17 Nov 2023
| 17 Nov 2023
Intercomparing the quality of recent reanalyses for offshore wind farm planning in Germany's exclusive economic zone of the North Sea
Thomas Spangehl
CORRESPONDING AUTHOR
National Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany
Michael Borsche
National Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany
Deborah Niermann
National Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany
Frank Kaspar
Hydrometeorology, Deutscher Wetterdienst, Offenbach, Germany
Semjon Schimanke
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Susanne Brienen
Central Climate Office, Deutscher Wetterdienst, Offenbach, Germany
Thomas Möller
Marine Climate Monitoring, Deutscher Wetterdienst, Hamburg, Germany
Maren Brast
Site Investigation, Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Germany
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Frank Kaspar, Deborah Niermann, Michael Borsche, Stephanie Fiedler, Jan Keller, Roland Potthast, Thomas Rösch, Thomas Spangehl, and Birger Tinz
Adv. Sci. Res., 17, 115–128, https://doi.org/10.5194/asr-17-115-2020, https://doi.org/10.5194/asr-17-115-2020, 2020
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Reanalyses are long-term meteorological datasets that are based on numerical weather prediction models and the assimilation of historic observations. The regional model COSMO of Germany’s national meteorological service (Deutscher Wetterdienst) has been used to develop regional reanalyses with spatial resolution of up to 2 km. In this paper, we provide an overview of evaluation results and application examples at the European and national German level with a focus on renewable energy.
Rita Glowienka-Hense, Andreas Hense, Thomas Spangehl, and Marc Schröder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-141, https://doi.org/10.5194/gmd-2018-141, 2018
Revised manuscript not accepted
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Ensemble forecast verification treats the issues of forecast errors and uncertainty estimated from ensemble spread. We suggest measures based on relative entropy. For continuous variables correlation and the mean ratio of the ensemble spread to climate variance (analysis of variance (anova)) are related to these entropies. For categorical data corresponding scores are deduced that allow the comparison with continuous data.
S. Dietrich, M. Werner, T. Spangehl, and G. Lohmann
Clim. Past, 9, 13–26, https://doi.org/10.5194/cp-9-13-2013, https://doi.org/10.5194/cp-9-13-2013, 2013
Daniel Gliksman, Paul Averbeck, Nico Becker, Barry Gardiner, Valeri Goldberg, Jens Grieger, Dörthe Handorf, Karsten Haustein, Alexia Karwat, Florian Knutzen, Hilke S. Lentink, Rike Lorenz, Deborah Niermann, Joaquim G. Pinto, Ronald Queck, Astrid Ziemann, and Christian L. E. Franzke
Nat. Hazards Earth Syst. Sci., 23, 2171–2201, https://doi.org/10.5194/nhess-23-2171-2023, https://doi.org/10.5194/nhess-23-2171-2023, 2023
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Wind and storms are a major natural hazard and can cause severe economic damage and cost human lives. Hence, it is important to gauge the potential impact of using indices, which potentially enable us to estimate likely impacts of storms or other wind events. Here, we review basic aspects of wind and storm generation and provide an extensive overview of wind impacts and available indices. This is also important to better prepare for future climate change and corresponding changes to winds.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
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The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 1227–1232, https://doi.org/10.5194/nhess-23-1227-2023, https://doi.org/10.5194/nhess-23-1227-2023, 2023
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Estimating the severity of a rainfall event based on the damage caused is easy but highly depends on the affected region. A less biased measure for the extremeness of an event is its rarity combined with its spatial extent. In this brief communication, we investigate the sensitivity of such measures to the underlying dataset and highlight the importance of considering multiple spatial and temporal scales using the devastating rainfall event in July 2021 in central Europe as an example.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Christopher W. Frank, Frank Kaspar, Jan D. Keller, Till Adams, Miriam Felkers, Bernd Fischer, Marcus Handte, Pedro José Marrón, Hinrich Paulsen, Markus Neteler, Jochen Schiewe, Marvin Schuchert, Christian Nickel, Richard Wacker, and Richard Figura
Adv. Sci. Res., 17, 183–190, https://doi.org/10.5194/asr-17-183-2020, https://doi.org/10.5194/asr-17-183-2020, 2020
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Access to high quality weather and climate data is crucial for a wide range of societal and economic issues. It allows optimising industrial processes with respect to efficiency. The goal of the research project FAIR is to simplify the information exchange between the DWD and economical players. This paper summarizes the results of the requirement analysis of three use cases and presents the deduced technical architecture and FAIR services aiming at a user-friendly exchange of weather data.
