Reanalysis (meteorology)

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A meteorological reanalysis is a method of creating longer-term meteorological data sets using models of weather forecast and assimilation of historical observation data . The result is typically a multi-year, three-dimensionally consistent description of the atmospheric condition. These data sets are used in many application areas that rely on long-term meteorological data, for example in the field of renewable energies . In addition to global reanalyses with worldwide coverage, there are also regional reanalyses that cover individual regions with a higher spatial resolution.

Producers

Meteorological reanalyses are typically produced by weather services or international meteorological centers (in Europe, for example, ECMWF as part of the Copernicus Climate Change Service C3S) on the basis of their numerical models and the archived observation data . Various centers around the world generate global reanalysis data sets and, to an increasing extent, regional reanalyses for different regions.

Known reanalysis datasets

Global reanalyses

  • ERA5, ERA-20C and ERA-Interim: ECMWF / Copernicus -Climawandeldienst (Copernicus Climate Change Service, C3S)
  • JRA-55: Japanese 55 year old reanalysis
  • MERRA / MERRA-2 (NASA)
  • NCEP / CFSR: Climate Forecast System Reanalysis

Regional reanalyses for Europe

  • COSMO-REA6 of the German Weather Service
  • Copernicus Regional Reanalysis for Europe

Further regional reanalyses

  • NCEP North American Regional Reanalysis (NARR)
  • Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA v1.0)
  • Arctic System Reanalysis (ASR)

Application examples

Climate monitoring

As part of the Copernicus Climate Change Service (C3S), the global reanalysis ERA5 is used for evaluations of temperature developments worldwide and in Europe.

Renewable energy

In the field of renewable energies , the parameters wind speed and solar radiation are of particular interest from reanalyses. Since reanalyses also provide this information for areas without direct observations, they are used in a large number of studies and applications in this sector.

Web links

Individual evidence

  1. ^ Kaiser-Weiss, AK, Borsche, M., Niermann, D., Kaspar, F. Lussana, C., Isotta, F., van den Besselaar, E., van der Schrier, G., Undén, P .: Added value of regional reanalyses for climatological applications, Environmental Research Communications, Vol. 1, No. 7, 2019. DOI: 10.1088 / 2515-7620 / ab2ec3
  2. ↑ Dee, DP, Uppala, SM, Simmons, AJ, Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137 (656), 553–597. DOI: 10.1002 / qj.828
  3. ↑ Bollmeyer, C., Keller, JD, Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S., Steinke, S .: Towards a high-resolution regional reanalysis for the European CORDEX domain, QJR Meteorol. Soc., 141, 1-15, 2015, DOI: 10.1002 / qj.2486
  4. ↑ Kaspar, F., Niermann, D., Borsche, M., Fiedler, S., Keller, J., Potthast, R., Rösch, T., Spangehl, T., 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, DOI: 10.5194 / asr-17-115-2020 , 2020.
  5. ↑ Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, PC Shafran, W. Ebisuzaki, D. Jović, J. Woolen, E. Rogers, EH Berbery, MB Ek, Y. Fan, R. Grumbine , W. Higgins, H. Li, Y. Lin, G. Manikin, D. Parrish, W. Shi, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343-360, DOI: 10.1175 / BAMS-87-3-343
  6. ↑ Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, CJ, Rennie, S., Franklin, C., Dharssi, I., Zhu, H., 2019: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049-2068, DOI: 10.5194 / gmd-12-2049-2019
  7. ^ Copernicus: Surface air temperature maps
  8. ↑ Niermann, D., Borsche, M., Kaiser-Weiss, AK, Kaspar, F .: Evaluating renewable energy relevant parameters of COSMO-REA6 by comparing against station observations, satellites and other reanalyses, Meteorologische Zeitschrift, 2019; DOI: 10.1127 / metz / 2019/0945
  9. ^ Philipp Henckes, Andreas Knaut, Frank Obermüller, Christopher William Frank: The benefit of long-term high resolution wind data for electricity system analysis. Energy 143, 934-942, 2018. DOI: 10.1016 / j.energy.2017.10.049
  10. ^ Raik Becker, Daniela Thrän: Optimal Siting of Wind Farms in Wind Energy Dominated Power Systems. Energies 2018, 11, 978; DOI: 10.3390 / en11040978
  11. ^ Staffell, I .; Pfenninger, S .: Using bias-corrected reanalysis to simulate current and future wind power output, Energy, Volume 114, Pages 1224-1239,2016, DOI: 10.1016 / j.energy.2016.08.068
  12. ↑ Kaspar, F., Borsche, M., Pfeifroth, U., Trentmann, J., Drücke, J., Becker, P .: A climatological assessment of balancing effects and shortfall risks of photovoltaics and wind energy in Germany and Europe, Adv. Sci. Res., 16, 119-128, 2019; DOI: 10.5194 / asr-16-119-2019
  13. Jump up ↑ Simmer, C., G. Adrian, S. Jones, V. Wirth, M. Göber, C. Hohenegger, T. Janjic, J. Keller, C. Ohlwein, A. Seifert, S. Trömel, T. Ulbrich, K. Wapler, M. Weissmann, J. Keller, M. Masbou, S. Meilinger, N. Riß, A. Schomburg, A. Vormann, C. Weingärtner (2016): HErZ: The German Hans-Ertel Center for Weather Research . Bull. Am. Meteorol. Soc., 97 (6), pp. 1057-1068, DOI: 10.1175 / BAMS-D-13-00227.1 , 2016