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Reanalysis: Data Assimilation for Scientific Investigation of Climate

Chapter

Abstract

Reanalysis is the assimilation of long time series of observations with an unvarying assimilation system to produce datasets for a variety of applications; for example, climate variability, chemistry-transport, and process studies. Reanalyses were originally proposed for atmospheric observations as a method to generate “climate” datasets from “weather” observations. As the satellite records of chemical, land and oceanic parameters build with time, “reanalyses” are being developed for other types of observations. Coupled reanalyses, for example atmospheric-ocean reanalyses, are possible.

Keywords

Data Assimilation Outgoing Longwave Radiation Assimilation System Reanalysis Dataset Reanalysis Product 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank L. Bengtsson, D. Dee, and K. Trenberth for reviewing the chapter and providing many useful comments.

References

  1. Adler, R.F., G.J. Huffman, A. Chang, et al., 2003. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeor., 4, 1147–1167.CrossRefGoogle Scholar
  2. Bengtsson, L., P. Arkin, P. Berrisford, et al., 2007. The need for dynamical climate assimilation. Bull. Amer. Meteorol. Soc., 88, 495–501.CrossRefGoogle Scholar
  3. Bengtsson, L., S. Hagemann and K.I. Hodges, 2004. Can climate trends be calculated from reanalysis data? J. Geophys. Res., 109, D11111, doi:10.1029/2004JD004536.CrossRefGoogle Scholar
  4. Bengtsson, L. and J. Shukla, 1988. Integration of space and in situ observations to study global climate change, Bull. Amer. Meteorol. Soc., 69, 1130–1143.CrossRefGoogle Scholar
  5. Betts, A.K., 2004. Understanding hydrometeorology using global models. Bull. Amer. Meteorol. Soc., 85, 1673–1688.CrossRefGoogle Scholar
  6. Betts, A.K. and M.G. Bosilovich, 2008. Comparison of MERRA with ERA-40 on river basin scales. Session on Advances in Atmospheric Reanalyses, American Meteorological Society Annual Meeting, New Orleans, LA, January 23, 2008.Google Scholar
  7. Betts, A.K., M. Zhao, P.A. Dirmeyer and A.C.M. Beljaars, 2006. Comparison of ERA40 and NCEP/DOE near-surface datasets with other ISLSCP-II datasets. J. Geophys. Res., 111, D22S04, doi:10.1029/2006JD007174.Google Scholar
  8. Bey, I., D.J. Jacob, R.M. Yantosca, et al., 2001. Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation. J. Geophys. Res., 106, 23073–23095.CrossRefGoogle Scholar
  9. Bosilovich, M.G., J. Chen, F.R. Robertson and R.F. Adler, 2008. Evaluation of global precipitation in reanalyses. J. Appl. Meteor. and Climat., 47, 2279–2299.Google Scholar
  10. Bosilovich, M.G., D. Mocko, J.O. Roads and A. Ruane, 2009. A multi-model analysis for the Coordinated Enhanced Observing Period (CEOP). J. Hydrometeorol., 10, 912–934.Google Scholar
  11. Bosilovich, M.G. and S.D. Schubert, 2001. Precipitation recycling over the central United States as diagnosed from the GEOS1 Data Assimilation System. J. Hydrometeorol., 2, 26–35.CrossRefGoogle Scholar
  12. Bromwich, D.H., A.N. Rogers, P. Kållberg, et al., 2000. ECMWF analyses and reanalyses depiction of ENSO signal in Antarctic precipitation. J. Climate, 13, 1406–1420.CrossRefGoogle Scholar
