156:, forecast models are used to predict future states of the atmosphere, based on how the climate system evolves with time from an initial state. The initial state provided as input to the forecast must consist of data values for a range of "prognostic" meteorological fields – that is, those fields which determine the future evolution of the model. Spatially varying fields are required in the form used by the model, for example at each intersection point on a regular grid of longitude and latitude circles, and initial data must be valid at a single time that corresponds to the present or the recent past. By contrast, the available observational data usually do not include all of the model's prognostic fields, and may include other additional fields; these data also have different spatial distribution from the forecast model grid, are valid over a range of times rather than a single time, and are also subject to observational error. The technique of
238:) and covers 45 years to 2002. As a precursor to a revised extended reanalysis product to replace ERA-40, ECMWF released ERA-Interim, which covers the period from 1979 to 2019. A new reanalysis product ERA5 has more recently been released by ECMWF as part of Copernicus Climate Change Services. This product has higher spatial resolution (31 km) and covers the period from 1979 to present. Extension up to 1940 became available in 2023.
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inconsistency if it spans any extended period of time, because operational analysis systems are frequently being improved. A reanalysis project involves reprocessing observational data spanning an extended historical period using a consistent modern analysis system, to produce a dataset that can be used for meteorological and climatological studies.
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Kaspar, F., Niermann, D., Borsche, M., Fiedler, S., Keller, J., Potthast, R., Rösch, T., Spangehl, T., and Tinz, B., 2020: Regional atmospheric reanalysis activities at
Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy, Adv. Sci. Res., 17,
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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,
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In addition to reanalysing all the old data using a consistent system, the reanalyses also make use of much archived data that was not available to the original analyses. This allows for the correction of many historical hand-drawn maps where the estimation of features was common in areas of data
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reanalysis conducted by the Japan
Meteorological Agency. In addition to these global reanalysis projects, there are also high-resolution regional reanalysis activities for different regions, e.g. for North America, Europe or Australia. Such regional reanalyses are typically based on a regional
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In addition to initializing operational forecasts, the analyses themselves are a valuable tool for subsequent meteorological and climatological studies. However, an operational analysis dataset, i.e. the analysis data which were used for the real-time forecasts, will typically suffer from
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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. R. Meteorol. Soc., 141, 1–15, 2015,
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Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H., 2019: BARRA v1.0: the Bureau of
Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049-2068,
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M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E. A., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Radnoti, G., Rosnay, P. D., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N., 2020: The
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Kaiser-Weiss, A. K., Borsche, M., Niermann, D., Kaspar, F. Lussana, C., Isotta, F., van den
Besselaar, E., van der Schrier, G., and Undén, P.: Added value of regional reanalyses for climatological applications, Environmental Research Communications, 2019.
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Kaiser-Weiss, A. K., Kaspar, F., Heene, V., Borsche, M., Tan, D. G. H., Poli, P., Obregon, A., and Gregow, H., 2015: Comparison of regional and global reanalysis near-surface winds with station observations over
Germany, Adv. Sci. Res., 12, 187-198,
234:(ECMWF). The first reanalysis product, ERA-15, generated reanalyses for approximately 15 years, from December 1978 to February 1994. The second product, ERA-40 (originally intended as a 40-year reanalysis) begins in 1957 (the
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Nigam, S., and A. Ruiz-Barradas, 2006: Seasonal
Hydroclimate Variability over North America in Global and Regional Reanalyses and AMIP Simulations: Varied Representation. J. Climate, 19, 815–837.
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project which aims to assimilate historical atmospheric observational data spanning an extended period, using a single consistent assimilation (or "analysis") scheme throughout.
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Peres, D. J.; Iuppa, C.; Cavallaro, L.; Cancelliere, A.; Foti, E. (2015-10-01). "Significant wave height record extension by neural networks and reanalysis wind data".
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Khatibi, A.; Krauter, S. Validation and
Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications. Energies 2021, 14, 882.
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Gelaro, R., and coauthors, 2017: The Modern-Era
Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Climate, 30, 5419-5454,
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Trenberth, K. E., D. P. Stepaniak, J. W. Hurrell, and M. Fiorino, 2001: Quality of
Reanalyses in the Tropics. J. Climate, 14, 1499–1510. ]
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sparsity. The ability is also present to create new maps of atmosphere levels that were not commonly used until more recent times.
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Parker, W.S., 2016: Reanalyses and Observations: What’s the Difference? Bull. Amer. Meteor. Soc., 97, 1565–1572,
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Diverse studies use reanalysis data for reproducing other climatic variables by black-box models (e.g.
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Uppala, S., and coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012.
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models. Reanalyses are known not to conserve moisture.
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563:(R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643.
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278:(NWP) model output from 1948 to present.
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1059:DISPERSION21
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41:Please help
36:verification
33:
865:IFS (ECMWF)
704:Model types
347:: 128–140.
293:temperature
1522:Categories
1300:Biological
1089:PUFF-PLUME
1049:AUSTAL2000
908:GME / ICON
875:GEM / GDPS
824:GFDL CM2.X
550:Kalnay, E.
487:2023-03-26
327:References
297:satellites
200:, and the
99:April 2010
69:newspapers
1130:GEOS-Chem
647:from the
612:115–128,
182:sea state
1099:SAFE AIR
932:RR / RAP
627:See also
188:Examples
166:best fit
162:analysis
1135:CHIMERE
1094:RIMPUFF
1074:MERCURE
1054:CALPUFF
904:JMA-GSM
819:HadGEM1
802:Climate
349:Bibcode
301:surface
139:climate
133:) is a
125:(also:
83:scholar
1429:Social
1209:NOGAPS
1125:MOZART
1044:ATSTEP
1039:AERMOD
1018:ADCIRC
1008:MITgcm
950:HIRLAM
912:ARPEGE
895:NAVGEM
814:HadCM3
602:JRA-25
579:ERA-40
406:JRA-25
202:JRA-25
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1156:CLASS
1151:JULES
1120:CLaMS
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1013:FESOM
1003:FVCOM
984:HyCOM
970:HRDPS
946:RAQMS
890:NAEFS
849:ECHAM
844:CFSv2
656:from
482:ECMWF
305:aloft
289:winds
90:JSTOR
76:books
1177:CICE
1161:ISBA
1084:OSPM
1079:NAME
1069:MEMO
1064:ISC3
1034:ADMS
988:ROMS
966:RGEM
961:HWRF
954:LAPS
937:RAMS
885:MPAS
839:CESM
834:CCSM
829:CGCM
809:IGCM
591:ERA5
291:and
258:The
222:The
172:Uses
137:and
129:and
62:news
1214:RUC
1204:NGM
1199:MM5
1195:LFM
1192:Eta
998:MOM
993:POM
957:RPM
942:WRF
927:NAM
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