Frank Kaspar, Deborah Niermann, Michael Borsche, Stephanie Fiedler, Jan Keller, Roland Potthast, Thomas Rösch, Thomas Spangehl, and Birger Tinz
Adv. Sci. Res., 17, 115–128, https://doi.org/10.5194/asr-17-115-2020, https://doi.org/10.5194/asr-17-115-2020, 2020
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Reanalyses are long-term meteorological datasets that are based on numerical weather prediction models and the assimilation of historic observations. The regional model COSMO of Germany’s national meteorological service (Deutscher Wetterdienst) has been used to develop regional reanalyses with spatial resolution of up to 2 km. In this paper, we provide an overview of evaluation results and application examples at the European and national German level with a focus on renewable energy.
Lisa Hannak, Karsten Friedrich, Florian Imbery, and Frank Kaspar
Adv. Sci. Res., 16, 175–183, https://doi.org/10.5194/asr-16-175-2019, https://doi.org/10.5194/asr-16-175-2019, 2019
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In order to analyze the impact on long time series by the transition from manual to automatic sunshine duration devices, parallel measurements of German climate reference stations are used. The different measurement principles (glass sphere and photodiode) cause systematic differences between the observations. With a linear regression model (used to adjust the automatic measurements) we want to prevent breaks in long time series of daily sunshine duration.
Frank Kaspar, Michael Borsche, Uwe Pfeifroth, Jörg Trentmann, Jaqueline Drücke, and Paul Becker
Adv. Sci. Res., 16, 119–128, https://doi.org/10.5194/asr-16-119-2019, https://doi.org/10.5194/asr-16-119-2019, 2019
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In this study, we assess balancing effects between photovoltaics and wind energy. On average, the seasonal cycles complement each other in Germany as well as in Europe. The frequency of events with a risk of low electricity generation is analyzed. The results illustrate that the number of such events is reduced when offshore regions are included, or when a combined system of PV and wind energy is considered. A European-wide analysis also leads to a distinct reduction of such events.
Frank Kaspar, Frank Kratzenstein, and Andrea K. Kaiser-Weiss
Adv. Sci. Res., 16, 75–83, https://doi.org/10.5194/asr-16-75-2019, https://doi.org/10.5194/asr-16-75-2019, 2019
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During recent years, Germany’s national meteorological service has significantly expanded the open access to its climate observations. To improve the interactive and user-friendly access to the data, a new data portal has been developed. The portal serves a variety of user requirements that result from the broad range of applications of DWD’s climate data. The focus of the recent developments was on improved access to German station data.
Robinson Hordoir, Lars Axell, Anders Höglund, Christian Dieterich, Filippa Fransner, Matthias Gröger, Ye Liu, Per Pemberton, Semjon Schimanke, Helen Andersson, Patrik Ljungemyr, Petter Nygren, Saeed Falahat, Adam Nord, Anette Jönsson, Iréne Lake, Kristofer Döös, Magnus Hieronymus, Heiner Dietze, Ulrike Löptien, Ivan Kuznetsov, Antti Westerlund, Laura Tuomi, and Jari Haapala
Geosci. Model Dev., 12, 363–386, https://doi.org/10.5194/gmd-12-363-2019, https://doi.org/10.5194/gmd-12-363-2019, 2019
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Nemo-Nordic is a regional ocean model based on a community code (NEMO). It covers the Baltic and the North Sea area and is used as a forecast model by the Swedish Meteorological and Hydrological Institute. It is also used as a research tool by scientists of several countries to study, for example, the effects of climate change on the Baltic and North seas. Using such a model permits us to understand key processes in this coastal ecosystem and how such processes will change in a future climate.
Rita Glowienka-Hense, Andreas Hense, Thomas Spangehl, and Marc Schröder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-141, https://doi.org/10.5194/gmd-2018-141, 2018
Revised manuscript not accepted
Short summary
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Ensemble forecast verification treats the issues of forecast errors and uncertainty estimated from ensemble spread. We suggest measures based on relative entropy. For continuous variables correlation and the mean ratio of the ensemble spread to climate variance (analysis of variance (anova)) are related to these entropies. For categorical data corresponding scores are deduced that allow the comparison with continuous data.
