167:, 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
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249:) 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,
245:(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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Kalnay, E., and coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471. ]
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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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289:(NWP) model output from 1948 to present.
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1070:DISPERSION21
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52:Please help
47:verification
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876:IFS (ECMWF)
715:Model types
358:: 128–140.
304:temperature
1533:Categories
1311:Biological
1100:PUFF-PLUME
1060:AUSTAL2000
919:GME / ICON
886:GEM / GDPS
835:GFDL CM2.X
561:Kalnay, E.
498:2023-03-26
338:References
308:satellites
211:, and the
110:April 2010
80:newspapers
1141:GEOS-Chem
658:from the
623:115–128,
193:sea state
1110:SAFE AIR
943:RR / RAP
638:See also
199:Examples
177:best fit
173:analysis
1146:CHIMERE
1105:RIMPUFF
1085:MERCURE
1065:CALPUFF
915:JMA-GSM
830:HadGEM1
813:Climate
360:Bibcode
312:surface
150:climate
144:) is a
136:(also:
94:scholar
1440:Social
1220:NOGAPS
1136:MOZART
1055:ATSTEP
1050:AERMOD
1029:ADCIRC
1019:MITgcm
961:HIRLAM
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957:RAQMS
901:NAEFS
860:ECHAM
855:CFSv2
667:from
493:ECMWF
316:aloft
300:winds
101:JSTOR
87:books
1188:CICE
1172:ISBA
1095:OSPM
1090:NAME
1080:MEMO
1075:ISC3
1045:ADMS
999:ROMS
977:RGEM
972:HWRF
965:LAPS
948:RAMS
896:MPAS
850:CESM
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840:CGCM
820:IGCM
602:ERA5
302:and
269:The
233:The
183:Uses
148:and
140:and
73:news
1225:RUC
1215:NGM
1210:MM5
1206:LFM
1203:Eta
1009:MOM
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938:NAM
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