalthoff, N. and Horlacher, V. 1997. On the profiles of wind velocity in the roughness sublayer above a coniferous forest. Boundary Layer Meteorol. 84, 219–230. 1000444911103. doi:https://doi.org/10.1023/A:1000444911103
- Wohland, J., Reyers, M., Märker, C. and Witthaut, D. 2018. Natural wind variability triggered drop in German redispatch volume and costs from 2015 to 2016. PLoS One. 13, e0190707. doi:https://doi.org/10.1371/journal.pone.0190707
- Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M. and co-authors. 2014. An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global non-hydrostatic equations. Q. J. R. Meteorol. Soc. 140, 1505–1520. doi:https://doi.org/10.1002/qj.2235
- Yang, Q., Berg, L. K., Pekour, M., Fast, J. D., Newsom, R. K. and co-authors. 2013. Evaluation of WRF-predicted near-hub-height winds and ramp events over a Pacific Northwest site with complex terrain. J. Appl. Meteorol. Climatol. 52, 1753–1763. doi:https://doi.org/10.1175/JAMC-D-12-0267.1
- Yang, Y., Uddstrom, M. and Duncan, M. 2011. Effects of short spin-up periods on soil moisture simulation and the causes over New Zealand. J. Geophys. Res. 116.
- Žagar, M. and Rakovec, J. 1999. Small-scale surface wind prediction using dynamic adaptation. Tellus A 51, 489–504. doi:https://doi.org/10.3402/tellusa.v51i4.14051
- Žagar, N., Žagar, M., Cedilnik, J., Gregorič, G. and Rakovec, J. 2006. Validation of mesoscale low-level winds obtained by dynamical downscaling of ERA40 over complex terrain. Tellus A Dyn. Meteorol. Oceanogr. 58, 445–455. doi:https://doi.org/10.1111/j.1600-0870.2006.00186.x
- Zerrahn, A. and Schill, W.-P. 2017. Long-run power storage requirements for high shares of renewables: review and a new model. Renew. Sustain. Energy Rev. 79, 1518–1534. doi:https://doi.org/10.1016/j.rser.2016.11.098
- Zhang, Q., Pan, Y., Wang, S., Xu, J. and Tang, J. 2017. High-resolution regional reanalysis in China: evaluation of 1 year period experiments. J. Geophys. Res. Atmos. 122, 10801–10819. doi:https://doi.org/10.1002/2017JD027476