Per Pemberton, Ulrike Löptien, Robinson Hordoir, Anders Höglund, Semjon Schimanke, Lars Axell, and Jari Haapala
Geosci. Model Dev., 10, 3105–3123, https://doi.org/10.5194/gmd-10-3105-2017, https://doi.org/10.5194/gmd-10-3105-2017, 2017
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The Baltic Sea is seasonally ice covered with intense wintertime ship traffic and a sensitive ecosystem. Understanding the sea-ice pack is important for climate effect studies and forecasting. A NEMO-LIM3.6-based model setup for the North Sea/Baltic Sea is introduced, including a method for ice in the coastal zone. We evaluate different sea-ice parameters and overall find that the model agrees well with the observation though deformed ice is more challenging to capture.
Kai-Uwe Eiselt, Frank Kaspar, Thomas Mölg, Stefan Krähenmann, Rafael Posada, and Jens O. Riede
Adv. Sci. Res., 14, 163–173, https://doi.org/10.5194/asr-14-163-2017, https://doi.org/10.5194/asr-14-163-2017, 2017
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As one element of the SASSCAL initiative (a cooperation of Angola, Botswana, Namibia, Zambia, South Africa and Germany) networks of automatic weather stations have been installed or improved in Southern Africa. Here we compare interpolation methods for monthly minimum and maximum temperatures which were calculated from hourly measurements. The best interpolation results have been achieved combining multiple linear regression with three dimensional inverse distance weighted interpolation.
Frank Kaspar, Lisa Hannak, and Klaus-Jürgen Schreiber
Adv. Sci. Res., 13, 163–171, https://doi.org/10.5194/asr-13-163-2016, https://doi.org/10.5194/asr-13-163-2016, 2016
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Germany's national meteorological service (DWD) operates a network of so-called climate reference stations. At these stations parallel measurements are performed in order to allow the comparison of manual and automatic observations. We present an analysis of parallel measurements of temperature at 2 m height. It is shown that the automation of stations did not cause an artificial increase in the series of daily mean temperature. A bias occurs for maximum temperature in specific configurations.
Michael Borsche, Andrea K. Kaiser-Weiss, and Frank Kaspar
Adv. Sci. Res., 13, 151–161, https://doi.org/10.5194/asr-13-151-2016, https://doi.org/10.5194/asr-13-151-2016, 2016
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Wind speeds derived from the regional reanalysis COSMO-REA6 and from two global reanalyses of ECMWF (ERA-Interim and ERA-20C) have been compared to tower measurements covering heights from 10 m up to 140 m, located in the North Sea, in The Netherlands, and in Northeast Germany. In general, the monthly and hourly mean values of COSMO-REA6 are at least as close to the measurements as the global reanalyses, and in the correlation of daily means an added value of COSMO-REA6 is found.
Alexander Loew, Jian Peng, and Michael Borsche
Geosci. Model Dev., 9, 2499–2532, https://doi.org/10.5194/gmd-9-2499-2016, https://doi.org/10.5194/gmd-9-2499-2016, 2016
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Surface water and energy fluxes are essential components of the Earth system. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high resolved flux estimates at the global scale. The framework maximizes the usage of existing long-term satellite data records. Overall the results indicate very good agreement with in situ observations when compared against 49 FLUXNET stations worldwide.
Rafael Posada, Domingos Nascimento, Francisco Osvaldo S. Neto, Jens Riede, and Frank Kaspar
Adv. Sci. Res., 13, 97–105, https://doi.org/10.5194/asr-13-97-2016, https://doi.org/10.5194/asr-13-97-2016, 2016
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To respond to the challenges of climate change, Angola, Botswana, Germany, Namibia, South Africa and Zambia have initiated the regional competence centre SASSCAL. As part of the initiative, Deutscher Wetterdienst (DWD) cooperates with the meteorological services of Angola, Botswana and Zambia to improve the management of climate data. First results of the cooperation between DWD and the Angolan Meteorological Services (INAMET) are presented in order to provide hints for comparable activities.
M. Borsche, A. K. Kaiser-Weiss, P. Undén, and F. Kaspar
Adv. Sci. Res., 12, 207–218, https://doi.org/10.5194/asr-12-207-2015, https://doi.org/10.5194/asr-12-207-2015, 2015
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Within the European Union’s seventh Framework Programme project Uncertainties in Ensembles of Regional Re-Analyses (UERRA), ensembles of RRAs covering the European area are produced and their uncertainties are quantified. In this study, we discuss different methods for quantifying the uncertainty of RRAs in order to answer the question to which extent the smaller scale information (or resulting statistics) provided by the RRAs can be relied on.
