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Observing-System Research and Ensemble Data Assimilation at JAMSTEC

  • Takeshi EnomotoEmail author
  • Takemasa Miyoshi
  • Qoosaku Moteki
  • Jun Inoue
  • Miki Hattori
  • Akira Kuwano-Yoshida
  • Nobumasa Komori
  • Shozo Yamane
Chapter

Abstract

Recent activities on ensemble data assimilation and its application to observing-system research at the Japan Agency for Marine-Earth Science and Technology are reviewed. A revised version of an ensemble-based data assimilation system for global atmospheric data has been developed on the second-generation Earth Simulator. This system assimilates conventional atmospheric observations and satellite-based wind data into an atmospheric general circulation model using the local ensemble transform Kalman filter (LETKF), a deterministic ensemble Kalman filter algorithm that is extremely efficient with parallel computer architecture. The updated system incorporates improvements to the previous system in the forecast model, data assimilation algorithm and input data. Using the LETKF system, observations taken during field campaigns are evaluated by data assimilation experiments involving adding or removing observations. The results of these observing-system experiments successfully demonstrate the value of the observations and are highly useful for exploring the predictability of atmospheric disturbances.

Keywords

Field Campaign Global Precipitation Climatology Project Ensemble Spread Earth Simulator Atmospheric Model Intercomparison Project 
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.