  13. Bromwich, D.H., R.L. Fogt, K.I. Hodges and J.E. Walsh, 2007. A tropospheric assessment of the ERA-40, NCEP, and JRA-25 global reanalyses in the polar regions. J. Geophys. Res., 122, D10111, doi: 10.1029/2006JD007859.CrossRefGoogle Scholar
  14. Chen J., A.D. Del Genio, B.E. Carlson and M.G. Bosilovich, 2008a. The spatiotemporal structure of 20th century climate variations in observations and reanalyses. Part I: Long-term trend. J. Climate, 21, 2611–2633.CrossRefGoogle Scholar
  15. Chen J., A.D. Del Genio, B.E. Carlson and M.G. Bosilovich, 2008b. The spatiotemporal structure of 20th century climate variations in observations and reanalyses. Part II: Pacific pan-decadal variability. J. Climate, 21, 2634–2650.CrossRefGoogle Scholar
  16. Compo, G.P., J.S. Whitaker and P.D. Sardeshmukh, 2006. The feasibility of a 100-year reanalysis using only surface pressure data. Bull. Amer. Meteorol. Soc, 87, 175–190.CrossRefGoogle Scholar
  17. Cullather R.I., D.H. Bromwich and M.L. Van Woert, 1998. Spatial and temporal variability of Antarctic precipitation from atmospheric methods. J. Climate, 11, 334–367.CrossRefGoogle Scholar
  18. Dee, D.P., 2005. Bias and data assimilation. Q. J. R. Meteorol. Soc., 131, 3323–3342.CrossRefGoogle Scholar
  19. Dee, D.P. and A. da Silva, 1998. Data assimilation in the presence of forecast bias. Q. J. R. Meteorol. Soc., 124, 269–295.CrossRefGoogle Scholar
  20. Douglass, A.R., M.R. Schoeberl, R.B. Rood and S. Pawson, 2003. Evaluation of transport in the Lower Tropical Stratosphere in a global chemistry and transport model. J. Geophys. Res., 108, Art. No. 4259.Google Scholar
  21. Fekete, B.M., C.J. Vorosmarty, J.O. Roads and C.J. Willmott, 2004. Uncertainties in Precipitation and their impacts on runoff estimates. J. Climate, 17, 294–302.CrossRefGoogle Scholar
  22. Gibson, J.K., P. Kållberg, S. Uppala, et al., 1997. ERA Description, ECMWF Re-analysis Final Report Series, 1.Google Scholar
  23. Hagemann, S. and L.D. Gates, 2001. Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model. J. Geophys. Res., 106, 1503–1510.CrossRefGoogle Scholar
  24. Haimberger, L., 2006. Homogenization of radiosonde temperature time series using innovation statistics. J. Climate, 20, 1377–1403.CrossRefGoogle Scholar
  25. Hou, A.Y., S.Q. Zhang, A.M. da Silva, et al., 2001. Improving global analysis and short-range forecast using rainfall and moisture observations derived from TRMM and SSM/I passive microwave sensors. Bull. Amer. Meteorol. Soc., 81, 659–679.CrossRefGoogle Scholar
  26. Hou, A.Y., S.Q. Zhang and O. Reale, 2002. Variational continuous assimilation of TMI and SSM/I rain rates: Impact on GEOS-3 analysis and forecasts. Mon. Weather Rev., 132, 2094–2109.CrossRefGoogle Scholar
  27. Kalnay, E. and R. Jenne, 1991. Summary of the NMC/NCAR Reanalysis Workshop of April 1991. Bull. Amer. Meteorol. Soc., 72, 1897–1904.Google Scholar
  28. Kalnay E., M. Kanamitsu, R. Kistler, et al., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteorol. Soc., 77, 437–471.CrossRefGoogle Scholar
  29. Kanamitsu, M., W. Ebisuzaki, J. Woollen, et al., 2002. NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteorol. Soc., 83, 1631–1643.CrossRefGoogle Scholar
  30. Kistler R., E. Kalnay, W. Collins, et al., 2001. The NCEP/NCAR 50-year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteorol. Soc., 82, 247–267.CrossRefGoogle Scholar