A. K. Kaiser-Weiss, F. Kaspar, V. Heene, M. Borsche, D. G. H. Tan, P. Poli, A. Obregon, and H. Gregow
Adv. Sci. Res., 12, 187–198, https://doi.org/10.5194/asr-12-187-2015, https://doi.org/10.5194/asr-12-187-2015, 2015
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Wind speed measured at the German stations correlate well with reanalysis fields. Monthly means from two global reanalyses (ERA-20C, ERA-Interim) and one regional reanalysis (COSMO-REA6) were analysed and correlate well for the majority of the German stations. Thus we conclude that the monthly and seasonal anomalies recorded at these stations can be understood as representative for a spatial area comparable to the resolution of the reanalyses, at least for the recent years.
F. Kaspar, J. Helmschrot, A. Mhanda, M. Butale, W. de Clercq, J. K. Kanyanga, F. O. S. Neto, S. Kruger, M. Castro Matsheka, G. Muche, T. Hillmann, K. Josenhans, R. Posada, J. Riede, M. Seely, C. Ribeiro, P. Kenabatho, R. Vogt, and N. Jürgens
Adv. Sci. Res., 12, 171–177, https://doi.org/10.5194/asr-12-171-2015, https://doi.org/10.5194/asr-12-171-2015, 2015
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One task of the “Southern African Science Service Centre for Climate Change and Adaptive Land Management” (www.sasscal.org) is the provision of climate data for Southern Africa. Extension and improvements of observational networks in Angola, Botswana, Namibia, Zambia and South Africa are supported. This effort is complemented by an improvement of climate data management at national weather authorities, capacity building activities and an extension of the data bases with historical climate data.
H. Gregow, P. Poli, H. M. Mäkelä, K. Jylhä, A. K. Kaiser-Weiss, A. Obregon, D. G. H. Tan, S. Kekki, and F. Kaspar
Adv. Sci. Res., 12, 63–67, https://doi.org/10.5194/asr-12-63-2015, https://doi.org/10.5194/asr-12-63-2015, 2015
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Many users of climate information are unaware of the availability of reanalysis feedback data and input observations, and uptake of feedback data is rather low. The most important factors limiting the use of this data is that the users feel that there is no easy interface to get the data or they do not find it at all. The relevant communities should invest resources to develop tools and provide training to bridge the gap between current capabilities and comprehensive exploitation of the data.
F. Kaspar, B. Tinz, H. Mächel, and L. Gates
Adv. Sci. Res., 12, 57–61, https://doi.org/10.5194/asr-12-57-2015, https://doi.org/10.5194/asr-12-57-2015, 2015
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Germany’s national meteorological service (Deutscher Wetterdienst, DWD) houses in Offenbach and Hamburg huge archives of historical handwritten journals of weather observations. They comprise not only observations from Germany, but also of the oceans and land stations in many parts of the world. DWD works on the digitisation and quality control of these archives. The paper presents the current status.
F. Kaspar, K. Zimmermann, and C. Polte-Rudolf
Adv. Sci. Res., 11, 93–99, https://doi.org/10.5194/asr-11-93-2014, https://doi.org/10.5194/asr-11-93-2014, 2014
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Plant phenology is the study of periodically recurring patterns of growth and development of plants during the year. First plant phenological observations have been performed in Germany already in the 18th century. Today, Germany’s national meteorological service (Deutscher Wetterdienst, DWD) maintains a dense phenological observation network and a database with phenological observations.