References

  1. Adler RF, et al (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitaion analysis (1979–present). J Hydrometeor 4:1147–1167CrossRefGoogle Scholar
  2. Betchold P, Köhler M, Jung T, Doblas-Reyes F, Leutbecher M, Rodwell MJ, Vitart F, Balsamo G (2008) Advances in simulating atmospheric variability with the ECMWF model: from synoptic to decadal time-scales. Q J R Meteor Soc 134:1337–1351. doi:10.1002/qj.289CrossRefGoogle Scholar
  3. Bishop CH, Etherton J, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon Wea Rev 129:420–436. doi:10.1175/1520-0493(2001)129 < 420:ASWTET > 2.0.CO;2Google Scholar
  4. Bony S, Emanuel KA (2001) A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J Atmos Sci 58:3158–3183CrossRefGoogle Scholar
  5. Chikira M, Sugiyama M (2010) A cumulus parameterization with state-dependent entrainment rate. Part I: description and sensitivity to temperature and humidity profiles. J Atmos Sci 67:2171–2193. doi:10.1175/2010JAS3316.1Google Scholar
  6. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48, 2313–2329CrossRefGoogle Scholar
  7. Emanuel KA, Živković-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 56, 1766–1782CrossRefGoogle Scholar
  8. Emori S, Nozawa T, Numaguti A, Itsushi U (2001) Importance of cumulus parameterization of precipitation simulation over east Asia in June. J Meteor Soc Jpn 79:939–947. doi:10.2151/jmsj.79.939CrossRefGoogle Scholar
  9. Enomoto T, Kuwano-Yoshida A, Komori N, Ohfuchi W (2008) Description of AFES 2: improvements for high-resolution and coupled simulations. In: Hamilton K, Ohfuchi W (eds) High resolution numerical modelling of the atmosphere and ocean, Springer, New York, pp 77–97CrossRefGoogle Scholar
  10. Enomoto T, Hattori M, Miyoshi T, Yamane S (2010) Precursory signals in analysis ensemble spread. Geophys Res Lett 37. doi:10.1029/2010GL042723Google Scholar
  11. Hosoda S, Ohira T, Nakamura T (2008) A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. JAMSTEC Rep Res Dev 8:47–59CrossRefGoogle Scholar
  12. Hosoda S, Ohira T, Sato K, Suga T (2010) Improved description of global mixed-layer depth using Argo profiling floats. J Oceanogr 66:773–787. doi:10.1007/s10872-010-0063-3CrossRefGoogle Scholar
  13. Hunt B, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local transform Kalman filter. Physica D 230:112–126CrossRefGoogle Scholar
  14. Inoue J, Hori ME (2011) Arctic cyclogenesis at the marginal ice zone: a contributory mechanism for the temperature amplification? Geophys Res Lett 38. doi:10.1029/2011GL047696Google Scholar
  15. Inoue J, Enomoto T, Miyoshi T, Yamane S (2009) Impact of observations from Arctic drifting buoys on the reanalysis of surface fields. Geophys Res Lett 36. doi:10.1029/2009GL037380Google Scholar
  16. Katsumata K, Yoshinari H (2010) Uncertainties in global mapping of Argo drift data at the parking level. J Oceanogr 66:553–569. doi:10.1007/s10872-010-0046-4CrossRefGoogle Scholar
  17. Kida S, Shige S, Kubota T, Aonashi K, Okamoto K (2009) Improvement of rain/no-rain classification methods for microwave radiometer observations over ocean using the 37-GHz emission signature. J Meteor Soc Jpn 87A:165–181. doi:10.2151/jmsj.87A.165CrossRefGoogle Scholar
  18. Komori N, Kuwano-Yoshida A, Enomoto T, Sasaki H, Ohfuchi W (2008) High-resolution simulation of the global coupled atmosphere-ocean system: description and preliminary outcomes of CFES (CGCM for the earth simulator). In: Hamilton K, Ohfuchi W (eds) High resolution numerical modelling of the atmosphere and ocean, Springer, New York, pp 241–260CrossRefGoogle Scholar
  19. Komori N, Takahashi K, Komine K, Motoi T, Zhang X, Sagawa G (2005) Description of sea-ice component of Coupled Ocean–Sea-Ice Model for the Earth Simulator (OIFES). J Earth Sim 4:31–45Google Scholar
  20. Kuwano-Yoshida A, Enomoto T, Ohfuchi W (2010) An improved statistical cloud scheme for climate simulations. Q J R Meteor Soc 136. doi:10.1002/qj.660Google Scholar
  21. Masuda S, Awaji T, Sugiura N, Ishikawa Y, Baba K, Horiuchi K, Komori N (2003) Improved estimates of the dynamical state of the North Pacific Ocean from a 4 dimensional variational data assimilation. Geophys Res Lett 30. doi:10.1029/2003GL017604Google Scholar
  22. Masumoto Y, et al (2004) A fifty-year eddy-resolving simulation of the World Ocean—preliminary outcomes of OFES (OGCM for the earth simulator). J Earth Simul 1:35–56Google Scholar
  23. Miyoshi T, Yamane S (2007) Local ensemble transform Kalman filtering with an AGCM at a T159/L48. Mon Wea Rev 135:3841–3861CrossRefGoogle Scholar
  24. Miyoshi T, Yamane S, Enomoto T (2007a) The AFES-LETKF experimental ensemble reanalysis: ALERA. SOLA 3:45–48. doi:10.2151/sola.2007-012CrossRefGoogle Scholar
  25. Miyoshi T, Yamane S, Enomoto T (2007b) Localizing the error covariance by physical distances within a local ensemble transform Kalman filter (LETKF). SOLA 3:89–92. doi:10.2151/sola.2007-023CrossRefGoogle Scholar
  26. Moteki Q, et al (2007) The impact of the assimilation of dropsonde observations during PALAU2005 in ALERA. SOLA 3:97–100CrossRefGoogle Scholar
  27. Moteki Q, et al (2011) The influence of observations propagated by convectively coupled equatorial waves. Q J R Meteor Soc 137:641–655. doi:10.1002/qj.779CrossRefGoogle Scholar
  28. Numaguti A, Takahashi M, Nakajima T, Sumi A (1997) Description of CCSR/NIES atmospheric general circulation model. In: Study on the climate system and mass transport by a climate model, CGER’s supercomputer monograph report, vol. 3. National Institute for Environmental Sciences, Tsukuba, pp 1–48Google Scholar
  29. Ohfuchi W, et al (2004) 10-km Mesh Meso-scale resolving simulations of the global atmosphere on the earth simulator—preliminary outcomes of AFES (AGCM for the earth simulator). J Earth Simul 1:8–34Google Scholar
  30. Onogi K, et al (2007) The JRA-25 reanalysis. J Meteor Soc Jpn 85:369–432CrossRefGoogle Scholar
  31. Pacanowski RC, Griffies SM (2000) The MOM 3.0 manual. National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, 680ppGoogle Scholar
  32. Peng SM, Ridout JA, Hogan TF (2004) Recent modifications of the Emanuel convective scheme in the Navy operational global atmospheric prediction system. Mon Wea Rev 132:1254–1268CrossRefGoogle Scholar
  33. Reynolds RW, Chunying L, Smith TM, Chelton DB, Schlax MG, Casey KS (2007) Daily high-resolution-blended analyses for sea surface temperature. J Climate 20:5473–5496CrossRefGoogle Scholar
  34. Sekiguchi M, Nakajima T (2008) A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. J Quant Spectrosc Radiat Trans 109:2779–2793. doi:10.1016/j.jqsrt.2008.07.013CrossRefGoogle Scholar
  35. St-James JS, Laroch S (2005) Assimilation of wind profiler data in the Canadian meteorological centre’s analysis systems. J Atmos Oceanic Technol 22:1181–1194CrossRefGoogle Scholar
  36. Sugiura N, Awaji T, Masuda S, Toyoda T, Igarashi H, Ishii M, Kimoto M (2009) Potential for decadal predictability in North Pacific region. Geophys Res Lett 36. doi:10.1029/2009GL039787Google Scholar
  37. Takata K, Emori S, Watanabe T (2003) Development of the minimal advanced treatments of surface interaction and runoff. Glob Planet Change 38:209–222CrossRefGoogle Scholar
  38. Yoneyama K, Masumoto Y, Kuroda Y, Katsumata M, Mizuno K (2006) Mirai Indian Ocean cruise for the Study of the MJO-convection Onset. CLIVAR Exch 11:8–10Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Takeshi Enomoto
    • 1
    • 2
    Email author
  • Takemasa Miyoshi
    • 3
    • 4
  • Qoosaku Moteki
    • 5
  • Jun Inoue
    • 5
  • Miki Hattori
    • 5
  • Akira Kuwano-Yoshida
    • 2
  • Nobumasa Komori
    • 2
  • Shozo Yamane
    • 6
  1. 1.Disaster Prevention Research InstituteKyoto UniversityUjiJapan
  2. 2.Earth Simulator CenterJapan Agency for Marine-Earth Science and TechnologyKanazawa-ku, YokohamaJapan
  3. 3.RIKEN Advanced Institute for Computational ScienceKobeJapan
  4. 4.Department of Atmospheric and Oceanic ScienceUniversity of MarylandCollege ParkUSA
  5. 5.Research Institute for Global ChangeJapan Agency for Marine-Earth Science and TechnologyYokosukaJapan
  6. 6.Department of Environmental Systems ScienceDoshisha UniversityKyotanabeJapan

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