  31. Lait, L.R., 2002. Systematic differences between radiosonde measurements. Geophys. Res. Lett., 29, doi:10.1029/2001GL014337.Google Scholar
  32. Lin, S.J., 2004. A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Weather Rev, 132, 2293–2307.CrossRefGoogle Scholar
  33. Mesinger, F., G. DiMego, E. Kalnay, et al., 2006. North American regional reanalysis. Bull. Amer. Meteorol. Soc., 87, 343–360.CrossRefGoogle Scholar
  34. Newman M., P.D. Sardeshmukh and J.W. Bergman, 2000. An assessment of the NCEP, NASA, and ECMWF reanalyses over the tropical west Pacific warm pool. Bull. Amer. Meteorol. Soc., 81, 41–48.CrossRefGoogle Scholar
  35. Newson, R.,1998. Results of the WCRP First International Conference on Reanalysis, GEWEX News, 8, 3–4.Google Scholar
  36. Onogi, K., 2000. ERA-40 Project Report Series 2. The long-term performance of the radiosonde observing system to be used in ERA-40, European Centre for Medium-Range Weather Forecasts, August 2000, 77pp.Google Scholar
  37. Onogi, K., J. Tsutsui, H. Koide, et al., 2007. The JRA-25 reanalysis. J. Meteor. Soc. Jpn., 85, 369–432.CrossRefGoogle Scholar
  38. Ozone Trends Panel, 1988. WMO Report of the International Ozone Trends Panel. World Meteorological Organization Global Ozone Research and Monitoring Project, Report No. 18.Google Scholar
  39. Pavelsky, T.M. and L.C. Smith, 2006. Intercomparison of four global precipitation datasets and their correlation with increased Eurasian river discharge to the Arctic Ocean. J. Geophys. Res., 111, D21112, doi:10.1029/2006JD007230.CrossRefGoogle Scholar
  40. Pawson, S., I. Štajner, S.R. Kawa, H. Hayashi, W.-W. Tan, J.E. Nielsen, Z. Zhu, L.-P. Chang and N.J. Livesey, 2007. Stratospheric transport using 6-h-averaged winds from a data assimilation system. J. Geophys. Res., 112, D23103, doi:10.1029/2006JD007673.CrossRefGoogle Scholar
  41. Redder, C.R., J.K. Luers and R.E. Eskridge, 2004. Unexplained discontinuity in the U.S. radiosonde temperature data, Part II: Stratosphere. J. Atmos. Oceanic Tech., 21, 1133–1144.CrossRefGoogle Scholar
  42. Roads J. and A.K. Betts, 2000. NCEP–NCAR and ECMWF Reanalysis surface water and energy budgets for the Mississippi River Basin. J. Hydrometeorol., 1, 88–94.CrossRefGoogle Scholar
  43. Rood, R.B., 2003. Reanalysis. In Data Assimilation for the Earth System. NATO Science Series: IV. Earth and Environmental Sciences 26, Swinbank, R., V. Shutyaev and W.A. Lahoz (eds.), Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 361–372, 378pp.CrossRefGoogle Scholar
  44. Rood, R.B., D.J. Allen, W. Baker, et al., 1989. The use of assimilated stratospheric data in constituent transport calculations. J. Atmos. Sci., 46, 687–701.CrossRefGoogle Scholar
  45. Santer, B.D., T.M.L. Wigley, A.J. Simmons, et al., 2004. Identification of anthropogenic climate change using a second-generation reanalysis. J. Geophys. Res., 109, D21104, doi:10.1029/2004JD005075.CrossRefGoogle Scholar
  46. Schoeberl, M.R., A.R. Douglass, Z. Zhu and S. Pawson, 2003. A comparison of the lower stratospheric age-spectra derived from a general circulation model and two data assimilation systems. J. Geophys. Res., 108, Art. No. 4113.Google Scholar
  47. Schubert, S.D. and Y. Chang, 1996. An objective method for inferring sources of model error. Mon. Weather Rev., 124, 325–340.CrossRefGoogle Scholar