J. Peng, M. Borsche, Y. Liu, and A. Loew
Hydrol. Earth Syst. Sci., 17, 3913–3919, https://doi.org/10.5194/hess-17-3913-2013, https://doi.org/10.5194/hess-17-3913-2013, 2013
F. Kaspar, G. Müller-Westermeier, E. Penda, H. Mächel, K. Zimmermann, A. Kaiser-Weiss, and T. Deutschländer
Adv. Sci. Res., 10, 99–106, https://doi.org/10.5194/asr-10-99-2013, https://doi.org/10.5194/asr-10-99-2013, 2013
K.-G. Karlsson, A. Riihelä, R. Müller, J. F. Meirink, J. Sedlar, M. Stengel, M. Lockhoff, J. Trentmann, F. Kaspar, R. Hollmann, and E. Wolters
Atmos. Chem. Phys., 13, 5351–5367, https://doi.org/10.5194/acp-13-5351-2013, https://doi.org/10.5194/acp-13-5351-2013, 2013
A. Riihelä, T. Manninen, V. Laine, K. Andersson, and F. Kaspar
Atmos. Chem. Phys., 13, 3743–3762, https://doi.org/10.5194/acp-13-3743-2013, https://doi.org/10.5194/acp-13-3743-2013, 2013
S. Dietrich, M. Werner, T. Spangehl, and G. Lohmann
Clim. Past, 9, 13–26, https://doi.org/10.5194/cp-9-13-2013, https://doi.org/10.5194/cp-9-13-2013, 2013
Cited articles
Akhtar, N., Geyer, B., Rockel, B., Sommer, P. S., and Schrum, C.: Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials, Sci. Rep., 11, 11826, https://doi.org/10.1038/s41598-021-91283-3, 2021. a
Akhtar, N., Geyer, B., and Schrum, C.: Impacts of accelerating deployment of offshore windfarms on near-surface climate, Sci. Rep., 12, 18307, https://doi.org/10.1038/s41598-022-22868-9, 2022. a, b
Andersson, A., Fennig, K., Klepp, C., Bakan, S., Graßl, H., and Schulz, J.: The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3, Earth Syst. Sci. Data, 2, 215–234, https://doi.org/10.5194/essd-2-215-2010, 2010. a, b
Andersson, A., Graw, K., Schröder, M., Fennig, K., Liman, J., Bakan, S., Hollmann, R., and Klepp, C.: Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS 4.0, EUMETSAT [data set], https://doi.org/10.5676/EUM_SAF_CM/HOAPS/V002, 2017. a, b
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belšić, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Christensen, O. B., Fernández, J., Fita, L., Frisius, T., Gšparac, G., Giorgi, F., Goergen, K., Haugen, J. E., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Remedio, A. R., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. M., Stocchi, P., Tölle, M. H., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K., Wulfmeyer, V., and Zander, M. J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w, 2021. a
Berg, P., Wagner, S., Kunstmann, H., and Schädler, G.: High resolution regional climate model simulations for Germany: part I – validation, Clim. Dynam., 40, 401–414, https://doi.org/10.1007/s00382-012-1508-8, 2013. a
BNetzA: Monitoringbericht 2022 von Bundesnetzagentur und Bundeskartellamt, https://data.bundesnetzagentur.de/Bundesnetzagentur/SharedDocs/Mediathek/Monitoringberichte/monitoringberichtenergie2022.pdf (last access: 6 November 2023), 2023. a
Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S. ., Pscheidt, I., Redl, S., and Steinke, S.: Towards a high-resolution regional reanalysis for the European CORDEX domain, Q. J. Roy. Meteorol. Soc., 141, 1–15, https://doi.org/10.1002/qj.2486, 2015. a, b, c
Borgers, R., Dirksen, M., Wijnant, I. L., Stepek, A., Stoffelen, A., Akhtar, N., Neirynck, J., Van de Walle, J., Meyers, J., and van Lipzig, N. P. M.: Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-33, in review, 2023. a
Borsche, M., Kaiser-Weiss, A. K., and Kaspar, F.: Wind speed variability between 10 and 116 m height from the regional reanalysis COSMO-REA6 compared to wind mast measurements over Northern Germany and the Netherlands, Adv. Sci. Res., 13, 151–161, https://doi.org/10.5194/asr-13-151-2016, 2016. a
Brienen, S., Früh, B., Walter, A., Trusilova, K., and Becker, P.: A Central European precipitation climatology Part II: Application of the high-resolution HYRAS data for COSMO-CLM evaluation, Meteorol. Z., 25, 195–214, https://doi.org/10.1127/metz/2016/0617, 2016. a
Brienen, S., Haller, M., Brauch, J., and Früh, B.: HoKliSim-De COSMO-CLM climate model simulation data version V2022.01, DWD [data set], https://doi.org/10.5676/DWD/HOKLISIM_V2022.01, 2022. a, b, c
Brousseau, P., Bouttier, F., Hello, G., Seity, Y., Berre, L., Montmerle, T., Auger, L., and Malardel, S.: A prototype convective-scale data assimilation system for operation: the Arome-RUC, in: HIRLAM Tech. Rep., vol. 68, 23–30, CNRM – Centre national de recherches météorologiques, CNRM-GAME, Météo-France and CNRS, Toulouse, France, http://www.umr-cnrm.fr/ (last access: 6 November 2023), 2008. a
Brune, S., Keller, J. D., and Wahl, S.: Evaluation of wind speed estimates in reanalyses for wind energy applications, Adv. Sci. Res., 18, 115–126, https://doi.org/10.5194/asr-18-115-2021, 2021. a
Bundesamt für Seeschifffahrt und Hydrographie: Nordsee-Handbuch, südöstlicher Teil: Lister Tief bis Ems, in: 7th Edn., Hamburg, Rostock, 399 pp., ISSN 2190-037X, 2023a. a
Bundesamt für Seeschifffahrt und Hydrographie: Forschungsplattformen in Nord- und Ostsee, FINO data, Bundesamt für Seeschifffahrt und Hydrographie [data set], https://www.bsh.de/EN/TOPICS/Monitoring_systems/MARNET_monitoring_network/FINO/fino_node.html (last access: 6 November 2023), 2023b. a
CEDIM: Orkantief “Christian”, West- und Mitteleuropa, 27.–29.10.2013, CEDIM – Center for Disaster Management and Risk Reduction Technology, http://www.wettergefahren-fruehwarnung.de/Ereignis/20131031_e.html(last access: 17 February 2023), 2013. a
de Kloe, J., Stoffelen, A., and Verhoef, A.: Improved Use of Scatterometer Measurements by Using Stress-Equivalent Reference Winds, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 2340–2347, https://doi.org/10.1109/JSTARS.2017.2685242, 2017. a
Deutscher Wetterdienst: COSMO-REA6 at DWD open data server, Deutscher Wetterdienst [data set], https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/ (last access: 6 November 2023), 2023. a
Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D., Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vogel, G.: A Description of the Nonhydrostatic Regional COSMO-Model, Part II Physical Parameterizations, DWD, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_5.00_II, 2013. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the New European Wind Atlas – Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a
El-Said, A., Brousseau, P., Ridal, M., and Randriamampianina, R.: Towards Full Flow-Dependence: New Temporally Varying EDA Quotient Functionality to Estimate Background Errors in CERRA, J. Adv. Model. Earth Syst., 14, e2021MS002637, https://doi.org/10.1029/2021MS002637, 2022. a
EU Copernicus Marine Service: WIND_GLO_PHY_L4_MY_012_006, EU Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00185, 2023. a
EUMETSAT: CM SAF – Product navigator, https://wui.cmsaf.eu (last access: 6 November 2023), 2023. a
Geyer, B., Weisse, R., Bisling, P., and Winterfeldt, J.: Climatology of North Sea wind energy derived from a model hindcast for 1958–2012, J. Wind Eng. Indust. Aerodynam., 147, 18–29, https://doi.org/10.1016/j.jweia.2015.09.005, 2015. a
Gliksman, D., Averbeck, P., Becker, N., Gardiner, B., Goldberg, V., Grieger, J., Handorf, D., Haustein, K., Karwat, A., Knutzen, F., Lentink, H. S., Lorenz, R., Niermann, D., Pinto, J. G., Queck, R., Ziemann, A., and Franzke, C. L. E.: Review article: A European perspective on wind and storm damage – from the meteorological background to index-based approaches to assess impacts, Nat. Hazards Earth Syst. Sci., 23, 2171–2201, https://doi.org/10.5194/nhess-23-2171-2023, 2023. a
Gustafsson, N., Berre, L., Hörnquist, S., Huang, X.-Y., Lindskog, M., Navascués, B., Mogensen, K., and Thorsteinsson, S.: Three-dimensional variational data assimilation for a limited area model, Tellus A, 53, 425–446, https://doi.org/10.3402/tellusa.v53i4.12198, 2001. a
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, 2020. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P. C., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c, d