  48. Schubert S.D., R.B. Rood and J. Pfaendtner, 1993. An assimilated dataset for earth-science applications. Bull. Amer. Meteorol. Soc., 74, 2331–2342.CrossRefGoogle Scholar
  49. Serreze, M.C., M.P. Clark and D.H. Bromwich, 2003. Monitoring precipitation over the Arctic terrestrial drainage system: Data requirements, shortcomings, and applications of atmospheric reanalysis. J. Hydrometeorol., 4, 387–407.CrossRefGoogle Scholar
  50. Serreze, M.C. and C.M. Hurst, 2000. Representation of Arctic precipitation from NCEP–NCAR and ERA Reanalyses. J. Climate, 13, 182–201.CrossRefGoogle Scholar
  51. Simmons, A.J., P.D. Jones, V. da Costa Bechtold, et al., 2004. Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature. J. Geophys. Res., 109, D24115, doi:10.1029/2004JD005306.CrossRefGoogle Scholar
  52. Štajner, I., N. Winslow, R.B. Rood and S. Pawson, 2004. Monitoring of observation errors in the assimilation of satellite ozone data, J. Geophys. Res., 109, D06309, doi:10.1029/2003JD004118.Google Scholar
  53. Stohl, A., O.R. Cooper and P. James, 2004. A cautionary note on the use of meteorological analysis fields for quantifying atmospheric mixing. J. Atmos. Sci., 61, 1446–1453.CrossRefGoogle Scholar
  54. Tan, W.-W., M.A. Geller, S. Pawson and A. da Silva, 2004. A case study of excessive subtropical transport in the stratosphere of a data assimilation system. J. Geophys. Res., 109, Art. No. D11102.Google Scholar
  55. Trenberth, K.E., J.T. Fasullo and J. Kiehl, 2008a. Earth’s global energy budget. Bull. Amer. Meteorol. Soc., doi:10.1175/2008BAMS2634.1.Google Scholar
  56. Trenberth, K.E., T.R. Karl. and T.W. Spence, 2002. The need for a systems approach to climate observations. Bull. Amer. Meteorol., Soc, 83, 1593–1602.CrossRefGoogle Scholar
  57. Trenberth, K.E., T. Koike and K. Onogi, 2008b. Progress and prospects for reanalysis for weather and climate, Eos, Trans. Amer. Geophys. Union, 89, 234–235.CrossRefGoogle Scholar
  58. Trenberth, K.E. and J.G. Olson, 1988. An evaluation and intercomparison of global analyses from NMC and ECMWF. Bull. Amer. Meteorol. Soc., 69, 1047–1057.CrossRefGoogle Scholar
  59. Trenberth, K.E. and L. Smith, 2008. Atmospheric energy budgets in the Japanese Reanalysis: Evaluation and variability. J. Meteor. Soc. Jpn., 86, 579–592.CrossRefGoogle Scholar
  60. Trenberth, K.E. and L. Smith, 2009. The three dimensional structure of the atmospheric energy budget: Methodology and evaluation. Climate Dyn., 32, doi:10.1007/s00382-008-0389-3.Google Scholar
  61. Uppala, S.M., P.W. Kållberg, A.J. Simmons, et al., 2005. The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 2961–3012.CrossRefGoogle Scholar
  62. Viterbo, P. and A.K. Betts, 1999. Impact of the ECMWF reanalysis soil water on forecasts of the July 1993 Mississippi flood. J. Geophys. Res., 104, 19361–19366.CrossRefGoogle Scholar
  63. WCRP, 1998. Proceedings of the 1st WCRP International Conference on Reanalyses. Silver Spring, MD, USA, 27–31 October, 1997, WMO/TD-N 876.Google Scholar
  64. WCRP, 2000. Proceedings of the 2nd WCRP International Conference on Reanalyses. Wokefield Park, nr. Reading, UK, 23–27 August 1999, WCRP-109, WMO/TD-N 985.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA
  2. 2.NASA Goddard Space Flight CenterGreenbeltUSA

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