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Hsu, S. A., Meindl, E. A., and Gilhousen, D. B.: Determining the Power-Law Wind-Profile Exponent under Near-Neutral Stability Conditions at Sea, J. Appl. Meteorol. Clim., 33, 757–765, https://doi.org/10.1175/1520-0450(1994)033<0757:DTPLWP>2.0.CO;2, 1994. a
Kalverla, P. C., Duncan Jr., J. B., Steeneveld, G.-J., and Holtslag, A. A. M.: Low-level jets over the North Sea based on ERA5 and observations: together they do better, Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, 2019. a
Kaspar, F., Niermann, D., Borsche, M., Fiedler, S., Keller, J., Potthast, R., Rösch, T., Spangehl, T., and Tinz, B.: Regional atmospheric reanalysis activities at Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy, Adv. Sci. Res., 17, 115–128, https://doi.org/10.5194/asr-17-115-2020, 2020. a, b, c, d
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014. a, b
Krieger, D., Krueger, O., Feser, F., Weisse, R., Tinz, B., and von Storch, H.: German Bight storm activity, 1897–2018, Int. J. Climatol., 41, E2159–E2177, https://doi.org/10.1002/joc.6837, 2021. a
Leiding, T., Tinz, B., Gates, L., Rosenhagen, G., Herklotz, K., Senet, C., Outzen, O., Lindenthal, A., Neumann, T., Frühmann, R., Wilts, F., Bégué, F., Schwenk, P., Stein, D., Bastigkeit, I., Lange, B., Hagemann, S., Müller, S., and Schwabe, J.: ABSCHLUSSBERICHT, Förderprogramm des Bundesministeriums für Wirtschaft und Energie, Forschungsvorhaben FINO-Wind, Standardisierung und vergleichende Analyse der meteorologischen FINO-Messdaten (FINO123), https://www.dwd.de/DE/klimaumwelt/klimaforschung/klimaueberwachung/finowind/finodoku/abschlussbericht_pdf (last access: 4 November 2022), 2016. a, b, c, d
Lindskog, M., Gustafsson, N., Navascués, B., Mogensen, K., Huang, X.-Y., Yang, X., Andræ, U., Berre, L., Thorsteinsson, S., and Rantakokko, J.: Three-dimensional variational data assimilation for a limited area model, Tellus A, 53, 447–468, https://doi.org/10.3402/tellusa.v53i4.14578, 2001. a
Maas, O. and Raasch, S.: Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight, Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, 2022. a
NEWA Consortium: New European Wind Atlas, https://www.neweuropeanwindatlas.eu/ (last access: 6 November 2023), 2019. a
Niermann, D., Borsche, M., Kaiser-Weiss, A. K., and Kaspar, F.: Evaluating renewable-energy-relevant parameters of COSMO-REA6 by comparison with satellite data, station observations and other reanalyses, Meteorol. Z., 28, 347–360, https://doi.org/10.1127/metz/2019/0945, 2019. a
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K., Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep., 8, 2045–2322, https://doi.org/10.1038/s41598-018-20389-y, 2018. a
Podein, P., Tinz, B., Blender, R., and Detels, T.: Reconstruction of annual mean wind speed statistics at 100 m height of FINO1 and FINO2 masts with reanalyses and the geostrophic wind, Meteorol. Z., 31, 89–100, https://doi.org/10.1127/metz/2021/1090, 2022. a, b, c
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1573–1472, https://doi.org/10.1007/s10546-019-00473-0, 2020. a
Rausch, T., Cañadillas, B., Hampel, O., Simsek, T., Tayfun, Y. B., Neumann, T., Siedersleben, S., and Lampert, A.: Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight, Atmosphere, 13, 839, https://doi.org/10.3390/atmos13050839, 2022. a
Rockel, B., Will, A., and Hense, A.: The Regional Climate Model COSMO-CLM (CCLM), Meteorol. Z., 17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309, 2008. a
Schimanke, S., Ridal, M., Le Moigne, P., Berggren, L., Undén, P., Randriamampianina, R., Andrea, U., Bazile, E., Bertelsen, A., Brousseau, P., Dahlgren, P., Edvinsson, L., El Said, A., Glinton, M., Hopsch, S., Isaksson, L., Mladek, R., Olsson, E., Verrelle, A., and Wang, Z.: CERRA sub-daily regional reanalysis data for Europe on height levels from 1984 to present, CDS [data set], https://doi.org/10.24381/cds.38b394e6, 2021a. a, b, c
Schimanke, S., Ridal, M., Le Moigne, P., Berggren, L., Undén, P., Randriamampianina, R., Andrea, U., Bazile, E., Bertelsen, A., Brousseau, P., Dahlgren, P., Edvinsson, L., El Said, A., Glinton, M., Hopsch, S., Isaksson, L., Mladek, R., Olsson, E., Verrelle, A., and Wang, Z.: CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present, CDS [data set], https://doi.org/10.24381/cds.622a565a, 2021b. a, b, c
Sedefian, L.: On the Vertical Extrapolation of Mean Wind Power Density, J. Appl. Meteorol. Clim., 19, 488–493, https://doi.org/10.1175/1520-0450(1980)019<0488:OTVEOM>2.0.CO;2, 1980. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Spangehl, T. and Tinz, B.: Bericht, Ensemble von Reanalysen für Ausschreibungsverfahren 2021 der BNetzA nach WindSeeG – hier: für die Flächen N-3.7, N-3.8 und O-1.3, https://pinta.bsh.de/ (last access: 3 February 2023), 2021. a
Stark, J. D., Ridley, J., Martin, M., and Hines, A.: Sea ice concentration and motion assimilation in a sea ice−ocean model, J. Geophys. Res., 113, C05S91, https://doi.org/10.1029/2007JC004224, 2008. a
Steger, C. and Bucchignani, E.: Regional Climate Modelling with COSMO-CLM: History and Perspectives, Atmosphere, 11, 1250, https://doi.org/10.3390/atmos11111250, 2020. a
Taillefer, F.: CANARI (Code for the Analysis Necessary for Arpege for its Rejects and its Initialization): Technical documentation, Technical documentation, Internal CNRM/GMAP Rep., XXX, 55 pp., Groupe de Mode ́l. pour l'Assimilationet la Prévision, Cent. Natl. de Rech. Météorol., Météo-France, Toulouse, France, http://www.cnrm.meteo.fr/gmapdoc/spip.php?article3 (last access: 6 November 2023), 2002. a
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteorol. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005. a
Vautard, R., Kadygrov, N., Iles, C., Boberg, F., Buonomo, E., Bülow, K., Coppola, E., Corre, L., van Meijgaard, E., Nogherotto, R., Sandstad, M., Schwingshackl, C., Somot, S., Aalbers, E., Christensen, O. B., Ciarlo, J. M., Demory, M.-E., Giorgi, F., Jacob, D., Jones, R. G., Keuler, K., Kjellström, E., Lenderink, G., Levavasseur, G., Nikulin, G., Sillmann, J., Solidoro, C., Sørland, S. L., Steger, C., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Evaluation of the Large EURO-CORDEX Regional Climate Model Ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032344, https://doi.org/10.1029/2019JD032344, 2021. a
von Storch, H., Feser, F., Geyer, B., Klehmet, K., Li, D., Rockel, B., Schubert-Frisius, M., Tim, N., and Zorita, E.: Regional reanalysis without local data: Exploiting the downscaling paradigm, J. Geophys. Res.-Atmos., 122, 8631–8649, https://doi.org/10.1002/2016JD026332, 2017. a
Wahl, S., Bollmeyer, C., Crewell, S., Figura, C., Friederichs, P., Hense, A., Keller, J. D., and Ohlwein, C.: A novel convective-scale regional reanalysis COSMO-REA2: Improving the representation of precipitation, Meteorol. Z., 26, 345–361, https://doi.org/10.1127/metz/2017/0824, 2017. a
Wang, Z. Q. and Randriamampianina, R.: The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA), Remote Sens., 13, 426, https://doi.org/10.3390/rs13030426, 2021. a
WindSeeG: Gesetz zur Entwicklung und Förderung der Windenergie auf See, http://www.gesetze-iminternet.de/windseeg/ (last access: 6 November 2023), 2023. a
Winterfeldt, J., Geyer, B., and Weisse, R.: Using QuikSCAT in the added value assessment of dynamically downscaled wind speed, Int. J. Climatol., 31, 1028–1039, https://doi.org/10.1002/joc.2105, 2011. a, b
Short summary
The quality of the global reanalysis ERA5, the regional reanalysis COSMO-REA6 and a successor version (R6G2), the new Copernicus European Regional Re-Analysis (CERRA) and a regional downscaling simulation with COSMO-CLM (HoKliSim-De) is assessed for offshore wind farm planning in the German Exclusive Economic Zone (EEZ) of the North Sea. The quality is assessed using in-situ wind measurements at the research platform FINO1 and satellite-based data of the near-surface wind speed as reference.
The quality of the global reanalysis ERA5, the regional reanalysis COSMO-REA6 and a successor...
